Tag: Machine Learning

In real-life data science and machine learning scenarios, we often deal with large-size datasets. Dealing with tremendously large datasets is challenging and at least significantly difficult to cause a bottleneck in modelling an algorithm.

When we go deeper, we find the number of features in a dataset makes data large in size. However, not always a large number of instances comes with a large number of features, but this is not the point of discussion here. It is also very often that in a high-dimensional dataset, we find many irrelevant or insignificant features because they contribute less or zero when applying data for predictive modelling. It has also been seen that they can impact modelling negatively. Here are some possible impacts these features have in efficient predictive modelling:

• Unnecessary memory and resource allocation are required for such features and make the process slow.
• Machine learning algorithm performs poorly because such features act as noise for them.
• Modelling data with high-dimensional features takes more time than data with low dimensions.

So, feature selection comes here as a saviour here, which is also an economical solution. In this article we are going to talk about the following topics:

Table of content

• What is Feature Selection?
• Feature Selection Methods
• Difference Between Filter, Wrapper and Embedded Methods for Feature Selection
• A Case Study in Python

What is Feature Selection?

Feature selection is the process of extracting or selecting a subset of features from a dataset having a large number of features. While extracting features from a dataset, we should consider their potential level before applying them for machine learning and statistical modelling.

The motive behind this procedure is to reduce the number of input features used for final modelling. At the same time selected feature should be the most important one to model. Talking about the impact, this procedure simplifies the machine learning model and improves accuracy and efficiency. Many times it also saves models from overfitting.

The point which is noticeable here is that feature selection is different from features engineering in some cases, because feature engineering refers to the process of creating new features or variables that are not explicitly present in the original dataset but may be useful in improving the performance of a model. On the other hand, feature selection is concerned with selecting the most relevant features from a given set of features.

However, there are different methods of feature selection, such as filter wrapper methods and embedded methods. Let’s take a look at the basic methods of feature selection.

Feature Selection Methods

In general feature selection method can be classified into three main methods:

Filter methods: these methods help us in selecting important features by evaluating the statistical properties of dependent and independent features, such as correlation, mutual information, or significance tests, independent of the learning algorithm. The below image explains further methods.

Some examples of this type of method are as follows

• Correlation-based Feature Selection (CFS): In this type of feature selection procedure, we consider the correlation evaluation between the dependent and independent features of data. Here we select the subsets of features based on the highest correlation with the target feature.
• Mutual Information: this method is similar to the CFS method, but it works based on the mutual information evaluation between the dependent and independent variables. Based on the mutual information evaluation, we eliminated features from data that have the lowest mutual information with the target variables.

Principal Component Analysis (PCA): Using this method, we reduce the dimension of the data and try to get a smaller set of principal components that explain most of the variance in the data.

Wrapper methods: In this method, we evaluate the performance of the model with different subsets of features. Here we use a specific algorithm to select the best subset of features. This type of method assesses the performance of a predictive model using a particular subset of features and iteratively searches for the best subset of features that results in the highest performance. The below picture gives us a glimpse of wrapper methods of feature selection:

Some examples of wrapper methods for feature selection are as follows:

• Forward Selection: in this method, any selected algorithm starts modelling data with an empty set of features and iteratively adds one feature at a time, evaluating the performance of the predictive model at each step. This process continues until the algorithm uses a desired number of features or until it not gains an optimal performance.
• Backward Elimination: We can think of this method as the opposite of the forward selection method, where it starts with a whole set of features and removes one feature in every iteration. This process continues until the algorithm uses a desired number of features or until it not gains an optimal performance.
• Recursive Feature Elimination (RFE): With this method, we recursively remove the features from the model based on their importance in the modelling procedure, and it ends where we get optimal results from the model or optimal subset of features.

Embedded Methods: As the name suggests, this type of feature selection method perform feature selection and model training simultaneously. In embedded methods, feature selection is performed during model training, with the aim of selecting the most relevant features for the specific model being used. There are a variety of algorithms such as decision trees, support vector machines, and linear regression, that can work with embedded feature selection methods.

Some examples of embedded methods for feature selection include LASSO (Least Absolute Shrinkage and Selection Operator) and Ridge Regression, where these methods perform regularisation by shrinking the coefficients of the less important features to zero and selecting the subset of features that have non-zero coefficients for linear regression, and Decision Trees with pruning for decision tree models.

Difference Between Filter, Wrapper and Embedded Methods for Feature Selection

In the above, we have seen the basic classification of different feature selection methods, and in difference, we can say that these methods belong to three broad categories. Some basic differences between these methods are as follows:

• Filter methods are independent of any specific machine learning model, whereas Wrapper methods are used to improve the performance of any specific machine learning model. Embedded methods select features during the model training process.
• Filter methods rank the features based on their ability to explain the target variable, Wrapper methods evaluate the relevance of features based on their ability to improve the performance of a specific ML model, whereas Embedded methods incorporate the feature selection process into the model training process itself with the aim of selecting the most relevant features for the specific model being used.
• Filter methods may not always identify the optimal subset of features when there is insufficient data to capture the statistical correlations between the features. In contrast, Wrapper and Embedded methods can provide the best subset of features as they evaluate the performance of a model with different subsets of features in iterations or during the time of training exhaustively.
• Wrapper methods are generally more computationally expensive and time taking than filter methods, while embedded methods can be more efficient than wrapper methods.
• Using features selected by wrapper methods in the final machine learning model may increase the risk of overfitting as the model has already been trained using those features in multiple iterations. When talking about embedded methods, the risk of overfitting with embedded feature selection methods depends on the complexity of the model being trained, the quality of the selected features, and the regularisation techniques used. In contrast, filter methods typically select a subset of features based on their relevance to the target variable without directly incorporating the model performance into the selection process.

Good enough!

Now take a look at the basic implementation of feature selection.

A Case Study in Python

Here, we are going to use Pima Indians Diabetes Dataset, whose objective is to diagnostically predict whether or not a patient has diabetes based on certain diagnostic measurements included in the dataset.

Let’s start by importing some basic libraries, modules and packages that we will need on the way to feature selection.

import pandas as pd

import numpy as np

from sklearn.feature_selection import SelectKBest, chi2, RFE

from sklearn.linear_model import LogisticRegression

Now, let’s import the dataset.

data = pd.read_csv(“/content/diabetes.csv”)

After successfully importing the data, let’s take a look at some of the rows.

data.head()

In the above, we can see that eight features in the dataset are told about the patient being diabetic in the form of 0 and 1. Talking about the missing values on the data, we can see the NAN values are replaced by 0. Anyone can deduce this by knowing the definition of the columns because it is impractical to have zero values in body mass and insulin columns.

Now we can convert these data into numpy array form to get faster computation.

array = data.values

#features

X = array[:,0:8]

#target

Y = array[:,8]

Filter Method

Here, we will perform a chi-squared statistical test for features with non-negative values and will select three features from the data. The chi-squared test belongs to the filter method of feature selection.

test = SelectKBest(score_func=chi2, k=4)

fit = test.fit(X, Y)

print(fit.scores_)

Output:

Here, we can see the Chi-square score of the features. Now we can transform important features. Let’s take a look.

features = fit.transform(X)

print(features[0:5,:])

Output:

Here are the four selected features of the dataset based on the chi-square test.

Wrapper Method

Next, we will take a look at the implementation of Recursive Feature Elimination, which belongs to the wrapper method of feature selection. In the above, we have explained how this method works.

We know that the wrapper methods are used to improve the performance of any specific machine learning model so here we will work with the logistic regression model.

model = LogisticRegression()

rfe = RFE(model, n_features_to_select=3, step=3)

fit = rfe.fit(X, Y)

Output:

Here, we have applied the RFE feature selection for the logistic regression model. Lets see the results now.

print(“Num Features: n”, fit.n_features_)

print(“Selected Features: n”, fit.support_)

print(“Feature Ranking: n”, fit.ranking_)

Output:

Here we can see the ranking of the features of the dataset, also in the second output we can see which features are supporting the most. Now let’s take a look at the embedded method.

Embedded Method

Here, we will use the lasso regression for feature selection. Basically, it is a regression technique which adds a penalty term to the cost function of regression that encourages sparsity in the coefficients.

In practice, Lasso can be used as a feature selection method by fitting a Lasso regression model on a dataset and examining the resulting coefficient vector to determine which features are important. Features with non-zero coefficients are considered important, while those with zero coefficients can be discarded.

Let’s make an object of lasso regression and fit the data on it.

