Speaker notes: Open with the objective. Introduce yourself and the Swiss AI Center briefly. Five HES from the HES-SO (HEIG-VD, HEIA-FR, HE-Arc, HEVS and HEPIA) work on a project called Swiss AI Center, which accelerates AI adoption in SMEs digital transition. HEIG-VD is responsible for setting up tools to manage ML experiments from code to production.
Speaker notes: By the end of this workshop, you will know how to turn a messy notebook into a reproducible, cloud-deployed ML system. This is the workshop objective.
Speaker notes: This is the reality. Ordinary ML problems: - Models trained on data trapped in spreadsheets, logs, and sensors. - Forecasts built on fragile business systems. - Classifiers trained on years of messy history. - Anomaly detection on manufacturing or server metrics.
Speaker notes: This is the surprise. The required surrounding infrastructure is vast and complex.
Speaker note: Pause after the question.
Speaker notes: When reading the mobile app example, also mention websites orally. Root cause: everyone manages their own code, data, and models. Each quote is a pain point the workshop addresses: - Not the same results → experiments are not reproducible (Part 1). - No idea which change helped → improvements are hard to track (Part 2). - Use my model in an app → models are hard to share and deploy (Part 3). - Did the data change? → drift and degradation go unnoticed (Part 4).
Speaker notes: These are the people who build and run models, often without a dedicated MLOps team: data scientists in notebooks, software engineers moving to ML, and small teams in SMEs where the same people both build and operate models.
Speaker notes: This is the path from notebook to production. MLOps adapts software and DevOps practices to machine learning, improving the management and quality of ML projects.
Speaker notes: This is the fence. This is our scope — what we cover and what we do not cover. This guide is not: - a heavy, all-in-one MLOps platform - a course on LLMs, machine learning, or deep learning This guide is: - a lightweight, composable path - for small teams who already use Git - from notebook to production, one step at a time
Speaker notes: Our proposal: a hands-on, cloud-deployed, monitored workflow you can transfer to your own experiment.
Speaker notes: This is the salient idea. Say each principle as a contrast pair: - Version-controlled: from scattered files to one source of truth. - Composable: from monolithic platforms to best-of-breed tools. - Incremental: from big bang to one step at a time. - Pragmatic: from perfect theory to working production. Then expand: - Version-controlled means tracking code, parameters, and deployments in Git, with data versions linked to them, so every model can be reproduced. - Composable means using best-of-breed open-source tools that each solve one problem well. - Incremental means adopting one practice at a time, not all at once, with each step fitting into the existing workflow with minimal friction. - Pragmatic means prioritizing reproducibility first, then automation, deployment, then monitoring, then feedback loops. In this guide, that loop means using AI-assisted labeling to add data and retrain iteratively. That is why we avoid all-in-one MLOps platforms that need dedicated infrastructure or databases. A lightweight, Git-native stack is a pragmatic path from notebooks to production while you stay in control of your tooling.
Speaker notes: This is the story. This is the hands-on mission. This is also the cycle. "From notebook to production" is the recurring slogan; return to it during the talk.
Speaker notes: This is the protagonist. Put the audience in the role. They just joined a team of data scientists and ML engineers. The team's mission is to identify planets or moons from images, but the model is stuck in a Jupyter Notebook. The audience's mission: help improve and deploy it.
Speaker notes — Bonus slide content: Without version control, code, data, and hyperparameters are scattered across individual machines and reproducing a result is hard. What changes: a shared codebase with Git, a common versioned dataset with DVC, and documented, reproducible steps that anyone can rerun.
Speaker notes — Bonus slide content: Today, model changes are hard to track and it is hard to know if they help. What changes: metrics and plots arrive in the pull request, so the team can review changes before merging.
Speaker notes — Bonus slide content: Today, models are hard to use outside the notebook and deployments are fragile. What changes: package the model with BentoML and Docker, expose it as an API, and deploy it on Kubernetes so other applications can call it.
Speaker notes — Bonus slide content: Today, model drift and degradation are often discovered too late. What changes: continuously log predictions, detect drift with Evidently AI, and alert the team when the model needs attention.
Speaker notes — Bonus slide content: A model is only as good as its data, and data ages. What changes: collect new data with Label Studio, use the model's own predictions to speed up labeling, and feed the new data back into training.
Speaker notes: Launch the workshop. Open mlops.swiss-ai-center.ch and start with the first chapter. The guide is open source and built iteratively from feedback. If you hit a pain point, a confusing step, or a typo, open an issue on GitHub or tell us during the workshop. That is when the feedback is most useful. If you launch the cluster during the workshop, beware the costs. We will come back to cleanup at the end.
Speaker notes: This is the close. Deliver on the opening objective. Three things to leave them with: 1. Clean up — open docs/clean-up.md. Stress that every cloud resource they created is still costing money. Walk through the checklist: delete the Kubernetes cluster, Artifact Registry, Storage bucket, service account, and finally the project. Unlink billing and remove the payment method if they created a fresh account. 2. Apply — they now have a closed loop: version control with Git and DVC, automated training and review with CI/CD and CML, serving with BentoML, monitoring with Fluent Bit and Evidently AI, and retraining with Label Studio. The value is not the tools; it is the workflow they can reuse on their own experiment. 3. Star — if the guide was useful, star the repository on GitHub. This helps others find it and gives us a signal that the work is valuable. Cycle back to the objective: "You started with a notebook stuck on one machine. Now you know how to take a model to production — and keep it healthy." Final words options — pick one, do not say "thank you": - Salute the venue: "Enjoy the rest of the conference." - Salute the audience: "Good luck with your models." - Congratulate them: "You now have a roadmap from notebook to production. The next step is yours." - Call to action: "Go clean up your resources, then go build something." - Convention: simply gesture toward the slide and step back. Deliver on the opening objective.