Author: saqibkhan

Data in machine learning are broadly categorized into two types − numerical (quantitative) and categorical (qualitative) data. The numerical data can be measured, counted or given a numerical value, for example, age, height, income, etc. The categorical data is non-numeric data that can be arranged in categories with or without meaningful order, for example, gender, blood group, etc.

Further, the numerical data can be categorized into discrete and continuous data. The categorical data can also be categorized into two types − nominal and ordinal. Let’s understand these types of data in machine learning in detail.

What is Data in Machine Learning?

Data in machine learning is a set of observations or measurement that are used to train, validate and test a machine learning model. Data is very crucial in machine learning because it is the foundation of creating accurate machine learning model.

What are Types of Data?

The data used in machine learning can be broadly categorized into two types −

• Numerical (quantitative) data
• Categorical (qualitative) data

Numerical (Quantitative) Data

The numerical (quantitative) data is data that can be measured, counted or given a numerical value. The examples of numerical data are age, height, income, number of students in class, number of books in a shelf, shoe size, etc.

The numerical data can be categorized into the folloiwng two types −

• Discrete Data
• Continuous Data

1. Discrete Data

The discrete data is numerical data that is countable, finite, and can only take certain values, usually whole numbers. Examples of discrete data are number of students in class, number of books in a shelf, shoe size, number of ducks in a pond, etc.

2. Continuous Data

The continuous data is numerical data that can take any value within a specified range including fractions and decimals. Examples of continuous data are age, height, weight, income, time, temperature, etc.

What is true zero?

True zero represents the absence of the quantity being measured. For example, height, weight, age, temperature in Kelvin are examples of data with true zero. As the height with 0 CM represents the absolute absence of height, 0K temperature represents no heat. But temperature in Celsius (or Fahrenheit) is an example of data with false zero.

We can categorize the numerical data into the following two types on basis of true zero −

• interval data − quantitative data with equal intervals between data points. Examples are temperature (Fahrenheit), temperature (Celsius), pH, SAT score (200-800), credit score (300-850), etc.
• ratio data − same as interval data but with true zero. Examples are weight in KG, number of students, income, speed, etc.

Categorical (Qualitative) Data

The categorical (qualitative) data can be categorized with or without a meaningful order. For example, gender, blood group, hair color, nationality, the school grades, level of education, range of income, ratings, etc.

The categorical data can be divided into the folloiwng two types −

• Nominal Data
• Ordinal Data

1. Nominal Data

The nominal data is categorical data that can not be arranged in an order or rank. The examples of nominal data are gender, blood group, hair color, nationality, etc.

2. Ordinal Data

The ordinal data is categorical data can be ordered or ranked with a specific attribute. The examples of ordinal data are the school grades, level of education, range of income, ratings, etc.

The Four Levels of Data Measurement

We can categorized data into four level − nominal, ordinal, interval, and ratio. These levels of measurement are divided on basis of the following four features −

• Categories − data can be categorized but not in an order.
• Rank Order − data can be categorized with some meaningful order.
• Equal Difference − The difference between subsequent data remains same.
• True Zero − it represents the absence of quantity being measured.

The following table highlights how the four level of measurement are associated with the above discussed four features.

	Nominal	Ordinal	Interval	Ratio
Categories	Yes	Yes	Yes	Yes
Rank Order		Yes	Yes	Yes
Equal Difference			Yes	Yes
True Zero				Yes

The nominal data is categorical data with no meaningful order whereas ordinal data is a categorical data with meaningful order. The concept of true zero plays role to differentiate interval and ratio data. Ratio data is same as interval data but it includes true zero.

Monetizing machine learning refers to transforming machine learning projects into profitable web applications. Monetizing an ML project involves many steps including problem understanding, ML model development, web application development, model integration to web application, serverless cloud deployment of the final web app and finally monetizing the application.

The idea behind monetizing machine learning project is simple. What we will do? We will build a simple fast SaaS application for project and monetize it.

Creating a Software as a Service (SaaS) is a good choice for its many benefits such as reduced costs, scalability, ease of management, etc.

To monetize, we can consider subscription based pricing, premium features, API access, advertising, custom service, etc.

Let’s understand how to transform a machine learning project into a web application and monetize it.

Understanding the Problems

Take a real-world problem and do research on whether we can solve the problem using machine learning. If yes, find out if it is feasible to implement the solution using all your resources.

Who will benefit from the ML solution − the final end users? Who is the end user of the final machine learning application? Understanding the users is very important when you are analyzing a real-world problem.

The problem falls under what type of task in the machine learning context. What types of models can be used to solve the problem? Whether the problem can be solved using regression, classification, or clustering models. A proper understanding of the problem will help you to find the answers of these questions.

What would be the business model?  Whether web application of mobile application, API sale or combination of two or more?

What type of data we have? Structured or unstructured. Analyze the data properly before going to solve the problem. It will help to decide what type of machine learning approach you should follow.

What computational resources you have?  How to develop ML models? − on premise or cloud-based.

Understand the real world problem properly that you want to solve.

Defining the Solution

What will be the final solution of the problem?

Define the solution − how you will present the solution to the end user whether you will develop a web application, mobile app, API or a combination.

What is the business model?

Define your business model. What type of product for machine leaning model you want to create? One of the best solution is to create a software as a service (SaaS). You can consider for PaaS, AIaaS, Mobile Applications, API Service, and Selling ML APIs, etc.

Building a web application using serverless technology is a good choice to showcase your machine leaning application or solution. It is also easy to monetize your solution later on.

When you decide how you bring the solution to world, the next step is defining the core features of your machine learning solution. User interaction with the application, navigation, login, security, data privacy, etc., should be defined before diving into building the machine learning model.

Developing Machine Learning Model

The next step is to start developing your machine learning model. But before actually starting, you need to understand the machine learning models in detail. Without having a good knowledge of ML models you can’t be able to decide which model to select for your problem.

Understand Machine Learning Models

It is very important to understand different types of machine learning models and how to choose the right one for your project. Understanding the ML models will help select an appropriate model for your machine learning application.

Understanding that the underlined solution will fall under a particular machine learning task will help you decide on the proper model. Suppose your solution falls under the classification, then you have many choices of machine learning model. You can apply Naïve base, logistic regression, k-nearest neighbor, decision trees, and many more. So having a proper understanding of models is required before going to make your hands dirty with data and model training.

Types of ML Models

You should have a good understanding of the following types of machine learning models −

• Supervised − regression, classification,
• Unsupervised − clustering, dimensionality reduction
• Reinforcement − game theory, multi agent systems
• Neural Networks − recognition (image, speech), NLP

Select the right model

The most important step in building a machine learning model is to select the right one that solves your business problem. While selecting the right ML model, you should consider different factors such as −

• Data characteristics − consider the nature of data (structured, unstructured, time series data) to select a suitable model.
• Problem type − determine whether your problem is regression, classification or other task.
• Model complexity − determine the optimal model complexity to avoid the overfitting or under fitting.
• Computational resources − consider the computational resources to choose a complex or simple model.
• Desired outcome − consider it to perform the model evaluation.

Train Machine Learning Model

After selecting the right model for your machine learning problem, the next is to start building the actual machine learning model. There are different ways to build an ML model. The easiest way is to use a pre-trained model and custom train on your own datasets.

Pre-trained models − Pre-trained models are machine learning models that are trained with huge datasets. If your data is similar to the datasets on which the pre-trained models are trained, you can select them for your solution. In such cases, you need only to build a web or mobile application and deploy it on the cloud for worldwide users.

Fine-Tuning Pre-Trained Model − You can consider fine-tuning a pre-trained model on your custom datasets. You can fine-tune any publicly available model using machine learning libraries/ frameworks such as TensorFlow/ Keras, PyTorch, etc. You can also consider some online platforms such as AWS Sagemaker, Vertex AI, IBM Watson Studio, Azure Machine Learning, etc. for fine-tuning purposes.

Build from Scratch − You can consider building a machine learning model from scratch if you have all the required resources. It may take more time compared to the above two ways but may cost a little less.

Amazon SageMaker is a cloud-based machine-learning platform to create, train, evaluate, and deploy etc. machine-learning models on the cloud.

Evaluate Model

You have trained your ML model on your custom dataset. Now you have to evaluate the model on some new data to check whether the model is performing as per our desired outcomes or not.

For evaluating your machine learning model, you can calculate the metrics such as accuracy, precision, recall, f1 score, confusion matrix, etc. Based on these metrics, you can decide on a further course of action − finalizing the current model or going back with training again.

You can consider ensemble methods, combining multiple models (bagging and boosting) to improve model performance and reduce overfitting.

Deploy Demo Model online

Before building a full-fledged web application and deploying it on a cloud server, it is advised to deploy your machine learning model online. There are many free hosting providers where you can deploy your machine learning model and get feedback from the real time users. You can consider the following providers for this purpose −

• Hugging Face Space
• Streamlit Cloud
• Heroku

Creating Machine Learning Web Applications

As of now, you have developed your ML model and deployed the demo model online. Your model is working perfectly. Now you are ready to build a full-fledged machine learning web or mobile application.

You can consider the following technology stack to build web applications −

• Python frameworks – Flask, Django, FastAPI, etc.
• Web development (frontend) concepts − HTML, CSS, JavaScript
• Integrating machine learning models − how to integrate using APIs or libraries − Rest API

Deploying on the Serverless Cloud

Deploying your ML application on a serverless cloud will open doors to monetize your application. It will reach a worldwide audience. Choosing a cloud platform is a good idea to host your app. Going serverless can benefit you with reduced costs, scalability, ease of management, etc.

The following is a list of some well-known serverless cloud service providers best for your machine learning web applications −

• Google Cloud Platform − Google Cloud Functions
• Amazon Web Services − AWS Lambda, AWS Fargate, AWS Amplify Hosting
• Microsoft Azure − Microsoft Azure Functions
• Heroku
• Python Anywhere
• Cloudflare Workers
• Vercel Functions

You can use services like EC2 for computing power and S3 for storage.

Monetizing Your Machine Learning Applications

Now, your machine learning application is live on the cloud. You can promote, and market to your users. You can give them special offers to use your application.

Your machine learning application can reach to any corner of the world. When you get enough user, you can think about monetizing your application. There are different strategies to monetize ML web application including subscription model, pay-per-use pricing, advertising, premium features, etc.

• Subscription Model − Subscription-based pricing tiers (e.g., basic, premium, enterprise).
• Freemium Model − Offer a free version with limited features, and charge for advanced features.
• API Access − Charge businesses to access your AI tools via an API.
• Custom Solutions − Offer bespoke content generation services for larger clients.
• Advertising − you can also consider putting advertisement on your application but keep it in mind that advertisements will distort your application’s premium look.

Marketing and Sales

Marketing and sales are important to grow any business. Continuous marketing is required for a better sale of the product.

You can sell your Machine Learning application APIs on different online API marketplaces.

You can consider the following API Marketplaces −

• RapidAPI
• APILayer
• AWS Marketplace
• Infosys API Marketplace
• IBM API Connect

Monetizing machine learning has now become easy but more competitive. Monetizing the ML application needs a detailed market analysis before starting the building application. Each step of the machine learning software development needs deep research. Building a minimum viable product (MVP) and testing it before building a full-fledged web application is advisable.

Data leakage is a common problem in machine learning that occurs when information from outside the training dataset is used to create or evaluate a model. This can lead to overfitting, where the model is too closely tailored to the training data and performs poorly on new data.

There are two main types of data leakage: Target Leakage and Train-test Contamination

Target Leakage

Target leakage occurs when features that are not available during prediction are used to create the model. For example, if we are predicting whether a customer will churn, and we include the customer’s cancellation date as a feature, then the model will have access to information that would not be available in practice. This can lead to unrealistically high accuracy during training and poor performance on new data.

Train-test Contamination

Train-test contamination occurs when information from the test set is inadvertently used in the training process. For example, if we normalize the data based on the mean and standard deviation of the entire dataset instead of just the training set, then the model will have access to information that would not be available in practice. This can lead to overly optimistic estimates of model performance.

How to Prevent Data Leakage?

To prevent data leakage, it is important to carefully preprocess the data and ensure that no information from the test set is used in the training process. Some strategies for preventing data leakage include −

• Splitting the data into separate training and test sets before doing any preprocessing or feature engineering.
• Only using features that would be available at the time of prediction.
• Using cross-validation to evaluate model performance instead of a single train-test split.
• Ensuring that all preprocessing steps (such as normalization or scaling) are applied to the training set only and then using the same transformations on the test set.
• Being aware of any potential sources of leakage, such as date or time-based features, and handling them appropriately.

Implementation in Python

Here is an example in which we will be using Sklearn breast cancer dataset and ensure that no information from the test set is leaked into the model during training −

Example

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC

# Load the breast cancer dataset
data = load_breast_cancer()# Separate features and labels
X, y = data.data, data.target

# Split the data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)# Define the pipeline
pipeline = Pipeline([('scaler', StandardScaler()),('svm', SVC())])# Fit the pipeline on the train set
pipeline.fit(X_train, y_train)# Make predictions on the test set
y_pred = pipeline.predict(X_test)# Evaluate the model performance
accuracy = accuracy_score(y_test, y_pred)print("Accuracy:", accuracy)

Output

When you execute this code, it will produce the following output −

Accuracy: 0.9824561403508771

MLOps (Machine Learning Operations) is a set of practices and tools that combine software engineering, data science, and operations to enable the automated deployment, monitoring, and management of machine learning models in production environments.

MLOps addresses the challenges of managing and scaling machine learning models in production, which include version control, reproducibility, model deployment, monitoring, and maintenance. It aims to streamline the entire machine learning lifecycle, from data preparation and model training to deployment and maintenance.

MLOps Best Practices

MLOps involves a number of key practices and tools, including −

• Version control − This involves tracking changes to code, data, and models using tools like Git to ensure reproducibility and maintain a history of all changes.
• Continuous integration and delivery (CI/CD) − This involves automating the process of building, testing, and deploying machine learning models using tools like Jenkins, Travis CI, or CircleCI.
• Containerization − This involves packaging machine learning models and dependencies into containers using tools like Docker or Kubernetes, which enables easy deployment and scaling of models in production environments.
• Model serving − This involves setting up a server to host machine learning models and serving predictions on incoming data.
• Monitoring and logging − This involves tracking the performance of machine learning models in production environments using tools like Prometheus or Grafana, and logging errors and alerts to enable proactive maintenance.
• Automated testing − This involves automating the testing of machine learning models to ensure they are accurate and robust.

Python Libraries for MLOps

Python has a number of libraries and tools that can be used for MLOps, including −

• Scikit-learn − A popular machine learning library that provides tools for data preprocessing, model selection, and evaluation.
• TensorFlow − A widely used open-source platform for building and deploying machine learning models.
• Keras − A high-level neural networks API that can run on top of TensorFlow.
• PyTorch − A deep learning framework that provides tools for building and deploying neural networks.
• MLflow − An open-source platform for managing the machine learning lifecycle that provides tools for tracking experiments, packaging code and models, and deploying models in production.
• Kubeflow − A machine learning toolkit for Kubernetes that provides tools for managing and scaling machine learning workflows.