# Fit Lasso model

lasso = Lasso(alpha=0.1)

lasso.fit(X, Y)

Let’s check the importance of all the features

# Extract coefficients and print feature importance

coef = np.abs(lasso.coef_)

print(“Feature importance:n”)

for i in range(len(data.columns:

print(f”{data.columns[i]}: {coef[i]}”)

Output:

Here we can see the ranking of important features when we use the lasso regression.

Final words

Till now, we have discussed feature selection, different methods of feature selection and a basic implementation of feature selection using the Python programming language. Because of this article, we get to know that the subject feature selection is itself a big course, so in future articles, we will take a look at more details of this topic where, one by one, we will explain all the variants of the feature selection method.

About DSW

DSW, specializing in Artificial Intelligence and Data Science, provides platforms and solutions for leveraging data through AI and advanced analytics. With offices located in Mumbai, India, and Dublin, Ireland, the company serves a broad range of customers across the globe.

Our mission is to democratize AI and Data Science, empowering customers with informed decision-making. Through fostering the AI ecosystem with data-driven, open-source technology solutions, we aim to benefit businesses, customers, and stakeholders and make AI available for everyone.

Our flagship platform ‘UnifyAI’ aims to streamline the data engineering process, provide a unified pipeline, and integrate AI capabilities to support businesses in transitioning from experimentation to full-scale production, ultimately enhancing operational efficiency and driving growth.

In the field of data science, the deployment and operation of AI/ML models can be a challenging task due to various reasons, like increasing the amount of data. To overcome these challenges, the concept of ModelOps was introduced in the early 2020s. ModelOps encompasses a set of practices and processes that not only aid in the creation of models but also in the deployment of them in a scalable and flexible manner. This focus on ModelOps has become increasingly important as organizations strive to effectively utilize machine learning models in their operations. ModelOps has become a rapidly growing field as a result. So let’s take an introductory dive into the subject and understand what ModelOps is and how it is becoming the point of attraction for AI and ML developers.

What is ModelOps?

ModelOps can be referred to as the management and operationalisation of ML models within the ML processing of any organisation. As many big organisations are using a load of Ai use cases, it becomes a compulsion to develop these use cases so that they can have higher speed and scalability and improved quality and accuracy. Like DevOps, MLOps and DataOps, ModelOps is also a set of practices that involves the enhancement of a wide range of activities, such as machine learning model development, testing, deployment, monitoring, and maintenance.

According to Gartner,” ModelOps (or AI model operationalisation) is focused primarily on the governance and life cycle management of a wide range of operationalised artificial intelligence (AI) and decision models, including machine learning, knowledge graphs, rules, optimisation, linguistic and agent-based models. Core capabilities include continuous integration/continuous delivery (CI/CD) integration, model development environments, champion-challenger testing, model versioning, model store and rollback.”

The aim behind applying ModelOps in the development process is to ensure that ML models are better developed and used efficiently in real-world applications. This helps organisations to enhance their capability to generate business value from their ML models and investments. ModelOps is a relatively newer term in the technology space, but it is becoming way more important because organisations are relying more on machine learning models for critical business processes.

Talking about the challenge that ModelOps helps organisations overcome, mainly in the deployment of machine learning models, such as difficulties in the reproduction of final results, less transparency, slow iteration times etc., the ultimate goal of applying ModelOps is to maximise the value of organisations investment on machine learning.

Component of ModelOps

The components of ModelOps can be segregated into the following groups:

Model development: this section may include the tools for extracting prepared and feature-engineered data from data sources and for model selection and training.

Model deployment: this section contains tools for model packaging, model and architecture provisioning, and model deployment to the production environment.

Model Monitoring: this section includes tools for monitoring and capturing model performance and data and model drift while interfacing for model retraining.

Model maintenance: this section includes tools for managing models, storing model versions, model documentation and requirement and model retraining place.

Model governance: this section includes a set of rules to control access, implement policies and track activity for models and their results.

Collaboration and communication: these are a set of rules and management skills to make better collaboration between data scientists and DevOps teams, communication between stakeholders, and integration with other tools and systems.

Benefits of ModelOps

However, the term ModelOps is inspired by the concept of DevOps and MLOps, but its adoption ensures:

• Developers are provided with CI/CD(continuous integration / continuous delivery)
• Improved Development Environment
• Better Testing
• Controlled model versioning

One can think of ModelOps as a part of MLops, which mainly focuses on keeping deployed models ready to involve in continuous model developing or retraining and synchronized deployment. There are several benefits of ModelOps, such as:

• Improved model quality: ModelOps helps ensure that machine learning models are reliable, accurate, and consistent by introducing processes for testing, validation, and monitoring the performance of the models.
• Faster model deployment: ModelOps automates the deployment process, reducing the time it takes to get models into production and increasing the speed at which new models can be deployed.
• Better model governance: ModelOps provides a framework for managing the lifecycle of machine learning models, including versioning, auditing, and regulatory compliance.
• Increased agility: ModelOps enables organizations to respond quickly to changes in business requirements or market conditions by allowing teams to update or replace models in a timely manner.
• Improved operational efficiency: ModelOps streamlines the operations of machine learning models, reducing manual effort and increasing the scalability and reliability of the models.

Difference between ModelOps and MLOps

Often organisation uses the terms ModelOps and MlOps interchangeably, but when going into the deeper section, we find subtle differences between the two.

In one of our articles, we explored the MLOps profoundly and learned that it is a broader term that helps in processing the entire lifecycle of AI/ML models, from processing data to deployment and management of the models. This term consists of an entire set of practices for data processing, data management building, testing, deploying and monitoring AI/ML models.

On the other hand, ModelOps only focuses on the operational aspects of AI/ML models. ModelOps includes practices for processes such as model management, deployment, monitoring, and maintenance. Involving these practices in development ensures that the models are deployed, monitored, and maintained efficiently in production.

In conclusion, we can say that ModelOps practices are more focused on operational aspects of AI/ML models, while MLOps is a broader term that includes ModelOps as a subset that majorly focuses on the deployment, maintaining and monitoring the AI/ML models.

Is MLOps a combination of DataOps and ModelOps?

In the above section, we learned that ModelOps is a subset of MLOps that focuses only on operational aspects of AI/ML models, but due to this article, we have learned that DataOps is a set of practices and technologies that are designed to address the challenges of managing and processing large volumes of data in a fast-paced and constantly changing environment.

So we can say that the terms MLOps, ModelOps and DataOps are related but distinct concepts that are all part of the broader term of data science and machine learning.

The focus point of DataOps is to ensure that data is high-quality, accessible, and secure and that the process of moving data through the pipeline is efficient and reliable.

On the other hand, ModelOps is a set of practices, tools and technologies focusing on streamlining the model development and deployment process, making it more efficient and reliable while also ensuring that models are continuously monitored and updated to reflect changing data and business needs.

Here we can say that MLOps is a combination of DataOps and ModelOps that is focused specifically on the machine learning workflow, from data collection to model deployment. While DataOps and ModelOps are both important components of MlOps, they are not the same. Rather MLOps is an umbrella term that encompasses both DataOps and ModelOps, as well as other practices and technologies that are specific to the machine learning workflow.

Implementing a comprehensive data-driven strategy that involves the use of DataOps, ModelOps, and MLOps can be a complex and challenging task for organizations. This is because it often requires the collaboration of different teams with different areas of expertise and can involve the integration of a variety of technologies and processes.

UnifyAI, DSW’s platform, is an ideal solution to overcome the challenges and intricacies that organizations face when implementing DataOps, ModelOps, and MLOps simultaneously. Collaboration and communication among different teams are necessary to address the complexities. UnifyAI integrates MLOps, DataOps, and ModelOps to create a culture of collaboration and knowledge-sharing among teams. This allows organizations to achieve a comprehensive data-driven strategy and reduces the dependence on a large number of teams and members by offering all necessary technologies and tools aligned with best practices for implementing smooth ML workflows.

About DSW

DSW, specializing in Artificial Intelligence and Data Science, provides platforms and solutions for leveraging data through AI and advanced analytics. With offices located in Mumbai, India, and Dublin, Ireland, the company serves a broad range of customers across the globe.

Our mission is to democratize AI and Data Science, empowering customers with informed decision-making. Through fostering the AI ecosystem with data-driven, open-source technology solutions, we aim to benefit businesses, customers, and stakeholders and make AI available for everyone.

Our flagship platform ‘UnifyAI’ aims to streamline the data engineering process, provide a unified pipeline, and integrate AI capabilities to support businesses in transitioning from experimentation to full-scale production, ultimately enhancing operational efficiency and driving growth.

