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ai-tools-lab12 lessons

MLflow

A practical, student-friendly introduction to MLflow—what it does, where it fits, and how to use it safely.

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Course Lessons

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1
What it is
A practical, student-friendly introduction to MLflow—what it does, where it fits, and how to use it …
2
What you’ll be able to do after this
- Explain what problem MLflow solves - Complete a hands-on mini project using a free/trial tier - Id…
3
Prereqs
- Basic web literacy (files/folders, copy/paste) - Optional: a free GitHub account
4
Fast orientation (10 minutes)
1. Create an account (free/trial). 2. Find the dashboard + where API keys / projects live. 3. Locate…
5
Core concepts (teach-back checklist)
- Inputs/outputs: what goes in, what comes out - Limits: rate limits, quotas, model limits, file lim…
6
Step 1 — Set up
- Create a project/workspace. - Turn on any free-tier features you need. - Create a test resource (e…
7
Step 2 — Run a “hello world”
- Do the smallest possible action that proves it works. - Screenshot the successful result for your …
8
Step 3 — Add guardrails
- Turn on usage limits / budgets if available. - Store secrets in .env locally (never in code).
9
Project (Free/Trial Tier)
Deliverable: a small “Capability Demo” that proves you can use MLflow in a real workflow.
10
What you can do on a paid plan (and why it matters)
- Higher limits / faster throughput - Team collaboration and governance - Better monitoring / reliab…
11
Common pitfalls (read before you spend)
- Accidentally leaking API keys to GitHub - Turning on “all events” / overly broad triggers that cre…
12
Reflection (5 minutes)
- Where would MLflow sit in an end-to-end AI product? - What is the single highest-risk failure mode…