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mkdocs.yml
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repo_url: https://github.com/swiss-ai-center/a-guide-to-mlops
repo_name: swiss-ai-center/a-guide-to-mlops
site_name: A guide to MLOps
site_author: Swiss AI Center contributors
site_url: https://mlops.swiss-ai-center.ch/
site_description: >-
A simple yet complete guide to MLOps tools and practices - from a conventional way to a modern approach of working with ML projects.
copyright: Copyright © 2022 - 2024 Swiss AI Center
nav:
- 🏁 Introduction:
- "Syllabus": syllabus.md
- "Concept": concept.md
- "Tools": tools.md
- 🏠 Local training and model evaluation:
- "Introduction": part-1-local-training-and-model-evaluation/introduction.md
- "Chapter 1.1 - Run a simple ML experiment with Jupyter Notebook": part-1-local-training-and-model-evaluation/chapter-11-run-a-simple-ml-experiment-with-jupyter-notebook.md
- "Chapter 1.2 - Adapt and move the Jupyter Notebook to Python scripts": part-1-local-training-and-model-evaluation/chapter-12-adapt-and-move-the-jupyter-notebook-to-python-scripts.md
- "Chapter 1.3 - Initialize Git and DVC for local training": part-1-local-training-and-model-evaluation/chapter-13-initialize-git-and-dvc-for-local-training.md
- "Chapter 1.4 - Reproduce the ML experiment with DVC": part-1-local-training-and-model-evaluation/chapter-14-reproduce-the-ml-experiment-with-dvc.md
- "Chapter 1.5 - Track model evolution with DVC": part-1-local-training-and-model-evaluation/chapter-15-track-model-evolution-with-dvc.md
- "Conclusion": part-1-local-training-and-model-evaluation/conclusion.md
- "Clean up": part-1-local-training-and-model-evaluation/clean-up.md
- ☁️ Move the model to the cloud:
- "Introduction": part-2-move-the-model-to-the-cloud/introduction.md
- "Chapter 2.1 - Move the ML experiment code to the cloud": part-2-move-the-model-to-the-cloud/chapter-21-move-the-ml-experiment-code-to-the-cloud.md
- "Chapter 2.2 - Move the ML experiment data to the cloud": part-2-move-the-model-to-the-cloud/chapter-22-move-the-ml-experiment-data-to-the-cloud.md
- "Chapter 2.3 - Reproduce the ML experiment in a CI/CD pipeline": part-2-move-the-model-to-the-cloud/chapter-23-reproduce-the-ml-experiment-in-a-cicd-pipeline.md
- "Chapter 2.4 - Track model evolution in the CI/CD pipeline with CML": part-2-move-the-model-to-the-cloud/chapter-24-track-model-evolution-in-the-cicd-pipeline-with-cml.md
- "Chapter 2.5 - Work efficiently and collaboratively with Git": part-2-move-the-model-to-the-cloud/chapter-25-work-efficiently-and-collaboratively-with-git.md
- "Conclusion": part-2-move-the-model-to-the-cloud/conclusion.md
- "Clean up": part-2-move-the-model-to-the-cloud/clean-up.md
- 🚚 Serve and deploy the model:
- "Introduction": part-3-serve-and-deploy-the-model/introduction.md
- "Chapter 3.1 - Save and load the model with BentoML": part-3-serve-and-deploy-the-model/chapter-31-save-and-load-the-model-with-bentoml.md
- "Chapter 3.2 - Serve the model locally with BentoML": part-3-serve-and-deploy-the-model/chapter-32-serve-the-model-locally-with-bentoml.md
- "Chapter 3.3 - Build and publish the model with BentoML and Docker locally": part-3-serve-and-deploy-the-model/chapter-33-build-and-publish-the-model-with-bentoml-and-docker-locally.md
- "Chapter 3.4 - Build and publish the model with BentoML and Docker with the CI/CD pipeline": part-3-serve-and-deploy-the-model/chapter-34-build-and-publish-the-model-with-bentoml-and-docker-with-the-cicd-pipeline.md
- "Chapter 3.5 - Deploy and access the model on Kubernetes": part-3-serve-and-deploy-the-model/chapter-35-deploy-and-access-the-model-on-kubernetes.md
- "Chapter 3.6 - Continuous deployment of the model with the CI/CD pipeline": part-3-serve-and-deploy-the-model/chapter-36-continuous-deployment-of-the-model-with-the-cicd-pipeline.md
- "Chapter 3.7 - Use a self-hosted runner for the CI/CD pipeline": part-3-serve-and-deploy-the-model/chapter-37-use-a-self-hosted-runner-for-the-cicd-pipeline.md
- "Chapter 3.8 - Train the model on a Kubernetes pod": part-3-serve-and-deploy-the-model/chapter-38-train-the-model-on-a-kubernetes-pod.md
- "Conclusion": part-3-serve-and-deploy-the-model/conclusion.md
- "Clean up": part-3-serve-and-deploy-the-model/clean-up.md
- 🏷️ Labeling the data and retrain:
- "Introduction": part-4-labeling-the-data-and-retrain/introduction.md
- "Chapter 4.1 - Setup Label Studio": part-4-labeling-the-data-and-retrain/chapter-41-setup-label-studio.md
- "Chapter 4.2 - Label new data with Label Studio": part-4-labeling-the-data-and-retrain/chapter-42-label-new-data-with-label-studio.md
- "Chapter 4.3 - Link the model to Label Studio": part-4-labeling-the-data-and-retrain/chapter-43-link-the-model-to-label-studio.md
- "Chapter 4.4 - Retrain the model from new data with DVC": part-4-labeling-the-data-and-retrain/chapter-44-retrain-the-model-from-new-data-with-dvc.md
- "Conclusion": part-4-labeling-the-data-and-retrain/conclusion.md
- "Clean up": clean-up.md
- 🪄 Conclusion:
- "Clean up": clean-up.md
- "Conclusion": conclusion.md
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