Overview

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This programme introduces MLOps practices for building, deploying, and maintaining reliable machine learning systems in real-world environments. You’ll begin by understanding how operational machine learning differs from traditional software development. This includes the challenges of managing changing datasets, experimental results, model versions, and performance after deployment. Next, you’ll explore techniques for recording experiments, controlling data changes, and reproducing training results. These practices improve collaboration, traceability, and consistency across the machine learning lifecycle. You’ll then learn how automated workflows coordinate data preparation, training, testing, validation, and release activities. This helps teams reduce manual effort, identify issues earlier, and deliver updates more efficiently. The later sections focus on managing approved models, packaging prediction services, and releasing them into production environments. You’ll also examine how continuous monitoring, drift detection, and retraining strategies help preserve accuracy and reliability over time. By the end of this course, you will be able to: • Explain the principles of MLOps and how they differ from traditional DevOps practices. • Track experiments, control data changes, and reproduce training results. • Build automated workflows for model development, testing, and delivery. • Manage model versions, approvals, and lifecycle stages. • Package and deploy models as scalable prediction services. • Monitor production performance and identify data or model drift. • Apply retraining strategies to maintain long-term model effectiveness. Designed for aspiring ML engineers, AI engineers, data scientists, and software developers, this programme provides the practical skills needed to move machine learning solutions from experimentation into dependable production use. To be successful, you should have a basic understanding of Python, machine learning concepts, and model training. Develop the operational knowledge required to create machine learning systems that are reproducible, scalable, and ready for production.

Syllabus

Introduction to MLOps and Experiment Tracking

This module introduces the foundational concepts of MLOps, focusing on how machine learning workflows are organized, tracked, and reproduced in real-world environments. Learners explore experiment tracking, dataset versioning, and reproducibility practices using tools such as MLflow and DVC. Through hands-on workflows, they learn how to manage machine learning experiments systematically, compare model performance, and maintain consistent development pipelines.

Pipelines , CI/CD and Model Registry

This module focuses on automating and orchestrating machine learning workflows using production-oriented MLOps practices. Learners build ML pipelines, implement CI/CD workflows, and manage model lifecycle stages through registries and version control systems. The module emphasizes scalable automation, workflow reliability, deployment readiness, and collaborative model management across machine learning teams.

Model Deployment and Production Monitoring

This module explores how machine learning systems are deployed, monitored, and maintained in production environments. Learners work with APIs, Docker, monitoring systems, and observability workflows to deploy scalable ML services and track production performance. The module also covers drift detection, retraining strategies, and continuous improvement practices required to maintain reliable and adaptive machine learning systems over time.

Course Wrap-Up and Assessment

This module consolidates learning through a hands-on vision project and final assessment. Learners demonstrate their ability to design, train, and evaluate complete their MLOps journey .