In a previous article, I explored why many leading companies are building custom AI platforms, even when a range of off-the-shelf options exists. The reasons vary from seeking greater adaptability to establishing market differentiation through tailored AI capabilities. As more businesses push toward these bespoke platforms, scaling becomes a central challenge.
Few technologies match the power of Ray when it comes to scalable, distributed AI infrastructure. At Ray Summit this year, practitioners will have the chance to learn from the best teams in the business, particularly those pioneering new ways of scaling AI. The summit will showcase how companies like Pinterest, Reddit, and Spotify leverage Ray to scale their machine learning operations.
Ray is designed to tackle the unique challenges posed by modern AI workloads. Its flexible framework allows for seamless integration of various AI and machine learning libraries, making it an ideal choice for organizations looking to unify their AI infrastructure. Ray’s appeal lies in its ability to abstract away the complexities of distributed systems, allowing teams to streamline their development processes and reduce the time-to-market for AI-powered solutions.
Distributed Computing and Scalability with Ray
Ray’s ability to support scalable, distributed systems is particularly vital for machine learning pipelines, where computation and data must be efficiently managed across clusters of machines. Companies like Pinterest and Reddit are leveraging Ray to distribute data loading and model training, drastically reducing the time to deploy recommender systems and large-scale machine learning models.
For instance, Netflix is evolving its machine learning platform to handle generative AI workloads, using Ray to distribute the computational load. Spotify has scaled its large language model (LLM) training across Kubernetes clusters, tapping into Ray’s ability to manage distributed compute resources seamlessly. These examples illustrate Ray’s role in enabling teams to build systems that can scale horizontally (across machines) and vertically (handling more complex models).
A key benefit of Ray’s architecture is that it benefits many different applications, optimizing performance for diverse infrastructures. Ford, for example, uses Ray to process Lidar data in autonomous vehicle systems, while Zoox employs Ray for inference in deep learning models for self-driving cars. This flexibility allows teams to focus on building AI applications without being bogged down by infrastructure challenges.
Model Serving and Machine Learning Pipelines
Ray Serve, a scalable model serving library built on Ray, simplifies the deployment of machine learning models into production environments. Ray Serve handles high availability and low-latency inference, making it an essential tool for production-grade AI systems.
Take Klaviyo, for example, which built a self-service model serving platform using Ray Serve. By automating and optimizing the serving of models, they reduced the time to deployment, ensuring faster iteration cycles. Similarly, Hinge used Ray to minimize the time to production for machine learning models, enabling their engineering teams to deploy solutions faster without sacrificing performance.
For companies dealing with large volumes of data or more complex models, such as Netflix’s LLM-based recommendation systems or Spotify’s batch inference pipelines, Ray’s distributed framework provides the necessary infrastructure to scale training and serving. By integrating Ray Serve with NVIDIA’s Triton model serving, teams can combine Ray’s scalability with in-process model optimizations, resulting in significant performance improvements.
Efficient Inference with vLLM
While Ray handles distributed workloads and model serving, vLLM focuses on making large language model (LLM) inference more efficient. vLLM, a specialized library for serving LLMs, addresses some of the biggest bottlenecks in LLM deployment: high memory usage and inference latency. Ray Summit has a dedicated vLLM track at the conference that delves deep into the intricacies of this fast and user-friendly library.
One of vLLM’s key innovations is PagedAttention, an algorithm that divides attention keys and values into smaller chunks, reducing the memory footprint and allowing for higher throughput during inference. This is particularly important for teams that are working with resource-constrained environments or needing to optimize for low-latency applications.
In the latest release of vLLM, performance improvements of 1.8 to 2.7 times have been achieved, mainly due to continuous batching and optimized CUDA kernels. This enables applications to handle larger workloads without sacrificing speed or performance. For example, Uber has successfully integrated vLLM with Ray to optimize batch predictions in their AI-driven recommendation systems, ensuring fast, cost-efficient inference. Apple’s talk at Ray Summit will highlight how combining Ray and vLLM with LLMs optimizes data classification processes, leading to smarter, more efficient governance solutions.
Practical Applications Across Industries
Both Ray and vLLM have broad applications across industries. Ray is powering AI models in healthcare that support diagnostic tools, such as Rad AI’s LLM training for radiology workflows. In financial services, firms like Bridgewater deploy machine learning models at scale, leveraging Ray to optimize workflows in quantitative research. For retail giants like Shopify and eBay, Ray is scaling recommendation systems and batch inference to personalize user experiences in real-time.
Combining Ray’s distributed architecture with vLLM’s optimized inference provides a powerful toolkit for AI teams working across sectors. Whether scaling LLMs for content generation, processing autonomous vehicle data, or managing real-time recommendations, these tools enable efficient AI deployment at scale.
Conclusion
As AI continues to evolve, Ray Summit is a critical opportunity to explore the latest advancements in AI and machine learning. Ray and vLLM stand at the forefront of this transformation, offering teams the tools to scale AI infrastructure efficiently. Whether you’re working in the cloud or at the edge, the insights from Ray Summit will provide the strategies needed to scale AI effectively and drive innovation in your industry.
See you at Ray Summit! Register with code AnyscaleBen15 to save 15% on this can’t-miss AI event.
If you enjoyed this post please support our work by encouraging your friends and colleagues to subscribe to our newsletter: