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Learning path: Get started with Watson Machine Learning Accelerator

Topic Type
An introduction to Watson Machine Learning Accelerator Article
Accelerate your deep learning and machine learning Article + notebook
Elastic Distributed Training in Watson Machine Learning Accelerator Article + notebook
Expedite retail price prediction with Watson Machine Learning Accelerator hyperparameter optimization Tutorial
Drive higher GPU utilization and throughput Tutorial
Classify images with Watson Machine Learning Accelerator (Optional) Article + notebook
Deliver faster deep learning results on GPUs in Cloud Pak for Data Video + notebook
Balance deep learning jobs with Elastic Distributed Training Video

This learning path is designed for anyone interested in quickly getting up to speed with IBM Watson Machine Learning Accelerator. The learning path consists of articles that explain what Watson Machine Learning Accelerator is as well as its various features, and notebooks to help you work with it and get hands-on experience.

To get started, click on a card below, or see the previous table for a complete list of topics covered.

An introduction to Watson Machine Learning Accelerator


Learn about:

  • Overview of Watson Machine Learning Accelerator
  • Key capabilities

Accelerate your deep learning and machine learning


Learn about:

  • Configuring the link between IBM Watson Studio in IBM Cloud Pak for Data and IBM WML Accelerator
  • Methods for submitting deep learning workload from IBM Watson Studio to IBM WML Accelerator, including automating model hyperparameter search through the WML Accelerator Rest API

Elastic Distributed Training in Watson Machine Learning Accelerator


Learn about:

  • Overview of EDT
  • Use a Notebok to:
    • Make changes to your code
    • Make your data set available
    • Set up an API end point and log on
    • Submit a job through an API
    • Monitor a running job
    • Retrieve output and saved models
    • Debug any issues

Expedite retail price prediction with Watson Machine Learning Accelerator hyperparameter optimization


Learn about:

  • IBM Watson Machine Learning Accelerator’s ease of use and high resource efficiency for distributed machine learning jobs as well as the power of the HPT process

Drive higher GPU utilization and throughput


Learn about:

  • Using the Watson Machine Learning Accelerator advanced scheduler to accelerate multiple deep learning training jobs by batching and running four jobs on a single GPU

Classify images with Watson Machine Learning Accelerator (Optional)


Learn about:

  • Data science experience in Watson Machine Learning Accelerator
  • Deep learning lifecycle management with robust tooling starting with data ingestion, hyper-parameter tuning, model training and inference

Deliver faster deep learning results on GPUs


Learn about:

  • How a deep learning workload is run on a CPU
  • How much faster the same deep learning workload runs on a GPU

Balance deep learning jobs with Elastic Distributed Training


Learn about:

  • How to use elastic distributed training to balance IBM Watson Machine Learning Accelerator jobs in IBM Cloud Pak for Data