Building Production AI Applications with Ray Serve

Productionizing modern machine learning workloads is challenging. Not only do you need to train and optimize your models, but also find a way to serve them efficiently without too much operational cost. Ray Serve solves these complex requirements to enable you to go to production safely and at low cost: you can flexibly scale and coordinate multiple models, deploy and upgrade safely, and maximize your hardware utilization with minimal management overhead.

This talk will demonstrate Ray Serve’s production-ready capabilities, including a demo of serving an ML-powered application using Ray Serve on the Anyscale platform. Some highlights include improvements around scalability, high availability, fault tolerance, and observability.

Takeaways:

• Learn about patterns of production ML serving and how Ray Serve is tailored to solve them.

• Hear how users in the community are using Ray Serve in production to lower their ML inference costs.

• Watch a real time demo of how to serve an ML application using Ray Serve on the Anyscale platform. This will highlight recent improvements around observability, autoscaling, and cost savings.

Find the slide deck here: https://drive.google.com/file/d/1NgBv...


About Anyscale
---
Anyscale is the AI Application Platform for developing, running, and scaling AI.

https://www.anyscale.com/

If you're interested in a managed Ray service, check out:
https://www.anyscale.com/signup/

About Ray
---
Ray is the most popular open source framework for scaling and productionizing AI workloads. From Generative AI and LLMs to computer vision, Ray powers the world’s most ambitious AI workloads.
https://docs.ray.io/en/latest/


#llm #machinelearning #ray #deeplearning #distributedsystems #python #genai