AWS Marketplace

Publish algorithm on the AWS Marketplace

Create your algorithm and model package

• Building your own container as Algorithm / Model Package
  • When should I build my own algorithm container?
  • Permissions
  • The example
  • The presentation
• Part 1: Packaging and Uploading your Algorithm for use with Amazon SageMaker
  • Testing your algorithm on your local machine or on an Amazon SageMaker notebook instance
• Part 2: Training, Batch Inference and Hosting your Algorithm in Amazon SageMaker
  • Set up the environment
  • Create the session
  • Upload the data for training
  • Create an estimator and fit the model
  • Batch Transform Job
    • Batch Transform Input Preparation
    • Run Batch Transform
      • Inspect the Batch Transform Output in S3
  • Deploy the model
    • Choose some data and use it for a prediction
    • Cleanup Endpoint
• Part 3 - Package your resources as an Amazon SageMaker Algorithm
  • Algorithm Definition
  • Putting it all together
    • Describe the algorithm
• Part 4 - Package your resources as an Amazon SageMaker ModelPackage
  • Model Package Definition
  • Putting it all together
  • Debugging Creation Issues
  • List on AWS Marketplace

Curate your sample notebook

Templates

• Train, tune, and deploy a custom ML model using For Seller to update: Title_of_your_ML Algorithm Algorithm from AWS Marketplace
  • Pre-requisites:
  • Contents:
  • Usage instructions
    • 1. Subscribe to the algorithm
    • 2. Prepare dataset
  • A. Dataset format expected by the algorithm
  • B. Configure and visualize train and test dataset
  • C. Upload datasets to Amazon S3
• 3: Train a machine learning model
  • A. Deploy trained model
  • B. Create input payload
  • C. Perform real-time inference
  • D. Visualize output
  • E. Calculate relevant metrics
  • F. Delete the endpoint
    • 5: Tune your model! (optional)
  • A. Tuning Guidelines
  • B. Define Tuning configuration
  • C. Run a model tuning job
    • 6. Perform Batch inference
    • 7. Clean-up
  • A. Delete the model
  • B. Unsubscribe to the listing (optional)
• Deploy For Seller to update: Title_of_your_ML Model Model Package from AWS Marketplace
  • Pre-requisites:
  • Contents:
  • Usage instructions
    • 1. Subscribe to the model package
    • 2. Create an endpoint and perform real-time inference
  • A. Create an endpoint
  • B. Create input payload
  • C. Perform real-time inference
  • D. Visualize output
  • E. Delete the endpoint
    • 3. Perform batch inference
    • 4. Clean-up
  • A. Delete the model
  • B. Unsubscribe to the listing (optional)

Use AWS Marketplace algorithms

• AWS Marketplace Product Usage Demonstration - Algorithms
  • Using Algorithm ARN with Amazon SageMaker APIs
  • Overall flow diagram
  • Compatibility
  • Set up the environment
    • Create the session
  • Upload the data for training
  • Creating Training Job using Algorithm ARN
  • Run Training Job
  • Automated Model Tuning (optional)
  • Batch Transform Job
    • Batch Transform Input Preparation
      • Inspect the Batch Transform Output in S3
  • Live Inference Endpoint
    • Choose some data and use it for a prediction
    • Cleanup the endpoint

Autogluon

• AutoGluon-Tabular in AWS Marketplace
  • Contents:
  • Step 1: Subscribe to AutoML algorithm from AWS Marketplace
  • Step 2 : Set up environment
  • Step 3: Get the data
    • Check the data
  • Step 4: Train a model
  • Step 5: Deploy the model and perform a real-time inference
    • Deploy a remote endpoint
    • Predict on unlabeled test data
    • Predict on data that includes label column
    • Check that classification performance metrics match evaluation printed to endpoint logs as expected
  • Step 6: Use Batch Transform
  • Step 7: Clean-up

AutoML

• Goal: Train a model using AutoML functionality!
  • Contents:
    • Compatibility
    • Usage instructions
  • Step 1: Subscribe to AutoML algorithm from AWS Marketplace
  • Step 2 : Set up environment
  • Step 3: Prepare and upload data
    • Background - The Iris dataset
  • Step 4: Train a model
  • Step 5: Deploy the model and perform a real-time inference
  • Step 6: Clean-up

Use AWS Marketplace model packages

• AWS Marketplace Product Usage Demonstration - Model Packages
  • Using Model Package ARN with Amazon SageMaker APIs
  • Overall flow diagram
  • Compatibility
  • Set up the environment
    • Create the session
  • Create Model
  • Batch Transform Job
    • Batch Transform Input Preparation
      • Inspect the Batch Transform Output in S3
  • Live Inference Endpoint
    • Choose some data and use it for a prediction
    • Cleanup endpoint

Auto insurance

• Goal: Automate Auto Insurance Claim Processing Using Pre-trained Models
  • Pre-requisites:
  • Set up environment and view a sample image
• Step 1: Deploy Vehicle Damage Inspection model
  • Step 1.1: Deploy the model for performing real-time inference.
  • Step 1.2: Perform a prediction on Amazon Sagemaker Endpoint created.
• Step 2: Deploy the Vehicle recognition model.
  • Step 2.1: Deploy the model for performing real-time inference.
  • Step 2.2: Perform real-time inference on the model.
  • Step 2.3: Store the precise car image for further processing
• Step 3. Extract labels from the picture (optional)
• Step 4: View all outputs
  • 5. Cleanup

Financial transaction processing

• Extracting insights from your credit card statements
  • Overview:
  • Contents:
  • Usage instructions
    • Step 1: Perform preliminary analysis on the dataset
  • Step 1.1 Load and View the dataset
  • Step 1.2 Identify Transaction date
  • Step 1.3 Identify subscriptions
    • Step 2: Use an ML model to identify potential merchants and locations for each transaction
  • Step 2.1: Deploy the model
  • Step 2.2: Populate candidate merchants and locations in dataframe
    • Step 3: Identify merchant name from transaction log
  • Step 3.1: Identify merchant name
  • Step 3.2: Visualize expenses
    • Step 4: Identify state and city for each transaction log entry
  • Step 4.1: Populate state in which transaction took place
  • Step 4.2: Populate city and country
    • Step 5: Next steps and cleanup

Improving industrial workplace safety

• Demonstrating Industrial Workplace Safety using Pre-trained Machine Learning Models
  • Introduction
  • Pre-requisites:
• Step 1: Set up environment and view sample images
  • Step 1.1: Set up environment
  • Step 1.2: View sample images
  • Step 1.2.1: View construction site image
  • Step 1.2.2: View an image with a worker and a person at a workplace
  • Step 1.2.3: View an image with an excavator and a truck at work
• Step 2: Deploy construction worker detection model
  • Step 2.1: Deploy the model for performing real-time inference.
  • Step 2.2: Perform a prediction (Test 1)
  • Step 2.3: Perform a prediction (Test 2)
• Step 3: Deploy the hard-hat detection model.
  • Step 3.1: Deploy the model for performing real-time inference.
  • Step 3.2: Perform real-time inference on the model.
• Step 4. Deploy the Personal Protective Equipment (PPE) detection model
  • Step 4.1: Deploy the model for performing real-time inference.
  • Step 4.2: Perform real-time inference on the model.
• Step 5. Deploy the Construction Machines detection model
  • Step 5.1: Deploy the model for performing real-time inference.
  • Step 5.2: Perform real-time inference on the model (Test 1).
  • Step 5.3: Perform real-time inference on the model (Test 2).
• Step 6. Generate actionable insights on video input
• Step 7. Explore other relevant models!
• Step 8. Cleanup

Generic sample notebook

• Deploy and perform inference on ML Model packages from AWS Marketplace.
  • Pre-requisites:
  • Additional Resources:
  • Contents:
  • Usage instructions
    • 1. Subscribe to the model package
  • A. Identify compatible instance-type
  • B. Identify content_type
  • C. Specify model-package-arn
    • 2. Create an Endpoint and perform real-time inference.
  • A. Create an Endpoint
  • B. Create input payload
  • View the file you just downloaded
  • Step B.1 Pre-process the data (Optional for some models)
  • C. Perform Real-time inference
  • D. Visualize output
  • E. Delete the endpoint
    • 3. Perform Batch inference
  • C. Visualize output
    • 4. Delete the model
    • 5. Cleanup