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Build, Deploy, and Monitor ML Models with Amazon SageMaker
1. Introduction to SageMaker
1.1 Challenges of Running Production Machine Learning Systems (2:58)
1.2 What is Amazon SageMaker? (1:56)
1.3 SageMaker Architecture Overview (4:20)
1.4 Course Breakdown (2:28)
2. Setting Up Your SageMaker Studio Development Environment
2.1 Introduction to SageMaker Studio (3:08)
2.2 Creating a Studio Instance from the AWS Console (3:50)
2.3 Walk-thru of Studio and Creating a Jupyter Notebook (5:45)
2.4 Connect to a Git Repository from Studio (1:49)
2.5 Walk-thru of Course Codebase (8:25)
3. Training Models on Amazon SageMaker
3.1 Introduction to Model Training in SageMaker (4:56)
3.2 Training an XGBoost model using Built-in Algorithms (15:57)
3.3 Training a scikit-learn model using Pre-built Docker Images and Custom Code (12:39)
3.4 Installing Custom python Requirements (7:25)
3.5 Preprocessing Data with SageMaker Preprocessor (8:28)
4. Experiment Management on SageMaker
4.1 Introduction to SageMaker Experiments (1:46)
4.2 Understanding the SageMaker Experiments SDK (5:45)
4.3 Running a single Trial Experiment with Tensorflow (13:23)
4.4 Distributed Hyperparameter Optimization with PyTorch (12:55)
4.5 Tracking Lineage: Finding the Best Model (7:13)
5. Deploying Models on Amazon SageMaker
5.1 Introducing Model Deployment (0:58)
5.2 Performing Batch Inference using SageMaker Batch Transform (15:51)
5.3 Deploying Models as Persistent Endpoints for Online Inference (7:25)
5.4 Automatically Scaling Deployed Models to Meet Demand (7:06)
5.5 Cleaning Up Deployed Models (1:40)
6. Model Monitoring
6.1 Introducing Model Monitoring (1:05)
6.2 Capturing Live Data with a Deployed Model Endpoint (12:27)
6.3 Generating Constraints and Suggestions from a Baseline dataset (10:40)
6.4 Creating a Monitoring Schedule (5:25)
6.5 Visualizing Drift by Comparing Data Distributions (13:25)
7. Conclusion
7.1 Course Wrap-Up (3:03)
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6.2 Capturing Live Data with a Deployed Model Endpoint
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