Machine Learning Engineering & MLOps: From Model to Production

Ideal For

This course is designed for:

• Data scientists who can build models in notebooks but want to learn how to deploy, serve, and maintain them in live production environments

• Software engineers and backend developers transitioning into machine learning engineering and AI infrastructure roles

• ML practitioners and researchers who have built models in academic or experimental settings and want to understand production-grade engineering practices

• Data engineers who work with data pipelines and want to extend their skills into the machine learning system layer

• Technical leads and engineering managers overseeing AI product teams who want a deep, practical understanding of the full ML engineering lifecycle

• Full-stack developers building AI-powered applications who want to understand how to build and serve their own ML models reliably

• DevOps and platform engineers working in organisations adopting machine learning who want to understand the ML-specific requirements of their infrastructure

• Career switchers with a programming background who want to enter the high-demand, high-salary ML engineering job market

Recommended prior knowledge: Comfortable with Python and familiar with basic machine learning concepts — able to train a simple model using scikit-learn or similar. No prior DevOps or cloud experience required.

Summary of Learning  

By the end of this course, participants will be able to:

• Explain the complete ML production lifecycle and identify the engineering gaps that cause most ML projects to fail before reaching live deployment

• Instrument a full machine learning training pipeline with MLflow — logging parameters, metrics, and artefacts, comparing experiments in the tracking UI, and managing model versions through a structured model registry

• Build reproducible, production-grade feature engineering pipelines using scikit-learn Pipelines and ColumnTransformer — with automated data validation using Great Expectations at every stage

• Package a trained machine learning model as a fully documented, production-ready REST API using FastAPI — including input validation, error handling, health check endpoints, and API versioning

• Write optimised, multi-stage Dockerfiles for ML applications, build and test containerised model serving systems locally using Docker Compose, and push production images to container registries

• Build a complete GitHub Actions CI/CD pipeline that automatically tests, trains, evaluates, containerises, and deploys a new model version on every code commit — with automated rollback on deployment failure

• Detect and diagnose model degradation in production — distinguishing between data drift, concept drift, and system-level failures — using Evidently AI statistical monitoring and feature distribution tracking

• Configure production alerting for model performance degradation and build automated retraining triggers that respond to detected drift without manual intervention

• Deploy a containerised ML model to a live cloud platform with auto-scaling configured — managing compute costs, storage, and endpoint reliability in a production cloud environment

• Build and present a complete, end-to-end production ML system — covering pipeline, serving API, CI/CD automation, and monitoring dashboard — as a portfolio-ready engineering project

Course Description

Participants will learn to build production-ready machine learning systems from the ground up — covering experiment management, feature engineering pipelines, model packaging, containerised deployment with Docker, CI/CD automation, and real-time monitoring for drift and performance degradation. The course every data scientist needs to become a complete ML engineer.