In this series of articles, we have introduced the ensemble learning methods, and we have seen how we can implement these methods using the Python programming language. One thing which we have planned to discuss later is boosting technique in ensemble learning. Ensemble learning can be thought of as the combined results of multiple machine learning algorithms, which can be further categorized into two sections based on the difficulty levels:

  • Simple ensemble learning
  • Advanced ensemble learning

By looking at the complexity of boosting algorithms, we can think of them as a part of advanced ensemble learning methods. However, many of the modellers misinterpret the term boosting. In this article, we will have a brief explanation of this and will get to know how the boosting techniques of ensemble learning give power to the machine learning process to improve the accuracy of predictions.

Table of content

  • What is Boosting?
  • Why use Boosting algorithms? 
  • Key Stages in Boosting Algorithms
  • Types of Boosting Algorithm

What is Boosting?

Boosting is a type of ensemble learning where we try to build a series of weak machine-learning models. These sequentially aligned models are known as base or weak learners and combine them to create a strong ensemble model.  

Unlike traditional ensemble methods that assign equal weights to all base learners, boosting assigns varying weights to each learner, focusing more on the instances that were previously misclassified. The iterative nature of boosting allows subsequent learners to correct the mistakes made by previous ones, resulting in a powerful ensemble that excels in handling complex datasets. Let’s just understand the boosting using an example.

Understanding Boosting Using an Example

Suppose we have a dataset of images classified as either dog or cat. Now we need to build an animal classification model using the boosting method. Here we can start by developing an initial weak learner, such as a decision tree. This weak learner is trained to predict whether the image contains a cat or a dog based on a single feature, such as the presence of a specific pixel.  

Unlike traditional ensemble learning, here, we need to define the weight of all training examples in the dataset, and initially, we assign equal weights. Some images may be misclassified, resulting in prediction errors. 

Now we adjust the weights of misclassified examples to give them more importance in the next iteration. The intuition is to focus on the challenging examples that the weak learner struggles with. By assigning higher weights to these examples, we force the subsequent weak learners to pay more attention to them. We repeat the process and create another weak learner, and we continue this iterative process, building multiple weak learners while adjusting the weights of training examples. Each new learner tries to address the misclassifications made by the ensemble of previous learners. 

Finally, we combine all the weak learners into a strong ensemble model by assigning weights to their predictions. The weights are determined based on the performance of each weak learner during training, and to make predictions on new, unseen data, we apply the ensemble model to the features of the image. Each weak learner provides a prediction, and their weighted votes determine the final prediction of whether the image contains a cat or a dog. 

Let’s understand why it becomes necessary to use boosting algorithms in machine learning procedures. 

Why use Boosting algorithms? 

There are multiple reasons behind the use of boosting algorithms, as they offer various benefits in many machine-learning procedures. Here are some key reasons why boosting algorithms are commonly employed:

  • One of the main reasons behind the adoption of boosting algorithms is to enhance the accuracy of predictive models. Utilizing boosting algorithms enables procedures to handle complex patterns and capture subtle relationships within the data, leading to more accurate predictions.
  • In the case of the dataset being noisy and outlier-prone, boosting algorithms are robust and reliable. The iterative nature of boosting allows the models to learn from mistakes and focus on challenging examples, thus reducing the impact of noisy data points and outliers.
  • Boosting algorithms are versatile across tasks and can be applied to various types of machine learning tasks, including classification, regression, and ranking problems. They have been successfully used in domains such as finance, healthcare, natural language processing, and computer vision.
  • As part of ensemble learning, boosting algorithms help in enhancing the interpretability of the procedure. Since it analyzes the contribution of different features during the process, a modeller can gain a better understanding of the relative importance and impact of various input variables. When we look at the model interpretability, it enables us with the analysis of contributions of weak learners to gain insights and understanding from the ensemble model. 
  • Boosting algorithm increases the performance of the procedure on unseen data. By iteratively improving the model’s performance during training, boosting helps reduce overfitting and enhances the model’s ability to make accurate predictions on new, unseen examples. 

Key Stages in Boosting Algorithms

Boosting techniques typically follow these compact steps:

  1. Initialize weights for training examples.
  2. Train a weak learner on the weighted dataset.
  3. Evaluate the weak learner’s performance.
  4. Update the weights based on the weak learner’s performance.
  5. Build the next weak learner to correct previous mistakes.
  6. Repeat steps 3-5 for multiple iterations.
  7. Combine the weak learners into a strong ensemble model.
  8. Use the ensemble model to make predictions.
  9. Optionally, iterate further or finalize the boosting process.

One noticeable thing here is that the boosting techniques can be further classified into other categories, and specific boosting algorithms may have additional steps or variations in the process. To know more about them, let’s take move forward to the next section. 