In one of our articles, we have seen how MLOps is a set of practices to bridge data science, machine learning, data analytics, engineering, and development. This bridging feature of MLOps made it a highly emerging option to adopt between many organisations. Nowadays, we can see this as a helper to organisations, professionals, and advanced systems to continuously and consistently deploy data models. This technology combines some operating technology components, people and a set of practices. In simpler terms, MlOps leverages data, technology and people systems to empower production-level machine learning.

While working on catering MLOps to many clients, we have observed that growing companies are using MLOPs to meet the evolving challenges in scaling machine learning. In this blog, we will look at some top MLOps trends and our predictions for 2023 in the field that is certain to become a leading aspect in the field.

DataOps with MLOps

This has been seen from the start that machine learning models are more reliable when the data we use to train them is appropriate. The term DataOps has been in use since around 2013 and has gained widespread recognition and adoption in recent years as organisations have become more data-driven and have faced increasing challenges in managing large amounts of data. This set of practices helps streamline the machine learning pipelines by fixing the quality of data coming to train and run machine learning models. Its impact has also been seen in advancing the growth of machine learning deployment.

DSW’s solution platform UnifyAI is designed in such a way that it can give you growth in AI and ML development, including both MLOps and DataOps practices. To learn more about DataOps, you can refer to this article.

Value Enhancement of AI/ML Solutions

The main reason behind the adoption of AI and ML solutions is that they can increasingly make an impact on global industries, and those who are applying them in their functions can quantify and track the value. The greater the visibility of the impact, the higher the enhancement of the organisation’s health, security, and reputation. To improve the visibility of the impact of AI and ML, we can track the quality of the model and data and the reusability of the data. After that, quantification of the impact becomes easier.

Here UnifyAI comes with its unique feature(observability), using which anyone can track the health of the applied model and data. Its advanced features increase observability and provide better monitoring. Enabled feature store gives you high data reusability using which you can go for CE/CD(continuous experiment and continuous deployment).

Technical Component Management in MLOps Deployment

MLOps is one of the trending topics in the field of data science and artificial intelligence that expands beyond the process of transforming AI and ML development. Participation of technology components to successfully implement MLOps is required, but managing those technologies better is more important. Only A good architecture can give the development of AI speed, accuracy, and scalability. It is crucial as well as tough to form a group of technology components from various sources and integrate together to follow best practices.

UnifyAI encompasses all the critical technology components, and its development follows a cutting-edge architecture, allowing organizations to concentrate solely on AI and ML development without worrying about architectural design.

MLOps will Enhance the Reliability of AI and ML

There is no doubt in saying MLOps and machine learning are advancing the industry, and it has been mentioned in many reports that many sectors of industries will continue to adopt AI. As MLOps is not only changing the way of AI development but also ensuring that models that are working in production are more accurate and robust. This way, organisations are more focused on adopting the SOTA way of implementing MLOps. We can say that companies adopting MLOps will trigger increasing investment in machine learning.

UnifyAI is developed to leverage MLOps and bring AI applications from the experimental phase to large-scale production with increased efficiency, making organizations more competitive in the industry.

Integration of MLOps will Remain Challenging.

Building AI and ML models are challenging, but streamlining and taking them into production is more challenging. Onboarding these models requires orchestrating workloads of technology components, balancing servers and giving them scalability in production. When we say making an organisation AI-enabled, is not just mean applying one or two AI applications in their processes, but it takes a load of AI models where some of the AI models are required to be trained and stored in repositories for further usage either in case of failure or in case of more accurate results and some of them to make it to the production with required scalability and robustness.

UnifyAI facilitates smooth experimentation and deployment of AI and ML models for organizations. It features accessible interfaces for the rapid creation and storage of new models, as well as effective management of stored or running models. An orchestrator, acting as the central component of AI systems, provides a seamless experience for distributing models, data, and workloads throughout the processes.

More Libraries and Packages for MLOps Tasks

Since MLOps enhances the capability and adaptability of machine learning models regardless of cloud providers or technical stacks, getting a one-stop solution will remain challenging. The reason being the number of libraries and packages is increasing rapidly and making it difficult to choose and become dependent on one. Being adaptable all time is a difficult process and causes a decrease in the speed of development.

In the development of UnifyAI, we have ensured that it can be easily integrated with new technologies, enabling users to adapt to changes and facilitating the acceleration of advancements.

The usage of Feature Stores will Increase

However, the technology is newer than the others, but it has become a mainstream component of MLOps. As it increases the reusability of the data features, enhances data collaboration between various teams, and allows faster experiments, it makes the MLOps more efficient.

UnifyAI incorporates a feature store to offer the advantages of using a feature store in MLOps. The orchestrator within UnifyAI obtains data from the feature store and passes it to the models in production, streamlining the deployment process and reducing the likelihood of errors.

Final words

In this blog post, we’ve discussed our predictions for MLOps in 2023 and its growing importance across various industries. We have found through working with organisations in different sectors that the proper approach to AI development is crucial for delivering more significant impact and value. Without scalability, robustness, adaptability, and sustainability in AI development, organisations fail to bring AI into production. Our aim through these predictions is to make AI accessible to all and guide them in the right direction using UnifyAI.

About DSW

DSW, specializing in Artificial Intelligence and Data Science, provides platforms and solutions for leveraging data through AI and advanced analytics. With offices located in Mumbai, India, and Dublin, Ireland, the company serves a broad range of customers across the globe.

Our mission is to democratize AI and Data Science, empowering customers with informed decision-making. Through fostering the AI ecosystem with data-driven, open-source technology solutions, we aim to benefit businesses, customers, and stakeholders and make AI available for everyone.

Our flagship platform ‘UnifyAI’ aims to streamline the data engineering process, provide a unified pipeline, and integrate AI capabilities to support businesses in transitioning from experimentation to full-scale production, ultimately enhancing operational efficiency and driving growth.

 

 

In the current data analytics and data science scenarios, we can see the emergence of one new member named as DataOps. When we work around MLOps(machine learning operations), we definitely use practices defined inside DataOps, which is also a set of rules, practices and processes but aims to improve data communication, integration and automation. As data is the new fuel, any organization processing based on data needs a higher quality data processing to run it appropriately. Practices of DataOps can establish better data collaboration and improve the data flow speed and data quality across any organization. So Let’s take an introductory dive into the subject and understand it.

What is DataOps?

In simple words, we can define DataOps as a set of practices, processes, and technologies to efficiently design, implement and maintain data distribution architecture in any data workflow so that higher business values can be obtained from big data. Generally, the implementation of these practices includes a wide range of open-source tools to make the data accurately flow in the direction of production.

We can also think of DataOps as DevOps of the data pipeline, where these practices strive to increase the speed of applications that work around big data frameworks. The main objective of opting DataOps should be leveraging data silos efficiently with the data management, IT operation and software teams. This ensures that data is being used in the most flexible, quickest and most effective manner to gain higher business values from the data.

When going deeper into this topic, we find that this newer technology includes so many technology components in the data lifecycle to bridge many technology disciplines like data development, data quality management, data extraction and transformation, data governance and centre capacity control, and data access.

One big difference between DevOps and DataOps is that almost zero software or tool helps in DataOps. So to build a complete DataOps process, the collaboration of tools like ETL tools, cataloguing tools, system monitors and data curation tools are required. With the help of the well-structured architecture of these tools, we can increase the agility of the system.

Practices Behind DataOps

Here are a few best practices associated behind the DataOps implementation strategy:

1. Establishment of performance measures and benchmarks at every stage of the data lifecycle.
2. Predefine the rules for data and metadata before applying them to any process.
3. Use monitoring and feedback loops to maintain the quality of data.
4. Use tools and technology to automate the process as much as possible.
5. Usage of optimization processes for better dealing with bottlenecks such as data silos and constraint data warehouses.
6. Ensure the scalability, growth and adaptability of the program before implementing it.
7. Treat the process as lean manufacturing that focuses on constant improvements to efficiency.

