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Generative AI & Large Language Models
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AI & Machine Learning
MLOps: Machine Learning in Production
Most machine-learning projects never reach production, and many that do quietly decay. Learn the engineering around the model: reliable data pipelines, reproducible experiments, safe deployment patterns such as canary releases, and the monitoring that catches drift before your customers do.
What you’ll learn
- Map the full machine-learning lifecycle and where projects typically fail
- Design data and feature pipelines with validation and no training–serving skew
- Track experiments so any model can be reproduced and audited
- Choose batch, online or streaming serving, and roll out models safely
- Monitor models in production and respond to data and concept drift
Curriculum
Module 1 · From notebook to production
5 lectures- 01From notebook to productionInteractiveFree16 min
- 02Data and feature pipelinesInteractiveFree18 min
- 03Experiment tracking and reproducibilityInteractiveFree17 min
- 04Model servingInteractiveFree18 min
- 05Monitoring and driftInteractiveFree18 min
Module 2 · Building the platform
5 lectures- 06CI/CD for machine learningIncluded with the course
- 07Feature storesIncluded with the course
- 08Model registries and governanceIncluded with the course
- 09Orchestrating training pipelinesIncluded with the course
- 10Scaling training and inferenceIncluded with the course
Module 3 · Operating ML systems
4 lectures- 11A/B testing and online evaluationIncluded with the course
- 12Cost and performance optimizationIncluded with the course
- 13Responsible AI in productionIncluded with the course
- 14Capstone: an end-to-end ML platformIncluded with the course
Who this course is for
- ML engineers and data scientists shipping models
- DevOps and platform engineers supporting ML teams
- Engineering managers responsible for AI features in production
Before you start
Machine Learning Foundations or equivalent, plus basic familiarity with Git and the command line.
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Training a team on this?
We can combine MLOps: Machine Learning in Production with other courses into a learning path built around your team’s roles and goals.