In this course, we will explore the world of MLOps (Machine Learning Operations) and dive deep into the best practices for building and managing robust, scalable, and automated machine learning pipelines. As machine learning models become more sophisticated and critical to business success, organizations are realizing the importance of operationalizing ML workflows to ensure reliability, repeatability, and maintainability. MLOps bridges the gap between data science and IT operations, enabling faster and more efficient deployment of ML solutions.

We begin with an introduction to MLOps, where you will learn the principles, challenges, and benefits of applying DevOps methodologies to machine learning systems. From there, we move into Automated MLOps, covering the complete pipeline from data ingestion, data validation, and feature engineering to model training, evaluation, and versioning. You’ll understand how to automate the entire ML lifecycle using pipelines that minimize manual intervention while maintaining transparency and control.

Next, we cover deployment strategies including batch inference, real-time APIs, and edge deployment. We’ll discuss different deployment environments such as on-premise servers, cloud platforms, and hybrid setups, highlighting the trade-offs and considerations for each.

The course then shifts focus to monitoring models in production, detecting data and concept drift, and implementing automated retraining mechanisms to ensure models remain accurate and relevant over time. You will learn how to set up alerts, metrics, and dashboards for continuous model health tracking.

We’ll also explore key MLOps tools like MLflow, Kubeflow, TFX, and others that are shaping the industry standard for ML workflow automation and governance.

To solidify your understanding, the course concludes with two real-world use cases where you'll apply the concepts learned to build end-to-end automated MLOps pipelines, from raw data to production-ready and self-healing ML systems.

Fully Automated MLOps