Benefits of DataOps

As we discussed, DataOps is a set of practices aiming at the betterment of collaboration, improving the speed and quality of data flowing and processing across any organization. This set of practices includes the following seven main benefits for organizations:

1. Collaboration: DataOps helps in improving the collaboration between the different teams like data engineers, data scientists, business stakeholders, and operations teams, which also helps to speed up the data flow with sustainable quality.
2. Automation: data automation is one of the key benefits of DataOps as it helps avoid manual and repetitive data processes like data ingestion, cleaning, processing, and deployment.
3. Continuous Integration and Continuous Deployment (CI/CD): It leverages a better CI/CD environment around data products, including data pipelines, machine learning models and many more, and enables rapid iteration and deployment.
4. Monitoring and Feedback: this set of practices encourages the importance of monitoring and Feedback. It loops them to detect and resolve issues in real time, which leads to continuous improvement of data products.
5. Data Quality: the main focus of DataOps is to improve the quality by using the practices such as data validation, profiling, and governance
6. Data Security: DataOps helps easily take control over data encryption, data access control, and data masking so that data security can be ensured.
7. Data Governance: DataOps includes practices that ensure data is managed nicely and used ethically. This part of the benefits can be achieved using processes like data stewardship, metadata management, and data lineage tracking.

How DataOps Works

As discussed above, DataOps is a set of practices that aim to streamline and strengthen the collaboration and communication of data processes and data flows. As a set of practices, it takes a team involving members of different roles and responsibilities, such as data engineers, data scientists, and business analysts in team works and follows this set of practices to work more efficiently and effectively. Making data pipeline integration and delivering continuity, making data validation easy, and real-time data monitoring are the primary motives of DataOps, while also increasing the quality of data and reducing errors.

We can also think of Dataops similarly to lean manufacturing, focusing on minimizing the waste within manufacturing processes. In many places, we see the use of SPC(statistical process control) to constantly monitor and validate the data analytics consistency. The SPC is used to ensure that statistics remain within optimized ranges, higher data processing efficiency and improved data quality.

Evolution from ETL to DataOps

We all know that it all started by ingesting data on-premises into databases where self-built data connectors helped in ingestion, and because of being slow, ETL tools came into the picture.

Being hardware, issues such as less scalability, transformation and continued deficiencies came with the data connectors.

In the second phase, cloud data warehouses got introduced in the field and eliminated the classic hardware with scalability issues. Here ETL platforms started reducing the number of data connectors in the pipeline. With these developments, data ingestion becomes easy, but data transformation remains similar.

Soon cloud data warehouses started providing facilities for data transformation inside them, and this is how the concert of data lakes came into the picture and offered unlimited insights from endlessly queryable data.

Nowadays, we can see that companies are not facing the issue of data generation but delivering it appropriately is a constant one. Also, various traditional ETL platforms are still working, leading to data silos and distribution between authorized users.

About DSW

DSW, specializing in Artificial Intelligence and Data Science, provides platforms and solutions for leveraging data through AI and advanced analytics. With offices located in Mumbai, India, and Dublin, Ireland, the company serves a broad range of customers across the globe.

Our mission is to democratize AI and Data Science, empowering customers with informed decision-making. Through fostering the AI ecosystem with data-driven, open-source technology solutions, we aim to benefit businesses, customers, and stakeholders and make AI available for everyone.

Our flagship platform ‘UnifyAI’ aims to streamline the data engineering process, provide a unified pipeline, and integrate AI capabilities to support businesses in transitioning from experimentation to full-scale production, ultimately enhancing operational efficiency and driving growth.

The concept of a feature store in data science and artificial intelligence is relatively new but has its root in the field. In the early stages, features for AI models were typically hand engineered by data annotators and stored in various formats such as CSV, spreadsheet or databases. However, this way of storing data features found difficulties in data management, sharing and reusability.

In the early 2010s, the field witnessed the rise of the big data concept and the increasing popularity of MLOps, which led to a need for specialised data storage systems for data features, and this is how feature stores found their way to fit in between the data and the ML models.

What is a feature store?

In one of our articles, we explored this topic. Still, in simple terms, a feature store is a technology we use to manage data more efficiently, particularly for machine learning models or machine learning operations.

As we all know, MLOps is a way to compile or structure all technology pieces together in order to deploy, run, train and manage AI models. An MLOps system majorly consists of components of

• Machine learning
• DevOps (development operations)
• Data Engineering

Empowering a feature store in the MLOps can increase the craftsmanship of ML teams because the data flow can be more easily managed.

How do feature stores work?

Typically, data used to be stored somewhere on servers, and data scientists could access the data when going for data analysis, or server users could access it to display the data. But as big data came into the picture, such storage facilities and recalling became less feasible.

In the real world, we can see that AI and ML fulfil different kinds of use cases which leads to the requirement of different datasets in different formats. Fulfilment of this need requires a lot of data transformation and a combination of different tables to generate a standard feature set to serve a model.

These common data-related challenges that an AI project faces when putting ML models into production:

• Access to the right raw data
• Getting correct features from raw data.
• Compiling feature into training data.
• Managing features in the production

Feature store helps in solving these problems and fulfilling the data demands made by the models and ML workflow. Using the feature store, data scientists become capable of:

• Verify the validity of the data.
• Check the quality.
• Re-use the data.
• Versioning and control of data.

Nowadays, in MLOps, feature store has become a compulsory technology to fit between the data source and machine learning model because it accommodates the accessibility that a machine learning team seeks.

Where data is the lifeblood of any ML or AI model, a feature store helps ML and AI teams to:

• Collaborate efficiently
• Reduce data duplication
• Develop faster
• Compliance with better regulations

Features of a Feature Store

Till now, we have majorly discussed the need for a feature store in MLOps, and when we talk about the features of any feature store then, these are the following feature a feature store should consist of.

Capable of data consumption from multiple sources

In real life, it has been observed that there are multiple data sources of companies, and from those sources, only a few data are usable for AI and ML models. In that case, a feature store should be capable of extracting and combining important data from multiple sources; this means the feature store should be able to be attached by many sources. A feature store can consume data from

• Various streams
• Data warehouses
• Data files

Data transformation

One of the key benefits of applying a feature store in MLOps is that it helps data scientists easily get different types of features together to train and manage their ML models.

As we know, data is being gathered from different sources in different formats, while s feature store fit in between models and data sources transforms data and joins the models in consistent ways and also enables data monitoring. For example

Gathering data features from different sources is not the only task a feature store performs but also enables data teams to transform the data in the required form. One-hot encoding, data labelling and test data encoding are a few data transformation techniques which value only an ML engineer can understand.

Here feature store verifies the consistency of data transformations while data analytics and monitoring ensure data is applicable for modelling.

Search & discovery

Feature store is one of the ways to encourage collaboration among DSA and ML teams. It simply enhances the reusability of data features because once a set of features is verified and works well with a model, the feature set becomes eligible to be shared and consumed for other modelling procedures that can be built for completing different purposes.

A good feature store is always provided with a smart sharing setting that ensures the important features are standardised, and reusability is increased.

Feature Serving

Features stores should not only be capable of extracting and transforming data from multiple sources, but also they should also be able to pass data to multiple models. Generally, different APIs are used to serve features to the models.

Since models need consistency in features served to them, so in serving a check is important to verify the data fits the requirements of the models.

Monitoring

Finally, one of the most important features that should be applied to any block of code is accessibility to monitoring. A feature store should be provided with appropriate metrics on the data, which can discover the correctness, completeness and quality of the data that is passing through the feature store.

The aim of monitoring is to become updated, debugged and advanced about the system.

Conclusion

If you go through this article, then you will get to know that The MLOps is a set of many blocks and steps that need to work Parelally when a machine learning or AI model is going to be deployed into production. Serving data in these steps or blocks in one of the first steps of the whole procedure can define the reliability and accuracy of the whole procedure. So feature store becomes a requirement when you follow the practises defined under MLOps and require efficient results from it.

To give you a valuable experience of MLOps for real-life AI use cases, DSW | Data Science Wizards has come up with a state-of-the-art platform UnifyAI. This platform not only allows you to deploy AI and ML models into production seamlessly but also engages fewer technology components in your MLOps journey to avoid the complex engineering and get more focused on experimenting across all the models to get value-making data and AI-driven decisions.

The provided feature store with UnifyAI has all the key features that one optimised feature store should have and using this feature store, you can enhance the consistency, quality, serviceability and reusability of important features and can get high-quality results from your AI use cases.

About DSW

Data Science Wizards (DSW) aim to democratise the power of AI and Data Science to empower customers with insight discovery and informed decision-making.

We work towards nurturing the AI ecosystem with data-driven, open-source technologies and AI solutions and platforms to benefit businesses, customers, communities, and stakeholders.

Through our industry-agnostic flagship platform, UnifyAI, we are working towards creating a holistic approach to data engineering and AI to enable companies to accelerate growth, enhance operational efficiency and help their business leverage AI capabilities end-to-end.

