Watch this on-demand webinar by Anyscale, the company behind Ray, the unified framework for scalable computing, and Weights & Biases, the leading developer-first MLOps platform. The webinar discusses how Ray and Weights & Biases are used together by developers and AI teams to ease AI/ML development, experimentation, experiment tracking, model scaling and model management.
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
Anish brings his data scientist background with SAP to his MLOps engineering role at Weights & Biases. He strives to approach data like it’s a science bringing the human component back into models and algorithms trained by machines. Anish also works to remove unwanted bias and control unplanned variance to the art of programming.
Phi has been working with Fortune 500 customers in Retail, CPG, HCLS, Financial services and startups to accelerate their machine learning practices. This includes a wide range of engagements such as helping teams organize and build a center of excellence for ML, MLOps processes and automation, ML use cases development and feasibility to providing cloud best practices combining Ray and public cloud such as AWS and GCP or open source projects running on Kubernetes.
Tricia is currently a Product Manager at Anyscale. Before that, she spent some time at Google as a Product Manager and LinkedIn as a Software Engineer. She holds a BS degree from UC Berkeley in Electrical Engineering and Computer Science. In her free time, she loves taking her dog on alpine lake hikes!