Applied Deep Learning Foundations for AI Practitioners
Lesson 4: Tuning, Monitoring, and Deployment Readiness
Lesson Objectives
By the end of this lesson, students should be able to:
- Explain tuning as improving performance through controlled changes.
- Name deployment readiness checks.
- Describe why monitoring after launch matters.
Lesson Content
Tuning means changing the workflow to improve results. That could mean adding better examples, cleaning labels, changing the model, adjusting thresholds, improving prompts, or changing the review process. Professional tuning changes one thing at a time when possible so the team can learn what helped.
Deployment readiness asks whether the system should be used with real people. A model that is interesting in practice may still be unsafe for production if errors are costly, logging is missing, privacy is unclear, or no one owns review.
After launch, the world changes. User language changes. Products change. Data changes. Monitoring helps catch drift before it damages trust.
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