Our niche expertise and positioning at the confluence of AI, data science, and open-source technologies help us to empower customers with seamless and informed decision-making capabilities.

DSW’s key purpose is to empower enterprises and communities with ease of using AI, making AI accessible for everyone to solve problems through data insights.

Connect us at contact@datasciencewizards.ai and visit us at darkseagreen-chicken-141904.hostingersite.com/

Nowadays, it is not surprising to see companies using AI to get huge benefits. Even a 2022 report from Mckinsey states that AI adoption globally is 2.5x higher than in 2017.

This data represents how the future of businesses is going to change due to AI adoption. Similarly, a Mckinsey 2020’s report signalling that revenue production by AI adoption will be doubled between 2020 and 2024.

While looking at the competition behind AI adoption, a well architect AI implementation can be a game-changing event for any organisation and make them stand out from the competitors. However, a well architect AI implementation is challenging to get, and we at DSW | Data Science Wizards focused on creating AI-enabled ecosystems in different organisations through our tech-agnostic platform UnifyAI, so in this article, we are going to look at the challenges that any organisation can face during the journey of AI adoption and how UnifyAI can help organisations to help simply face and overcome those challenges.

Let’s dive in.

Challenges in AI Adoption

Suppose you are considering using AI to complete any of your operations or looking for a scalable way to implement AI in your organisation. In that case, it is important to become aware of the challenges you might need to cope with and how UnifyAI, as a holistic approach, can resolve them. That way, you can successfully get a seamless path to AI adoption.

Here are the most common challenges you will meet in making AI work for you.

Organisations don’t understand the need for AI projects

If an organisation is doing good business, then its team often becomes reluctant to adapt to noticeable changes and the adoption of technologies like artificial intelligence can be a challenging shift or task to perform.

With this challenge convincing investors to invest in AI is also a challenge if the returns are unclear because where AI adoption is concerned, you will only sometimes be clear about what you are trying to build. Such uncertainty always becomes so much tricky to manage.

In that case, you need not worry about the uncertainties involved in AI adoption because, as an AI consultant DSW has phenomenal expertise in helping organisations become AI-enabled. So we understand the potential benefits of implementing AI ecosystems, projects and use cases. Using our this expertise, organisations can understand the value of their data and the involvement of AI with it.

Company’s data is not appropriate

To get an effective AI agent, it is suggested to model it using a sufficient amount of well-optimized data. Simply, high-quality data can give a high-quality AI model. Unfortunately, sometimes older or inadequate data management systems cause difficulties in AI adoption.

For example, suppose in any organisation, any CRM tool is being utilised for collecting demographic data, purchase behaviour and interaction data of customers. In that case, the organisation may have data that can be used for intelligent modelling. But if the data management is not optimised, that can mean the organisation is not interested in AI adoption. With the wrong data management system, making a structured way becomes difficult.

These insufficient data management systems lead to confusing data lakes and silos, and considering this fact, we have designed our platform in such a way that it can easily gather and structure only important data from complex data systems and involve that data in the process of making data as a value for you.

Organisations lack the skill-set

Being equipped with high-quality data is not only a requirement to become AI-enabled but also needs the right skill sets, directions and components to make AI use cases work.

In the competition of AI adoption, often organisation struggle to get the right data and AI skill set that leaves them unable to become AI-enabled. Even where companies have a high degree of in-house expertise, a lack of structuring AI components can become a significant obstacle in AI adoption.

Using UnifyAI, you can eliminate many common problems like Insufficient processing, multiple data and model pipeline, and inefficient orchestration. It works as a solution for organisations that needs more major skill sets to complete MLOps.

Organisations struggle to find good vendors to work with

Somehow organisations understand that AI adoption is a way to perform better than before, and they believe that they don’t understand how to use their data and technology together to deliver higher business value.

As a result, companies try to get AI adoption done with vendors, and negative experience with vendors makes companies reluctant to dive into AI adoption. However, with experienced AI vendors results of the work can speak for themselves.

DSW has developed itself as a user of the finest state-of-the-art technologies to fulfil customers’ demand for higher business values using their data. We have constantly been developing cutting-edge solutions that can provide a seamless experience of creating AI environments throughout organisations. As a result, UnifyAI has come in front of us, which not only makes AI adoption easy but also allows us to scale newer or older AI use cases and projects.

Organisations are not able to find a good use case

Implementing AI just for the sake of it often doesn’t encourage company-wide adoption. When any organisation doesn’t have a strong AI use case, it will always be challenging to deliver high business value. Without a strong reason behind AI adoption, it only makes a difference towards a technological perspective. So it is a best practice to apply AI only when you know how it will be a serious advancement.

However, there are more often chances that a company’s data has the potential to grab high business values, but somehow they need help understanding it. Our expertise in the field comes as a solution for such an organisation that helps organisations understand the value of their data and gain benefits using AI.

Low explainability of AI Team

Often, it has been observed that the data and AI teams end up working with data silos, meaning that most AI projects are stuck and die in dealing with vast amounts of data. Even after experimentation, they face considerable problems in production.

As a result, they plan architectures of AI workflow, which only increases the complexity of making scalable AI projects, and the benefits from such projects need to be explained better to get the workforce for implementation.

An AI team can avoid this complexity by using platforms such as UnifyAI that give them a seamless experience of taking AI use cases into production with high efficiency and explainability.

Fear of overhaul legacy systems

Nowadays, it is astonishing to see any organisation still rely on its old infrastructure to make its IT operation work. In such an organisation, management chooses not to adopt technologies like AI because of fear of the costs behind adoption.

If the cost is a concern, then thinking of AI adoption as a costly program is a myth because there are open-source technologies that make AI adoption simple and cheap. However, doing so might need an operational framework on the premises.

UnifyAI empowers organisations with an efficient operational framework in which all cutting-edge technologies are optimised and structured to give an easy and throughout experience from experiment to production to any organisation.

One sure thing is that there are huge benefits from AI adoption.

The complexity of AI Program Integration

In most cases, it has been seen that an AI team has made an optimised program that can potentially give huge benefits. Still, the integration of these programs needs a huge amount of engineering, and this engineering becomes an obstacle for companies.

More engineering effort for integration meant the solution never saw the light of day. This all happens because lack of skill sets for taking AI use cases from experimentation to production.

One of the most critical features of UnifyAI is that it is engineered to deliver all simple or complex AI projects into production without requiring a high level of model engineering. This feature not only avoids the significant complexities in taking AI into production but also gives an environment using which one can scale AI.

AI Governance

In one of our articles, we learned about AI/ML model governance, and many AI projects face problems of implementation in real life. For example, to operationalise a cloud-based banking platform in Poland, the organisation needs to build data centres only in Poland.

Often to build an AI use case, organisations need a massive amount of data, whether the data is sensitive or not, but it needs to keep in an adequately secure environment. In the failure of that organisation could face a considerable fine.

Such rules and regulations become obstacles to AI adoption because governing bodies often halt solutions in their track. Therefore, as we keep track of such an important step behind A implementation, we also help organisations to understand and get work done while following every piece of information, rules and regulations.

No challenges are greater than the results

Although there are many challenges in AI adoption, organisations should be confident in the way of AI adoption. It has always been said that becoming aware of the pitfalls is an essential first step.

After knowing all the obstacles an organisation might face, it can become more focused on finding strategic designs that can increase its chances of success. Looking at the potential benefits of AI adoption, there is no challenge that is too great to overcome.

About DSW

Data Science Wizards (DSW) aim to democratise the power of AI and Data Science to empower customers with insight discovery and informed decision-making.

We works towards nurturing the AI ecosystem with data-driven, open-source technologies and AI solutions and platforms to benefit businesses, customers, communities, and stakeholders.

Through our industry-agnostic flagship platform, UnifyAI, we are working towards creating a holistic approach to data engineering and AI to enable companies to accelerate growth, enhance operational efficiency and help their business leverage AI capabilities end-to-end.

Our niche expertise and positioning at the confluence of AI, data science, and open-source technologies help us to empower customers with seamless and informed decision-making capabilities.

DSW’s key purpose is to empower enterprises and communities with ease of using AI, making AI accessible for everyone to solve problems through data insights.

Connect us at contact@datasciencewizards.ai and visit us at darkseagreen-chicken-141904.hostingersite.com/

In one of our articles, we discussed the basics of random forests, where we have seen how they work by ensembling various trees, what are its important features, hyperparameters, and their pros and cons. This article will show how a random forest algorithm will work with a real-life dataset. With the completion of this article, we will be discussing the following subtopics:

Table of Contents

• The Dataset
• Exploratory Data Analysis
• Data processing
• Data Modelling
• Model Evaluation
• To Do List

Let’s start with understanding the data.

