Presenting

ML Engineering program

A long-term, applied program focused on building, deploying, and operating real-world machine learning systems.

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5th Jan 2025

20 months

Our partners

What you will learn in this program?

This program is built around real ML engineering practice, not theoretical coursework. Over two years, participants work through the full lifecycle of production machine learning systems, closing the gap between data science, software engineering, and infrastructure. The focus is on building systems that survive real-world constraints: scale, failures, cost, security, and change.

01.

Designing End-to-End ML Systems

You will design complete ML pipelines — from raw data ingestion and feature engineering to model serving and downstream integration — with an emphasis on reliability and long-term maintainability.

02.

Production Deployment and Cloud Platforms

You will deploy and operate models in production environments using Kubernetes and managed cloud platforms such as AWS SageMaker and Azure ML, learning how to make models stable, observable, and cost-aware.

03.

MLOps, Automation, and AI Agents

You will build automated ML workflows: training, validation, deployment, and rollback. The program explores modern MLOps practices and the use of AI agents to reduce manual engineering effort.

04.

Monitoring, Experimentation, and Reliability

You will implement monitoring, logging, A/B testing, drift detection, and rollback strategies to ensure models behave correctly after deployment and evolve safely over time.

05.

Large-Scale Data Processing

You will work with large datasets using distributed systems such as Spark and Dask, focusing on performance, data quality, and reproducibility in real production pipelines.

06.

Enterprise-Grade Ethics, Security, and Governance

You will learn how to integrate security, ethical considerations, and compliance requirements into ML systems, making them suitable for enterprise and regulated environments.