Types of Boosting Algorithm

When we dig deeper into the subject of boosting algorithms, we find several types of it, and some of them which are popular and frequently useful are as follows:

Adaptive Boosting(AdaBoost): People belonging to the data science and machine learning field know this algorithm as one of the earliest boosting algorithms. It works by assigning higher weights to misclassified examples, allowing subsequent weak learners to focus on those instances. AdaBoost combines the predictions of multiple weak learners to create a strong ensemble model, or we can say the above-explained example is similar to the working style of AdaBoost.

Gradient Boosting: As the name suggests, this technique utilizes gradient descent optimization to minimize a loss function. It sequentially builds weak learners, each aiming to minimize the errors of the previous models. Popular implementations of gradient boosting include XGBoost and LightGBM, which introduce additional enhancements and optimizations.

CatBoost(Categorical Boosting): This boosting algorithm is a general framework which mainly focuses on handling categorical variables effectively. Basically, it uses an ordered boosting scheme and employs unique techniques to handle categorical features without requiring extensive preprocessing. One of the major profits of CatBoost is that it provides high-quality predictions with robustness against overfitting.

XGBoost (Extreme Gradient Boosting): This Algorithm is based on gradient boosting techniques, but using a specialized tree-based learning algorithm makes it different from the general gradient boosting algorithm. As the name suggests, It focuses on achieving high efficiency and speed while maintaining accuracy. LightGBM implements advanced techniques such as leaf-wise tree growth and histogram-based computation for faster training.

LightGBM (Light Gradient Boosting Machine): This algorithm is also based on gradient boosting techniques, and it is popular because of its scalability and performance. In technicality, It employs a regularized objective function and incorporates techniques like tree pruning, column subsampling, and parallel processing.

Stochastic Gradient Boosting: This boosting technique is also known as Random Forest Regression because it combines the idea of gradient boosting and random feature selection as in a random forest algorithm. Because of this combination, it becomes capable of introducing randomness by using a subset of features at each iteration, enhancing diversity among the ensemble models and reducing overfitting.

These above-explained boosting algorithms are the most popular algorithm in the space,, and by looking at the explanation, we can conclude that each algorithm has its own characteristics, advantages, and parameter configurations. The choice of the boosting algorithm depends on the specific task, dataset, and performance requirements.  

Conclusion 

In the article, we have discussed the basic understanding of boosting algorithms. It is an important part of ensemble learning methods as it enables the creation of highly accurate and robust predictive models. By leveraging the strength of weak learners and focusing on challenging instances, boosting algorithms produce ensemble models with enhanced predictive power. Understanding boosting principles and exploring popular algorithms like AdaBoost, Gradient Boosting, CatBoost, and Stochastic Gradient Boosting can empower machine learning engineers to effectively utilize boosting techniques in their projects. Embracing boosting in ensemble learning opens the doors to improved accuracy, robustness, and interpretability, ultimately leading to better decision-making and impactful solutions across various domains.

In our series of articles discussing detailed information about machine learning models, we have already covered the basic and theoretical parts of support vector machine algorithms. In an overview, we can say that this algorithm is based on a hyperplane that separates the data points. The data points nearest to the separating hyperplane are called support vectors, and they are responsible for the position and orientation of the hyperplane. This algorithm gives a higher accuracy because it maximises the margin between the classes while minimising the error in regression or classification.

Now that we know how the support vector machine works, we must check this algorithm with real-world data. In this article, we are going to look at how this algorithm works and how we can implement it in our machine-learning project. To complete this motive of our, we will follow the below table of content.

Table of Content

  • Importing data
  • Data Analysis
  • Data Processing
  • Data Modelling
  • Model Evaluation

Let’s start by gathering data,

Importing data

In this article, we are going to use the MNIST dataset, which is a popular image classification dataset and holds a large database of handwritten digits that is commonly used for image classification tasks.

So here, we will try to model this data with a support vector machine, which can predict which image belongs to which class. This data is also available within the sklearn library.

Now let’s just start by importing the data into the environment.

import pandas as pd

from sklearn.datasets import fetch_openml

mnist = fetch_openml(‘mnist_784’)

Now let’s convert the data into a Pandas Dataframe object

X, y = pd.DataFrame(mnist.data), pd.Series(mnist.target)

X.info()

Output:

Here we can see that the data is in the form of a DataFrame, and it has around 70000 entries aligned with 784 columns, and the column name varies from pixel1 to pixel784. As we have already discussed that SVM gives high performance with data including a large number of features, So here, SVM can give optimal results. Before applying this data to an SVM model, we need to perform some data analysis. So let’s start by exploring insights into the data.

Data Analysis

We will divide this section into two steps where we will look at the descriptive insights of the data, and we will perform exploratory data analysis. Let’s find out information from the data.

Statistical Data Analysis

Here in this sub-part, we will take a look at the statistical details hidden inside the data.

