AI Engineering: Build & Deploy ML Models
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Learning Algorithm Foundations
How Machines Learn from Labeled Data
Preview
Regression and Classification in Practice
Clustering and Dimensionality Reduction Without Labels
3 lessons
Feature Engineering and Model Selection
Turning Raw Data into Useful Features
Choosing the Right Model for the Problem
Hyperparameter Tuning with Search Strategies
3 lessons
Evaluating Model Performance
Accuracy and Why It Misleads
Precision Recall and the F1 Score
ROC Curves and AUC for Threshold Decisions
3 lessons
Serving Models as REST APIs
From Trained Model to Prediction Service
Building a FastAPI Inference Service
Flask Deployments and Input Validation
3 lessons
Packaging and Cloud Deployment
Containerizing a Model with Docker
Deploying to AWS SageMaker Endpoints
Scaling Latency and Cost Trade-offs
3 lessons
Monitoring and Retraining
Detecting Data and Concept Drift
Building a Retraining Pipeline
Closing the Loop with Automated Evaluation
3 lessons
Evaluating Model Performance
ROC Curves and AUC for Threshold Decisions
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