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1c3139 Lucas Gonthier 2026-06-30 12:04:21
Initial commit: course content (Machine Learning, MLOps) in EN and FR Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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# Introduction
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> Lesson 01 · MLOps
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**Objectives**
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- Understand what MLOps is and why models need more than good accuracy.
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- Recognize how ML systems differ from traditional software.
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- See the lifecycle the rest of the course builds on.
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## What is MLOps?
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MLOps applies engineering and operations practices to the machine learning lifecycle so models
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can be built, shipped, and maintained reliably and repeatably.
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## Why it matters
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A model that works in a notebook is not a product. Production adds data drift, reproducibility,
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deployment, scaling, and monitoring concerns that the modelling step alone does not address.
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## The ML lifecycle
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1. Data collection and versioning.
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2. Experimentation and tracking.
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3. Training pipelines and automation.
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4. Deployment.
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5. Monitoring and feedback.
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Next: The ML Lifecycle *(planned)* · [Course overview](/en/MLOps)