X.info()

Output:

Here we can see the name of all 23 columns while we can also see that there are no null values in any columns of the data. Let’s use the describe method with the data.

X.describe()

Output:

Here, we can see some more details about the data. Here we can see that the maximum value in any of the columns is 254, and the minimum is 0, which indicates that the pixel number of any image varies from 0 to 255. Let’s take a look at the shape of the data.

print(“shape of X”, X.shape, “shape of y”, y.shape)

Output:

Let’s see the head of the X side.

After describing and seeing some rows, we are clear that any column in the data has no null values, as well as we can make it clear in our next step. Let’s move towards the basic EDA.

Basic EDA

Let’s start by analysing our target variable then slowly we will move towards the other independent variables of the data.

import matplotlib.pyplot as plt

print(y.value_counts())

y_counts = y.value_counts()

plt.figure(figsize=(8,6))

plt.bar(y_counts.index, y_counts.values)

plt.xlabel(‘Class Label’)

plt.ylabel(‘Count’)

plt.title(‘Distribution of Classes’)

plt.show()

Output:

Here we can see that there is enough data for every class of the data, ensuring lesser chances of the class imbalance problem. Also, we can see how the count of different classes is distributed throughout the data. Now let’s move towards the independent variable.

Let’s check for the null values on the independent data side.

#countoing missing value in the data

missing_values_count = X.isnull().apply(pd.value_counts)

counts = missing_values_count.sum(axis=1)

counts.plot(kind=’bar’)

Output:

 

Here we can see that there is no null value in the data. Low lets try to draw one of the image from the data.

import matplotlib.pyplot as plt

# Plot the first number in X

plt.imshow(X.iloc[0].values.reshape(28, 28), cmap=’gray’)

plt.axis(‘off’)

plt.show()

Output:

Here we can see how the images inside the data is bounded. Now our term is to preprocess the data because the model package defined under the sklearn library requires preprocessing data to model it.

Data Preprocessing

As we know that the values under this data are numerical, we would need to standardise and normalise the data. We do this to save the model from becoming overfitted.

X = X/255.0

from sklearn.preprocessing import scale

X_scaled = scale(X)

The above code helps us normalise and scale the data. Now we can split the data.

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size = 0.3, train_size = 0.2 ,random_state = 10)

After splitting the data, we are ready to model the data.

Data Modelling

To model this data using the SVM algorithm, we are going to use the model package given by the sklearn library under the SVM package.

from sklearn.svm import SVC

first_model = SVC(kernel = ‘rbf’)

first_model.fit(X_train, y_train)

Output:

This is how we can simply call and fit the model on the data. Let’s validate its results.

Model Evaluation

Till now, we have seen the data analysis, preprocessing and modelling. Now once we have the trained model, we need to validate the process we followed is optimum or not. To do so we can use a confusion matrix and accuracy. Using the below code, we can visualise our model performance as a confusion matrix.

y_pred = first_model.predict(X_test)

import seaborn as sns

# accuracy

from sklearn.metrics import confusion_matrix, accuracy_score

print(“accuracy:”, accuracy_score(y_true=y_test, y_pred=y_pred), “\n”)

# Generate the confusion matrix

cm = confusion_matrix(y_test, y_pred)

cmap = sns.diverging_palette(10, 220, sep=80, n=7)

# Plot the confusion matrix as a heatmap

sns.heatmap(cm,annot=True, cmap=cmap ,fmt=’g’)

Output:

Here we can see that the model we have defined is more than 94% accurate, and also, in the confusion matrix there are no major classes predicted wrong by the models. Now we can also check for the classification report of the model.

from sklearn.metrics import classification_report

print(classification_report(y_test, y_pred))

Output:

 

Here we can see that model is performing great, and its accuracy is 94%. Now let’s conclude this topic as we have got an optimum model for MNIST image classification.

Conclusion

In this article, we have seen how an SVM model can perform with real-life data when there is a huge number of features. As explained in the last article, the SVM is high performing when the feature of the data is higher than the data points, and there are rare fields where such data generates. So if we have a huge number of data features in a dataset and the task is classification, SVM becomes an optimum option to model the data that also requires less calculation and power than the other statistical machine learning algorithms.

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 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.

Component of ModelOps

Benefits of ModelOps

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

  • Improved Development Environment
  • Better Testing
  • Controlled model versioning
  • 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

Is MLOps a combination of DataOps and ModelOps?

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.

DataOps with MLOps

Value Enhancement of AI/ML Solutions

Technical Component Management in MLOps Deployment

MLOps will Enhance the Reliability of AI and ML

Integration of MLOps will Remain Challenging.

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.

The usage of Feature Stores will Increase

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.

 
 

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.

