Watch this on-demand webinar to see examples of how organizations successfully develop and deploy large-scale machine learning applications and address demand forecasting, NLP, scalable reinforcement learning, recommendation systems, estimated on-time arrival, dynamic pricing and more.
See examples of how organizations successfully develop and deploy large-scale machine learning applications using Ray, the Python-native unified distributed framework for AI/ML scaling:
GPU-intensive natural language processing (NLP) for better content interpretation and understanding
Scalable deep learning and distributed training on large datasets for dynamic pricing, estimating arrival times (ETA) and recommendation systems
With Ray and Anyscale, developers can accelerate model development and effortlessly deploy and scale models in production on large data sets - without the need for complex infrastructure engineering.
Learn about use cases including:
How Instacart uses Ray to optimize routing of deliveries in real time.
How Anatasia and others use Ray to scale time-series forecasting for thousands of different partitions..
How leading game game studioRiot Games and J.P. Morgan use Ray to scale
reinforcement learning with Ray’s RLlib library which provides over 25 scalable RL algorithms
How Shopify, Entrupy, Dendra and Riccardo incorporate unstructured data and text documents into large-scale inference pipelines.
Christy is a Developer Advocate at Anyscale. Her work involves figuring out how to parallelize different AI algorithms and creating demos and tutorials on how to use Ray and Anyscale. Before that, she was a Senior AI/ML Specialist Solutions Architect at AWS and Data Scientist at several other companies. In her spare time, she enjoys hiking and bird watching.
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.