The Dataset

To look deep into the subject, we choose to work with the health insurance cross-cell prediction data, which we can find here. Under the data, we get major information about the vehicle insurance acceptance record of more than 3.5 lakh customers. In against this acceptance, we get the demographic(gender, age, region, vehicle age, annual premium, etc.) information of the customers.

Using this dataset, our task is to make a model that can tell us which customers will be interested in buying vehicle insurance based on similar demographic information. We have information about demographics (gender, age, region code type), Vehicles (Vehicle Age, Damage), and Policy (Premium, sourcing channel). Let’s check the data head.

import pandas as pd

import numpy as np

train_data = pd.read_csv(‘/content/drive/MyDrive/articles/12–2022/17–12–2022 to 24–12–2022/train.csv’)

train_data.head()

Output:

Here we can see the values we have provided to train a random forest model. In the above Response variable is our target variable where 1 : Customer is interested, 0 : Customer is not interested. Now, let’s move towards the first step of this modelling procedure, which is exploratory data analysis.

Exploratory Data Analysis

This step will let us know about the insights of vehicle insurance data so lets start with knowing the infomarmation which this data consists.

train_data.info()

Output:

By looking at the above output, we can say that this data has 9 categorical values, and with this, we can start plotting these values into graphs so that we can get more information out of this data.

Let’s begin with our target variable.

By looking at the above output, we can say that this data has 9 categorical values, and with this, we can start plotting these values into graphs so that we can get more information out of this data.

Let’s begin with our target variable.

Response valuable

train_data[‘Response’].value_counts().plot(kind = ‘bar’)

Output:

Here we can see that we had a huge customer response of 0, which means the majority of customers are not interested in buying vehicle insurance. Now it’s our target to understand how this response variable is dependent on other information given in the data.

Response with Gender

train_data[[‘Gender’, ‘Response’]].value_counts().plot(kind = ‘bar’, stacked = True, )

Looking at this chart, the responses from men are more than women, which is also similar to the positive response.

Response with Age

train_data[‘Age’].describe()

Here we can see that in the Age column, we have the minimum age of any customer is 20, and the highest age is 85 years. So better visualisation In this section, we are going to separate the age values by the range of 10, and we will be able to see how different age ranges of customers are responding.

bins = np.arange(1, 10) * 10

train_data[‘category’] = np.digitize(train_data.Age, bins, right=True)

counts = train_data.groupby([‘category’,’Response’]).Age.count().unstack()

print(counts)

counts.plot(kind=’bar’, stacked=True)

Here we can see that most of the records in the data are for the customer age range of 30–40 years, but proportion-wise, customers over 40 years old are more interested in buying vehicle insurance.

Response with driving license

train_data[[‘Driving_License’,’Response’]].value_counts().plot(kind = ‘bar’)

Output:

Here we can see that there are few records of customers with no driving license, and they also responded as no, which is fair enough.

Response with Region

counts = train_data.groupby([‘Region_Code’,’Response’]).Gender.count().unstack()

counts.plot(kind=’bar’, stacked=True, figsize=(35, 10

Output

Here we can see the distribution of responses from customers according to the region, and by zooming in, we can see that region 28 holds the most number of records.

Previously Insured and Response

counts = train_data.groupby([‘Previously_Insured’,’Response’]).Gender.count().unstack()

print(counts)

counts.plot(kind=’bar’, stacked=True)

Output:

Here we can see that most of the positive responses were from customers who had not previously received their vehicle insurance.

Reponse with Vehicle Age

With this variable, we can extract information about the most positive responses with respect to vehicle age.

counts = train_data.groupby([‘Vehicle_Age’,’Response’]).Gender.count().unstack()

print(counts)

counts.plot(kind=’bar’, stacked=True)

With this output, we can assume that the top part of the data is covered by vehicles aged 0 to 2 years, and mostly positive responses are from the customers who have vehicles aged 1 to 2 years.

Response with Vehicle Damage

Here we take a look at how customers choose to buy insurance when their vehicle is damaged.

counts = train_data.groupby([‘Vehicle_Damage’,’Response’]).Gender.count().unstack()

print(counts)

counts.plot(kind=’bar’, stacked=True)

Output

Here we can see that most customers are ready to buy insurance for their vehicle when it is already damaged.

Annual Premium

Since this is a continuous value, we can draw a density plot of the annual premium and see its description to know its minimum, maximum and average value.

train_data[‘Annual_Premium’].describe()

train_data[‘Annual_Premium’].plot(kind = ‘kde’)

+6

Here we can see that the minimum size of the annual premium is 2630, and the maximum is 540165, while the average value is around 30564.

Vintage

This column represents the count of days of a customer associated with the organisation.

train_data[‘Vintage’].describe()

train_data[‘Vintage’].plot(kind = ‘kde’)

Output

Here we can see that the minimum count of customer association days is 10 days, and the maximum is 299, while the average count is 154 days. So this means most of the customers have been associated with customers for the last 154 days.

Now that we have completed a basic exploratory data analysis, we will prepare it for the data modelling procedure.

Data processing

For modelling the data, we are going to use the scikit learn library that only works with the numerical values, and as we know, we have many string values in the data, so we will need to convert them into numerical data by label encoding we can do.

Label encoding

By looking at the data, we know that we have three variables that have categorical values in the form of the string value, so let’s convert these values.

train_data[‘Gender’]=train_data[‘Gender’].replace({‘Male’:1,’Female’:0})

train_data[‘Vehicle_Age’]=train_data[‘Vehicle_Age’].replace({‘< 1 Year’:0,’1–2 Year’:1, ‘> 2 Years’:2})

train_data[‘Vehicle_Damage’]=train_data[‘Vehicle_Damage’].replace({‘Yes’:1,’No’:0})

train_data.head()

Output:

Here we can see that all the values are in the numerical format while we have changed binary string values in the form of 0 and 1 binary integer values, and for the vehicle age variable, we have given 0, 1 and 2 as numerical categorical data points.

Now we would need to split this data into train and test sets so that we can evaluate the fitted model properly.

Data splitting

from sklearn.model_selection import train_test_split

X = train_data.iloc[:, 0:-1]

y = train_data.iloc[:, -1]

X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.25, random_state = 4)

Here we split the data in 75:25 so that we can train a model using 75% of the data and evaluate it with 25% data. Next, let’s move towards the data modelling procedure.

Data Modelling

Using the below line of code, we can train a random forest model using our processed data.

from sklearn.ensemble import RandomForestClassifier

model = RandomForestClassifier()

model.fit(X_train,y_train)

Let’s make predictions from the model and plot it once to see whether the model is working well or not.

y_pred = model.predict(X_test)

y_prediction = pd.DataFrame(y_pred, columns = [‘predictions’])

y_prediction[‘predictions’].value_counts().plot(kind = ‘bar’)

Ouput:

Here we can see that model is making predictions for both categories. So now, let’s evaluate the model.

Model Evaluation

In the above, we have done the data modelling using the random forest algorithm. Now, we are required to perform a model evaluation to tell about our model’s reliability and performance. Using the below lines of codes, we can measure the performance of our model

from sklearn.metrics import mean_absolute_error,mean_squared_error,confusion_matrix,r2_score,accuracy_score,classification_report

print(“Classification Report:n”,classification_report(y_test,y_pred

print(“Confusion Matrix:n”,confusion_matrix(y_test,y_pred

print(“Training Score:n”,model.score(X_test,y_test)*100)

print(“Mean Squared Error:n”,mean_squared_error(y_test,y_pred

print(“R2 score is:n”,r2_score(y_test,y_pred

print(‘model parameters n’,model.get_params(

print(‘model accuracy n’,accuracy_score(y_test,y_pred)*100)

Ouput:

Here we get most of the metrics in the final report that can be used for model evaluation, and looking at the report, we can say that our model is performing well with such huge data. However, we can make many improvements to the model, which we will discuss later in the article.

To learn about the evaluation metrics, we can go through this article, where we explain every critical metric we use in real life to evaluate such models.

To-Do List

In this procedure, we have performed every basic step which a data modelling procedure needs to go through, and below are the advanced steps we will perform to improve the results of this procedure:

• More EDA: as we can see in this article, we used only pandas for data visualisation, so in the next article, we will be using more visualisation libraries to perform EDA more properly.
• SMOTE analysis: in the data visualisation part, we can see that records for a positive response were too low, which can lead to biased modelling, so in the next article, we will see if we can improve the performance using SMOTE analysis.
• Cross Validation: We know that we got good enough results from the last modelling, and to improve the score, we can also use the cross-validation method so that model can become more reliable.

