AI Model Lifecycle — Complete Guide
AI Model Lifecycle — Complete Guide: free step-by-step lesson with examples, common mistakes, and interview tips — part of ML.NET Tutorial on Toolliyo Academy.
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ML.NET Tutorial · Lesson 20 of 100
AI Model Lifecycle
Foundations → Models → NLP & advanced → MLOps
Foundations · 1 — Context & data · ~6 min · Module 2: Machine Learning Basics
What is this?
Lifecycle: data → train → evaluate → register → deploy → monitor → retrain.
Why should you care?
AIPredict MLOps treats models like versioned artifacts with owners and rollback.
See it live — copy this example
Use a .NET console or Web API project with Microsoft.ML. Run dotnet run after pasting.
// versions
// models/fraud-2026-07-19.zip + metrics.json + data-hash.txt
Console.WriteLine("register → deploy → monitor → retrain");What happened?
- Champion/challenger helps safe swaps.
- Drift and metric drops trigger retrain.
- Keep lineage of data hash.
Practice next
- Version a zip + metrics.
- Define rollback.
- Schedule retrain trigger.
- Add data-hash file.
- Write a one-page runbook.
Remember
Version artifacts. Monitor then retrain. Rollback path.
AIPredict model registry
Each fraud.zip has metrics + hash.
Outcome: Incidents can roll back in minutes.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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