Get started with Autopilot

• Direct Marketing with Amazon SageMaker Autopilot
  • Contents
  • Introduction
  • Prerequisites
  • Downloading the dataset
  • Upload the dataset to Amazon S3
    • Reserve some data for calling batch inference on the model
    • Upload the dataset to Amazon S3
  • Setting up the SageMaker Autopilot Job
  • Launching the SageMaker Autopilot Job
  • Tracking SageMaker Autopilot job progress
  • Results
    • Perform batch inference using the best candidate
    • View other candidates explored by SageMaker Autopilot
    • Candidate Generation Notebook
    • Data Exploration Notebook
  • Cleanup
• Regression with Amazon SageMaker Autopilot (Parquet input)
• Portfolio Churn Prediction with Amazon SageMaker AutoPilot and Neo4j
  • Deploy Neo4j
  • Using the Neo4j API
  • Load Data into Neo4j
  • Graph Data Science
  • SageMaker Connection
  • Upload to Amazon S3
  • Setting up the SageMaker AutoPilot Job
  • Launching the SageMaker AutoPilot Job
  • Tracking SageMaker AutoPilot job progress
  • Results
  • Batch Inference
  • View All Candidates
  • Candidate Generation Notebook
  • Data Exploration Notebook
  • Cleanup
  • Conclusion
• Top Candidates Customer Churn Prediction with Amazon SageMaker Autopilot and Batch Transform (Python SDK)
  • Contents
  • Introduction
  • Setup
  • Data
    • Inspect your Dataset
    • Reserve some data for calling inference on the model
  • Setting up the SageMaker Autopilot Job
    • Launching the SageMaker Autopilot Job
    • Tracking SageMaker Autopilot Job Progress
  • Describing the SageMaker Autopilot Job Results
    • Check Top Candidates
  • Evaluate Top Candidates
    • Upload Data for Transform Jobs
    • Customize the Inference Response
    • Create the Models and Tranform Estimators
    • Start the Transform Jobs
    • Evaluate the Inference Results
    • Deploy the Selected Candidate
  • Cleanup
• Housing Price Prediction with Amazon SageMaker Autopilot
  • Contents
  • Introduction
  • Setup
  • Prepare Training Data
  • Setting up the SageMaker Autopilot Job
    • Launching the SageMaker Autopilot Job
    • Tracking SageMaker Autopilot job progress
  • Results
  • Evaluate Model Using Test Dataset
    • Set up Transform Job
    • Launch Transform Job
      • Track Transform Job Status
    • Evaluate the Inference Results
  • Cleanup
• Customer Churn Prediction with Amazon SageMaker Autopilot
  • Contents
  • Introduction
  • Setup
  • Data
    • Upload the dataset to S3
    • Reserve some data for calling inference on the model
  • Setting up the SageMaker Autopilot Job
    • Launching the SageMaker Autopilot Job
    • Tracking SageMaker Autopilot job progress
  • Results
  • Host
    • Evaluate
  • Cleanup

Feature selection

• Bringing your own data processing code to SageMaker Autopilot
  • Table of contents
  • Setup
  • Generate dataset
  • Upload the data for training
  • Feature Selection
  • Prepare Feature Selection Script
  • Create SageMaker Scikit Estimator for feature selection
  • Batch transform our training data
  • Autopilot
    • First we add column names to transferred data
    • Set up and kick off autopilot job
    • Tracking SageMaker Autopilot job progress
    • Results
  • Serial Inference Pipeline that combines feature selection and autopilot
  • Set up the inference pipeline
  • Make a request to our pipeline endpoint
  • Delete Endpoint

Model explainability

• Explaining Autopilot Models
  • Table of Contents
  • Introduction
    • SHAP
  • Setup
    • Create an inference endpoint
    • Wrap Autopilot’s endpoint with an estimator class.
    • Data
    • Background data
    • Setup KernelExplainer
  • Local explanation with KernelExplainer
  • KernelExplainer computation cost
  • Global explanation with KernelExplainer
  • Conclusion