• GridSearchCV: it is a method used for finding the optimal model when the model has too many parameters, and Random Forest is one of those models that can be modified by changing parameters.

References

• Getting Started with Machine Learning Algorithms: Random Forest
• Evaluation Metrics for Machine Learning or Data Models
• Link to the codes

About DSW

Data Science Wizards(DSW ) aim to democratize the power of AI and Data Science to empower customers with insight discovery and informed decision making.

We working towards nurturing the AI ecosystem with data-driven, open-source technologies and AI solutions and platform to benefit businesses, customers, communities, and stakeholders.

Through our industry-agnostic flagship platform, UnifyAI, we are working towards creating a holistic approach to data engineering and AI to enable companies to accelerate growth, enhance operational efficiency and help their business leveraging AI capabilities end-to-end.

Our niche expertise and positioning at the confluence of AI, data science, and open-source technologies helps us to empower customers with seamless and informed decision-making capabilities.

DSW’s key purpose is to empower enterprises and communities with ease of using AI, making AI accessible for everyone to solve problems through data insights.

Connect us at contact@datasciencewizards.ai and visit us at www.datasciencewizards.ai

As every small, medium and large organisation are willing to become data-driven, the application of machine learning and artificial intelligence is increasing rapidly. Also, when we look at the market, we find that AI and ML market is one of the prominent and challenging markets nowadays. However, with these high values, this area also shows us a new source of risk. There can be various reasons, like an inadequately trained data model can lead to bad data-driven decisions, breaking the laws and many more.

So it becomes a compulsion to define governance in AI/ML development to minimise the risk and improve the development quality. So Let’s look at the broader picture of AI/ML model governance.

What is model governance?

When an organisation starts controlling the model development process, usage, and validation or assigns the model’s restrictions, responsibilities and roles, this process can be considered model governance.

We can compare model governance as a framework where this framework includes a set of strategies that help decide or specify how an organisation manages models within it. These strategies can be of the following type:

• Strategies to control models in production
• Strategies for versioning the models.
• Documentation reaction strategies.
• Model post-production monitoring
• Models comply with existing IT policies.

If any organisation can implement such a framework effectively, then they can get a high level of controllability and visibility into the model’s behaviour in production. At the same time, they get access to operational efficiencies and this help in achieving more benefits from AI investments.

Increased visibility also allows you to easily identify and resolve the problems and risks of ML and AI, such as models being biased. It also makes us increase the performance of the model in production because enhanced visibility allows you to spot issues that degrade models performance over time, such as data decay, model drift etc.

Importance of AI/ML Model Governance

We know that artificial intelligence and machine learning are relatively new areas, and many inefficiencies must be resolved. Model governance not only helps solve many of these problems but also improves every aspect of development and the potential value of any AI project.

We have already discussed that model governance helps in risk assessment, which is a major importance of AI governance as it ensures that the model stays out of risk with us. Many models are programmed to learn continuously after running them, and they can be biased because of biased data, which affects the decision-making of the model.

A set of rules in the governance framework allows us to audit and test for the model’s speed, accuracy and drift while in production to prevent further difficulties. Since various clauses can be applied to Ai governance, we can easily find out the ownership and accessibility of the model.

Such a governance framework can answer the following important questions:

• Who is the model’s owner?
• Do relevant rules and regulations restrict a model?
• Data on which model is trained?
• What sets of rules and regulations need to comply between the development stages?
• What are the steps required to monitor models after post-production?

Who is the model’s owner?

In an organisation, we can find that various people are arranged to complete various work of any project. So it becomes an important task to keep track of the work of every person involved in the project. This tracking helps improve collaboration, lesser duplication, quality improvement, and improve problem-solving. It always becomes necessary to keep this in the rule book so that well-catalogued inventory can allow people to build on the work together more easily.

Do relevant rules and regulations restrict a model?

Often models require following the local or domain rules and laws, such as a recommendation system developed to find relationships between different goods in a supermarket and representing a strong relationship between cigarettes and chewing gum. Most countries don’t allow to advertising of cigarettes, so this kind of business recommendation needs to be dropped. So before deploying a model into production, we should consider the following things:

• What local government defines regulations and laws relevant to any model’s functionality?
• What are the ways to test the model’s functionality are complying with defined laws?
• After making it into production, what will be the ways to monitor the model?

Data on which model is trained?

One very important thing about the machine learning model is that their results are indivisibly attached to the training data. So if there is any problem occurs in the development line, it becomes important to find the precise bad data points to replicate the issue. This is an ability in machine learning, and planning based on tracing the issues is crucial to avoid bigger failures.

Keeping track of the data source is a worthy task because this helps in measuring model drift frequency and stability of the models on old data. So it is always suggested to train the model on a high data range to get different results, but for stability, we should consider a low data range.

What sets of rules and regulations need to comply between the development stages?

There are various model development stages involved in the process, and one should have approval at every stage and keep records to ensure a high-quality standard. And it also reduces the chances of failure making their way through the production. This set of rules can tell us about the following things:

• Data quality
• Feature engineering
• Train/Test/Validation or cross-validation
• Compliance testing
• Code quality
• Version control
• Documentation

It is highly suggested to get the development thoroughly checked by a qualified team or person outside the development team.

What are the steps required to monitor models after post-production?

One of the most important things about model governance is that it gets complete after we become capable of regularly monitoring our deployed model’s performance using various aspects like model drift, data decay and failure in the development pipeline.

However, these aspects are internally connected, but they all represent their story differently. When things come into the post-production stages, it becomes necessary to maintain the system we have created and the new updates we are trying to give in the system. Early capturing of the likelihood of failure makes the system more accurate and reliable.

Final words

In the recent scenario, we have seen that every organisation are willing to become data-driven, or some are already data-driven, where machine learning models are helping them to complete various tasks. To maintain their high performance, effectiveness and quality, it is necessary to care about the model governance, which can lead your model to great success.

Data Science Wizards (DSW) is an Artificial Intelligence and Data Science start-up that primarily offers platforms, solutions, and services for making use of data as a strategy through AI and data analytics solutions and consulting services to help enterprises in data-driven decisions.

DSW’s flagship platform UnifyAI is an end-to-end AI-enabled platform for enterprise customers to build, deploy, manage, and publish their AI models. UnifyAI helps you to build your business use case by leveraging AI capabilities and improving analytics outcomes.

Connect us at contact@datasciencewizards.ai and visit us at www.datasciencewizards.ai

In current scenarios, we all have seen the growth of the EdTech industries. Even after Covid-19, we can say that the growth is doubled. There are many reasons behind this growth; one of them is artificial intelligence has found many gaps to fill. However, the impact of AI is huge across industries. According to Statista, the AI market is expected to reach USD 126 billion by the end of 2025.

A market and Markets report says that AI in EdTech is expected to grow to 3.68 billion USD by the end of 2023. These statics are sufficient to show EdTech’s reliability on AI. Especially, AI has found various use cases in online education systems. AI enables the industry right from delivering content to assessments and enhancing the learning experience. Here are the top applications of AI using which AI is helping EdTech to get more advanced.

Customise learning journeys

We find various unique values regarding learners’ interest and motivation to learn a subject. When we look back at the traditional approach to educational systems, they have been standardised for a long time because it was challenging to make them personalised for individuals. Nowadays, AI approaches have made it easy to create custom-tailored courses of learning based on learners’ behaviour observation and interpretation.

AI systems can find out the learning gaps based on the performances of learners, as well as these can also find them based on their behaviour in a classroom. As we all know, personalisation has been the KPI of many businesses here, and it plays a significant role in collecting huge audience engagement and focus.

Because of personalisation, learners get enabled to set up the pace of learning and ensure that they are on the right track of their learning journey.

Automated Assessments

Nowadays, we can see the impact of AI on grading because its use can be seen in both quantitative tests (multi-choice questions) and qualitative assessments (essay-based tests). This use of AI saves teachers time on grading and eliminates the chances of divergences based on favouritism and any other kind of corruption.

In addition, we also get facilitated with understanding the impact and effectiveness of learning programs based on group levels. This analysis can make us stand out from the ground if changes in the curriculum become in the appropriate direction.

AI Teaching Assistants

One of the most excellent applications of AI is adaptive chatbots, which can be utilised here as a teacher’s assistant in online programs. Just assume that there is a chatbot that can interact with the learners and clarify many basic doubts. At the same time, they are capable enough to create multiple real-time stages to let learners evaluate their understanding.

However, human teachers are a great way to teach anything, but they come with a working bandwidth and are incapable of always being available. This causes a gap and also an opportunity for AI to make a bridge. They can be available all the time and give freedom to learners using which they can approach them any time and get clarity.

Simplifying Non-Teaching tasks

Here, AI is not only to help the teachers and learners but also comes with many different use cases that are not core to teaching but are very helpful to run institutes easily. For example, using AI, we can easily handle records of students such as attendance, personal information etc., and teachers can be more focused on their teaching work. AI has also shown its capability in enrolment and admission processes so that more forces can get free.

Final Words

By looking at the above use cases, we can say the day is not so far where educators will need to rethink the learning journey and impact more students by giving extraordinary learning experiences, retention and focus. Furthermore, with the power of AI systems and tools, the EdTech industries can redefine the future and culture of education, where teachers and learners will be more focused on their actual work instead of being diverted because of too many other constraints or work.

About DSW

Data Science Wizards (DSW) is an Artificial Intelligence and Data Science start-up that primarily offers platforms, solutions, and services for making use of data as a strategy through AI and data analytics solutions and consulting services to help enterprises in data-driven decisions.

DSW’s flagship platform UnifyAI is an end-to-end AI-enabled platform for enterprise customers to build, deploy, manage, and publish their AI models. UnifyAI helps you to build your business use case by leveraging AI capabilities and improving analytics outcomes.

Connect us at contact@datasciencewizards.ai and visit us at www.datasciencewizards.ai

In supervised machine learning, there is a plethora of machine learning models like linear regression, logistic regression, decision tree and others. we use these models to resolve classification or regression problems, and ensemble learning is a part of supervised learning that gives us models that are built using several base models. Random forest is one of those ensemble learning models that are popular in the data science field for its high performance.

Technically, random forest models are built on top of decision trees and we have already covered the basics of a decision tree in one of our articles, so we recommend reading the article once to understand this article’s topic clearly. In this article, we will talk about random forests using the following points.

Table of content

• What is Random Forest?
• How Does a Random Forest Work?
• Important Features
• Important Hyperparameters
• Code Example
• Pros and Cons of Random Forest

What is Random Forest?

Random forest is a supervised machine-learning algorithm that comes under the ensemble learning technique. In supervised machine learning, a random forest can be used to resolve both classification and regression problems.

As discussed above that, it comes under the ensemble learning technique, so it works on top of many decision trees. We can say that decision trees are the base model of a random forest. The algorithm simply builds many decision trees on different data samples, and using the majority vote system solves the classification problem. In the case of regression, it uses the average of the decision trees.

How does a Random Forest Work?

When we talk about the working of the random forest, we can say that it gives outcomes by ensembling the results of many decision trees. Here if we talk about a classification problem, each decision tree predicts an outcome and whatever the class gets majority votes comes out as the final result of a random forest. Let’s take a look at the below image.

When we talk about the working of the random forest, we can say that it gives outcomes by ensembling the results of many decision trees. Here if we talk about a classification problem, each decision tree predicts an outcome and whatever the class gets majority votes comes out as the final result of a random forest. Let’s take a look at the below image.

The above image also gives the intuition behind the ensemble learning technique, where the final prediction is made by combining the results of several other models. The ensemble learning technique can be followed using two ways:

1. Bagging: this way, we divide data into various subsets and train the base models like decision trees in a random forest, and the majority vote for any class comes out as the final result.
2. Boosting: this way, we combine weak learners with strong learners and make a sequence of the model so that the final model is most accurate amongst every learner. For example, XG boost and ADA Boost models.

Random forest in ensemble learning uses the bagging method. We can say that every decision tree under the random forest uses a few samples from the whole training data to get trained and give predictions. Let’s talk about the steps involved in training the random forest algorithm.

Steps involved

1. First, it extracts n number of subsets from the dataset with k number of data points that we call n subsets.
2. n number of decision trees are constructed to get trained using n subsets.
3. Each decision tree gives predictions.
4. Final predictions are generated using the majority voting system for the classification problem and an averaging system for the regression problem.

Using the above four steps working of a random forest gets completed. Next, let’s discuss the important features of a random forest.

Important features

1. Highly immune to dimensionality: Since all data features are not considered in the making of decision trees, the whole random forest gives high performance even in a situation where data is high-dimensional.
2. Diversity: every decision tree uses some of the features from the data. That’s why the training procedure becomes different for the different decision trees. At final, we get more optimum results.
3. Data split: while making a random forest, we don’t really need to spit data in train and test because there will always be some percentage of data unknown for a decision tree.
4. Stable: random forests are stable algorithms when modelled because the majority voting or averaging system is used to make the final prediction.
5. Parallelization: as we know, every individual decision tree uses a part of the main data. It makes full use of the CPU to train random forests.
6. No overfitting: as the final results from the random forest come from the majority voting or averaging system and the decision tree uses subsets to get trained, there are fewer chances of overfitting.

Important Hyperparameters

In the above we have discussed the working and features of random forests, here we will discuss the important hyperparameters of any random forest using which we can control the random forest while increasing its performance and making it’s working or calculation faster.

1. n_estimators- The number of decision trees required to build the random forest.
2. max_features- Maximum number of features that random forest will use from data to split the data.
3. mini_sample_leaf — minimum number of leaves is required to split the decision tree node.
4. n_jobs — we use it to speed up the calculation of random forest because it tells the number of processors a system needs to train the model.
5. random_state- just like for other models, it controls the randomness of the sample.

Code Example

In the above discussion, we have seen how random forest work and their important hyperparameters. Now after knowing this, we need to know how it works using any tool. So here we will look at the simple implementation of the random forest using the python programming language.

We will use randomly generated data and the sklearn library in this implementation. So let’s start with generating data.

from sklearn.datasets import make_classification
X,y = make_classification(n_samples = 2000, n_features = 6, n_informative = 3)
print(‘data features n’,X)
print(‘data_classes n’, y)

Output:

Here we can see features and classes of randomly generated data. In the making of data, we have generated 2000 samples that have 6 features and one target variable.

Let’s build a model

from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier(max_depth = 4, random_state = 42)

Here we have created an object named clf that consists of a random forest classifier. Let’s train the model.

 

clf.fit(X,y)
print(‘count of the decision trees :’,len(clf.estimators_

Output:

Here we can see that 100 decision trees are under the random forest. Now we can draw a decision tree from our random forest using the following lines of code:

import matplotlib.pyplot as plt

from sklearn import tree

plt.figure(figsize=(12, 10

tree.plot_tree(clf.estimators_[0],max_depth = 2)

plt.show()

Output:

Here we have implemented a random forest, and to increase the explainability, Now we can draw a decision tree from a random forest using the following lines of code:

print(clf.predict([[0, 0, 0, 0, 0, 0]]

print(clf.predict([[1, 0, 1, 0, 1, 1]]

Output:

Now results from the model are in front of us and this is how we can implement a basic random forest. Let’s take a look at the pros and cons of the random forest algorithm.

Pros and Cons of Random Forest

Pros

1. We can use it for both classification and regression problems.
2. It does not overfit.
3. It can also work with data that contains null values.
4. High-performing with high dimensional data.
5. It maintains diversity in the results.
6. Highly stable.

Cons

1. Random forest is a highly complex algorithm.
2. Training time is more because it takes more time to calculate, develop and train decision trees.

Final words

Under the series of articles, this article consisted the information about the random forest, which is a machine learning algorithm used to resolve problems that come under supervised learning. In the article, we have discussed the what, why and how of random forests. Using an example we looked at its implementation. Looking at the pros and cons of this model, we can say that it has such features and functionality that gives us higher accuracy. Still, before using this model we should understand the basic concept behind the model so that we can tune it appropriately.

About DSW

Data Science Wizards (DSW) is an Artificial Intelligence and Data Science start-up that primarily offers platforms, solutions, and services for making use of data as a strategy through AI and data analytics solutions and consulting services to help enterprises in data-driven decisions.

DSW’s flagship platform UnifyAI is an end-to-end AI-enabled platform for enterprise customers to build, deploy, manage, and publish their AI models. UnifyAI helps you to build your business use case by leveraging AI capabilities and improving analytics outcomes.

Connect us at contact@datasciencewizards.ai and visit us at www.datasciencewizards.ai