Simplify Building, Scaling, Tracking and Monitoring Your AI/ML Models

Hear about:

• Instant ML workload scaling from a laptop to the cloud
  Understand how to scale ML workloads from your laptop to the cloud with no code changes. With a single script prepare data, tune, train and scale your workloads.

• Faster developer velocity and experimentation
  See how to speed model development and iterations without scaling complexity. Visualize, optimize, collaborate and standardize models and data pipelines.

• Reproducible and shareable ML workflows
  Learn how to track and ease repeatability of a model architecture, hyper-parameters, weights, model predictions, GPU usage, git commits, and even datasets, to ease experimentation and collaboration.

• Large-scale training and tuning
  Hear how organizations are benefitting from embarrassingly parallel experiments for use cases including recommendation systems to demand forecasting, logistics and on-time delivery optimization, drug discovery, game balancing and more.

“With very little effort, you can use Ray to write a distributed application and scale it in the cluster - because Ray has very simple and intuitive APIs. Even in a single node, it’s much easier to write a multi-processing application using Ray than using the python multiprocessing library” - Intel 

"Weight and Biases is a key piece of our fast-paced, cutting-edge, large-scale research workflow: great flexibility, performance, and user experience."  - Toyota