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Automate, Analyze, and AI Feedback

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Automate, Analyze, and AI Feedback

This course is part of Agentic AI Performance & Reliability Specialization

Instructor: LearningMate

Included with

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2 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

3 hours to complete

Flexible schedule

Learn at your own pace

2 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

3 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Design automated feedback loops to capture human insights, analyze model performance, and retrain AI to meet specific operational goals.

Skills you'll gain

Tools you'll learn

Model Deployment

Details to know

Shareable certificate

Add to your LinkedIn profile

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Build your subject-matter expertise

This course is part of the Agentic AI Performance & Reliability Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 2 modules in this course

Automate, Analyze, and AI Feedback is an intermediate-level course for MLOps professionals and data scientists who need to build AI systems that do not just launch, but last. In the real world, even the best models degrade over time due to model drift. This course teaches you to combat this by creating automated, self-improving systems that learn from operational experience.

You will learn to design and deploy Human-in-the-Loop (HITL) pipelines that identify low-confidence predictions, route them for expert human review, and schedule automated retraining with the new, high-quality data. Moving beyond simple accuracy, you will master advanced model evaluation techniques. Through hands-on labs, you will generate and analyze Precision-Recall (PR) curves, apply resampling methods to ensure your model generalizes well, and select the optimal decision threshold that balances competing business objectives, like maximizing recall while minimizing false alarms. This course will equip you to build resilient MLOps systems that turn human expertise into a continuous source of model improvement.

Module details

This module introduces the core principles of building dynamic, self-improving AI systems. Learners will learn why static models fail over time and how to design automated feedback loops that capture human expertise to drive continuous model improvement. This module covers the architecture of a human-in-the-loop (HITL) system, from identifying anomalous predictions to routing them for human review and scheduling automated retraining.

What's included

3 videos1 reading2 assignments

3 videosTotal 18 minutes

Model Drift and Technical Debt: A Definition7 minutes
Visualizing the HITL Architecture5 minutes
How to Build a Feedback Endpoint with FastAPI5 minutes

1 readingTotal 7 minutes

Core Components of a HITL System7 minutes

2 assignmentsTotal 30 minutes

Hands-on Learning (HOL): Designing a Human Feedback System20 minutes
Knowledge Check: Human-in-the-Loop Learning Systems10 minutes

This module shifts the focus from collecting feedback to rigorously analyzing model performance. You will learn to move beyond simple accuracy and use advanced diagnostic tools like Precision-Recall (PR) curves and resampling to understand a model's true behavior. The module culminates in selecting an optimal decision threshold that balances business needs, such as maximizing the detection of critical events while minimizing false alarms.