This course was created with the
course builder. Create your online course today.
Start now
Create your course
with
Autoplay
Autocomplete
Previous Lesson
Complete and Continue
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)
5.4 Automatically Scaling Deployed Models to Meet Demand
Lesson content locked
If you're already enrolled,
you'll need to login
.
Enroll in Course to Unlock