Webinar
How AI Teams Ease Scaling Popular AI/ML Use Cases
Wednesday, December 14 11 AM PSTWatch 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:
- Scalable demand forecasting and the use of distributed time-series partitions
- Scalable reinforcement learning including use cases in gaming, trading, portfolio optimization, robotics, traffic optimization and more
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.
Speakers
Christy Bergman
Developer Advocate, Anyscale, Anyscale
Phi Nguyen
GTM Tech Lead, Anyscale, Anyscale