This online class covers machine-learning art and generative image tools such as DALL·E and Stable Diffusion.
A guide to MLOps
This guide covers machine learning operations, including tools and practices for deploying models.
About A guide to MLOps
A Guide to MLOps is a five-part hands-on tutorial that walks through taking a machine learning model from a notebook experiment to a deployed, monitored system. The parts cover local training and evaluation, moving the project to GitHub and an S3 bucket with CI/CD, serving the model through a FastAPI endpoint in a Docker container on Kubernetes, monitoring prediction logs and data drift, and closing the loop with Label Studio labeling and retraining. The guide names the tools it uses, including DVC, Git, GitHub Actions, CML, Docker, BentoML, Fluent Bit, Evidently AI, and Label Studio.
The guide is written for engineers and small teams. It says it focuses on SMEs and on tools that fit established workflows. The steps depend on accounts and resources with a cloud provider and GitHub, and the final part explains how to delete them.
Key features
- Step-by-step path from a notebook experiment to a monitored ML system
- Versioned Python training pipelines tracked with Git and DVC
- CI/CD pipeline on GitHub Actions with CML comparison reports in pull requests
- Model packaging with BentoML and a FastAPI prediction endpoint in Docker
- Kubernetes rollout with Fluent Bit prediction logs stored in S3
- Label Studio workflow where the model suggests labels for human review and retraining
Best for: Engineers and small teams who want a hands-on, tool-based tutorial for moving a machine learning model from experimentation into production.
Good for
- Covers the full lifecycle from local training to retraining in one sequence of parts
- Uses tools that the guide describes as used in production
- Includes a final cleanup step to remove cloud resources and credentials
Watch out for
- Requires accounts and resources with a cloud provider and GitHub
- Cloud resources left running can lead to charges, according to the guide
A guide to MLOps at a glance
- Website
- mlops.swiss-ai-center.ch
- Category
- AI & Machine Learning › AI for Education
- Type
- Website
- Pricing
- Not confirmed. Check the site
- Platforms
- Web
- Open source
- Not stated
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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A guide to MLOps FAQ
Which tools does the guide use?
The guide uses DVC, Git, GitHub Actions, CML, Docker, BentoML, Fluent Bit, Evidently AI, and Label Studio, along with FastAPI and Kubernetes.
Why does the guide include a cleanup step?
The guide says cleanup removes cloud provider resources, the GitHub repository, the Personal Access Token, and local project directories. It states this prevents surprise bills and removes exposed credentials.
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