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1 | # MLOps |
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| 2 | ||||||||
| 3 | Taking machine learning systems to production: reproducibility, automation, deployment, and |
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| 4 | monitoring. |
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| 5 | ||||||||
| 6 | **Prerequisites:** the [Machine Learning](/en/Machine%20Learning) course, basic command line and Git. |
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| 7 | ||||||||
| 8 | ## Syllabus |
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| 9 | ||||||||
| 10 | 1. [Introduction](/en/MLOps/01%20Introduction) |
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| 11 | 2. The ML Lifecycle *(planned)* |
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| 12 | 3. Experiment Tracking *(planned)* |
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| 13 | 4. Data and Model Versioning *(planned)* |
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| 14 | 5. Pipelines and Automation *(planned)* |
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| 15 | 6. Model Deployment *(planned)* |
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| 16 | 7. Monitoring and Observability *(planned)* |
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| 17 | ||||||||
| 18 | --- |
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19 | [Machine Learning](/en/Machine%20Learning) · [Home](/en) |
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