As companies go multi-cloud, a new set of tools simplify IT management and application development. By Ben Lorica and Ion Stoica. [This post originally appeared on the Anyscale blog.] In a previous post we examined current serverless computing offerings and described why Ray is an ideal substrate for general purpose computing platforms. While serverless hasContinue reading “The Emergence of Multi-cloud Native Applications and Platforms”
Why Ray is poised to play a central role in future serverless offerings. By Ben Lorica, Eric Liang and Ion Stoica.
Analysts remain front and center in BI 3.0. By Assaf Araki and Ben Lorica.
Ray Serve simplifies the integration of classic web serving and model serving. By Ben Lorica and Ion Stoica.
I just came across a new paper that analyzes results from the 2018 Annual Business Survey, a study conducted by the US Census Bureau in partnership with the National Center for Science and Engineering Statistics. This survey was conducted over the second of half of 2018, and while the data is over a year old,Continue reading “One Simple Chart: Technology Adoption in the U.S.”
Ray is the future of the serverless API: Learn all about it at Ray Summit, a FREE virtual conference showcasing best practices, real-world case studies, and the latest in AI and scalable systems built on Ray. Join Dave Patterson, Ion Stoica, Manuela Veloso, Azalia Mirhoseini, