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MLOps Tutorial 2026 - Learn MLOps Online for Free

7 Modules27 Lessons5260 Learners

Dive into the fundamentals with our MLOPs tutorial. Learn MLOps best practices and streamline your ML operations for enhanced productivity and reliability.

Start LearningLast Updated: 10th February, 2026

Explore our MLOps Tutorial, your pathway to mastering the realm of Machine Learning Operations. If you're a beginner wondering how to learn MLOps, you're in the right place. Our MLOps tutorial for beginners is designed to demystify the world of machine learning operations. Learn MLOps essentials, from managing ML models to automating workflows.

We'll guide you through the entire process, ensuring you gain a solid foundation in this crucial field. By the end of this MLOps tutorial, you'll be equipped with the knowledge and skills necessary to streamline and optimize your machine learning projects. Start your journey to learn machine learning operations today.

The modules covered in this MLOPs Tutorial are:

Introduction to MLOps
Version Control for ML
Docker for ML
Continuous Integration and Delivery (CI/CD) for ML
Monitoring and Logging for ML
Scaling ML Workloads
Ethical Considerations in MLOps

Course Curriculum

Module 1Introduction to MLOps

Introduction to MLOps and Its Importance

Challenges in ML Model Development and Deployment

Key Principles of MLOps (Machine Learning Operations)

Module 2Version Control for ML

Introduction to Git and GitHub - MLOPs

Best Practices for Version Controlling ML Projects

Branching and Merging Strategies in MLOPs

Managing Data and Model Artifacts with Git LFS

Module 3Docker for ML

Introduction to Docker and Containerization in MLOPs

Building Docker Images for ML Applications

Running Docker Containers Locally and on the Cloud - MLOPs

Best Practices for Containerization of ML Applications

Module 4Continuous Integration and Delivery (CI/CD) for ML

CI/CD for Machine Learning

Setting up a CI/CD Pipeline Using Jenkins

Automated Testing and Validation of ML Models

ML Models Deployment in Kubernetes

Module 5Monitoring and Logging for ML

Importance of MLOPs Monitoring and Logging

Setting up Monitoring and Logging Infrastructure

Defining Metrics and Alerts for ML models

Visualizing and Analyzing Model Performance

Module 6Scaling ML Workloads

Introduction to Distributed Computing for ML

Setting up a Distributed ML Environment with Apache Spark

Scaling ML workloads: Docker Swarm vs Kubernetes

Best Practices for Scaling ML Workloads

Module 7Ethical Considerations in MLOps

Overview of Ethical Considerations of MLOps

Bias and Fairness in Machine Learning Models

MLOps Security and Privacy Considerations

Implementing Ethical Guidelines in MLOps

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Summary

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