Take ML Models to Production with MLOps
Compare MLflow, Weights & Biases, DVC and more, then build live: track experiments in MLflow and serve with BentoML.
Not scheduled yet — no date, time or fee fixed. The most-voted topic is hosted next.
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Learn this topic with a mentor
Don't want to wait for the webinar? Pick how you'd like help. Nothing is paid now — you see the fee before anything is booked.
- Bring your own work — code, campaign, report or plan
- Mentor matched to this topic
- Time, format and fee confirmed by email first
What this session would cover
Proposed outline — the mentor finalises the agenda once this topic is scheduled.
- 1The real problem: models stuck in notebooks
- 2The tool landscape: MLflow, Weights & Biases, DVC, Kubeflow, BentoML, KServe, Ray, Feast — what each is for and where they overlap
- 3How to choose: team size, integrations, where your data lives, and today's pricing and availability (checked live, not from memory)
- 4Live practical task: Track experiments in MLflow and serve with BentoML
- 5Build the reusable output together: MLOps checklist
- 6When you don't need a new tool at all, then live Q&A on your own setup
Who it's for
- • Students and developers who want hands-on practice with real tools
You'd leave with
- MLOps checklist
- A shortlist of 2–3 options from MLflow, Weights & Biases, DVC, Kubeflow and others, with the criteria to pick one
- Hands-on practice: track experiments in MLflow and serve with BentoML
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