anyscale
Anyscale Introduces Multi-Tenant Serve Applications with Containerized Runtime Environments
Anyscale unveils multi-tenant serve applications using runtime environments as containers, enhancing efficiency and reducing operational complexity.
Anyscale Introduces New Replica Compaction to Optimize Resource Usage
Anyscale launches Replica Compaction to address resource fragmentation, enhancing resource utilization and reducing costs for Ray Serve deployments.
Anyscale and MongoDB Collaborate to Enhance Multi-Modal Search
Anyscale and MongoDB join forces to revamp multi-modal search, offering scalable solutions and improved search relevance for e-commerce platforms.
Anyscale Explores Direct Preference Optimization Using Synthetic Data
Anyscale's latest blog post delves into Direct Preference Optimization (DPO) with synthetic data, highlighting its methodology and applications in tuning language models.
Keerti Melkote Takes Helm as CEO of Anyscale Amidst Rapid Growth
Anyscale has appointed Keerti Melkote as CEO, following significant growth driven by the adoption of its Ray platform in the AI sector.
Anyscale and Astronomer Collaborate to Enhance Scalable Machine Learning
Anyscale partners with Astronomer to streamline machine learning workflows using Apache Airflow and Ray, enhancing scalability and efficiency for data teams.
Anyscale Recognized as 2024 Cool Vendor in AI Engineering by Gartner
Anyscale has been named a 2024 Cool Vendor in AI Engineering by Gartner, highlighting its role as a leading AI platform overcoming AI complexity challenges.
Anyscale Unveils Deployment of DeepSeek R1 for AI Scalability
Anyscale introduces the deployment of DeepSeek R1, offering enhanced control, scalability, and transparency for AI models across various infrastructures without vendor constraints.
Anyscale Introduces Comprehensive Ray Training Programs
Anyscale launches new training options for Ray, including free eLearning and instructor-led courses, catering to AI/ML engineers seeking to scale AI applications effectively.
Google Cloud and Anyscale Collaborate to Enhance AI Development with RayTurbo Integration
Google Cloud and Anyscale have partnered to integrate RayTurbo with Google Kubernetes Engine, enhancing AI application development and scaling. This collaboration aims to simplify and optimize AI workloads.
Anyscale Enhances Ray Data with Joins and Hash-Shuffle for Improved Performance
Anyscale introduces a hash-based shuffle backend in Ray Data, enhancing joins and performance for repartitioning and aggregations. Discover the advancements in the Ray 2.46 release.
Anyscale Launches Ray Train and Ray Data Dashboards for Enhanced Observability
Anyscale introduces Ray Train and Ray Data Dashboards, offering new features for improved observability and performance optimization in distributed AI model training and data pipelines.
Anyscale Expands AI Compute Capabilities with New Multi-Cloud and AKS Support
Anyscale introduces enhanced AI compute solutions with support for Azure Kubernetes Service, Global Resource Scheduler, and upcoming multi-deployment management, optimizing resource utilization and scaling across cloud platforms.
RayTurbo Data Enhancements Boost Processing Speed by Fivefold
Anyscale's RayTurbo Data introduces significant improvements, offering up to 5x faster data processing. Key features include job-level checkpointing, vectorized aggregations, and optimized pipeline rules.
Enhancing RAG Pipelines with Ray and Anyscale for Scalable AI Solutions
Explore how Ray and Anyscale empower developers to build scalable Retrieval-Augmented Generation (RAG) pipelines, reducing hallucinations and integrating new information without retraining models.