Practices Behind DataOps

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

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

Benefits of DataOps

  1. 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.
  2. 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.
  3. 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.
  4. Data Quality: the main focus of DataOps is to improve the quality by using the practices such as data validation, profiling, and governance
  5. Data Security: DataOps helps easily take control over data encryption, data access control, and data masking so that data security can be ensured.
  6. 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

Evolution from ETL to DataOps

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.

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.

  • DevOps (development operations)
  • Data Engineering

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.

  • Getting correct features from raw data.
  • Compiling feature into training data.
  • Managing features in the production
  • Check the quality.
  • Re-use the data.
  • Versioning and control of data.
  • 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

  • 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.

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.

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.

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.

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.

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.

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.

Organisations don’t understand the need for AI projects

Company’s data is not appropriate

Organisations lack the skill-set

Organisations struggle to find good vendors to work with

Organisations are not able to find a good use case

Low explainability of AI Team

Fear of overhaul legacy systems

The complexity of AI Program Integration

AI Governance

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.

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.

Table of Contents

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

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.

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:

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:

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

Output:

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


train_data[‘Age’].describe()

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)

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

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

print(counts)

counts.plot(kind=’bar’, stacked=True)

Output:

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

print(counts)

counts.plot(kind=’bar’, stacked=True)

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

print(counts)

counts.plot(kind=’bar’, stacked=True)

Output

train_data[‘Annual_Premium’].describe()

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


+6

train_data[‘Vintage’].describe()

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

Output

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.

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:

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)

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:

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:

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:

  • 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

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.

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.

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

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.

  • 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 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.

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:

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

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.

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.

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.

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.

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.

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.

Table of content

  1. What is Ensemble Machine Learning
  2. Ensemble learning Techniques
  1. Averaging
  2. Weighted Averaging
  3. Advanced Techniques
  4. Stacking
  5. Blending
  6. Bagging
  7. Boosting

What is Ensemble Machine Learning?

As discussed above, ensemble learning is an approach that comes under supervised machine learning, where we use the combined results of several supervised learning models. So let’s try to understand it more using an example. Let’s say a person has written an article on an interesting topic, and he wants to know the preliminary feedback before publishing it. So he thinks about the following possible ways.

  • Ask five friends to rate the article: This way, he got the proper idea of the article because some of them chose to give an honest rating to him. But there is also a possibility that the people are not subject matter experts on the topic of his article.
  • Ask 50 people to rate the article: Here, he has included all his friends and some strangers to give feedback and got more generalized and diversified feedback. This approach can be the best of all the approaches he chose to get feedback for his work.

Ensemble Learning Techniques

The following are techniques to perform ensemble learning:

Simple techniques

  • Max Voting: we generally use this method for solving classification problems. Using each data point, multiple models give their outcome, and this outcome is considered as the vote. Using the majority vote-winning technique, we reach the final result.
  • Friend 2 = 4
  • Friend 3 = 5
  • Friend 4 = 2
  • Friend 5 = 3
  • Averaging: Just like in the max voting system, here also, all the models take each data point to make predictions, but we consider the final result as the average of results from all the models. The averaging method is mostly applied in regression problems.

Advanced techniques

  • Stacking: if the above-discussed methods can be considered basic ensemble learning methods, then methods after this can be considered advanced ensemble learning. Stacking is a method where several learners are attached one after another. Decision tree, KNN and SVM algorithms can be considered examples of base models used in stacking learning. The following steps a stacked ensemble learning model takes to give final results:

  1. Using the trained model, we calculate the predictions using the test data.

  1. Models are trained on the training set.
  2. A validation set and a train set are used to make the prediction.
  3. Validation set and predictions made using validation set used as a feature to train a new model.
  4. A new model and test data are used to make the final prediction.
  • Bagging: Bagging is an advanced form of ensemble learning where it uses multiple models to give their individual results on a sub-part of data. By combining these results gives a final outcome. Since multiple models have a high chance of giving the same results while inputs are similar, bootstrapping comes into the picture to fail this condition. It helps create various subsets of whole data and then trains multiple models on those subsets. The below picture is an illustration of the bagging technique.

  1. A base model is assigned to learn from each subset.
  2. The final prediction comes out as the combined result from all the models.
  1. At the initial stages, all the data points have similar weightage.
  2. A base model gets trained on the subset and gives predictions using the whole data.
  3. Errors are calculated using the initial model’s original value and predicted value.
  4. Incorrectly predicted data points take higher weights.
  5. Again a base model is used to get trained and give predictions on the dataset.
  6. The process from steps 3 to 6 is repeated until the final learner doesn’t occur.

Final words

Here in the article, we have discussed the basic introduction of ensemble machine learning. Using an example, we tried to understand how it works and learn about the different ensemble learning techniques, such as max voting, averaging, bagging and boosting. In our next articles, we will discuss the models based on ensemble learning techniques.

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.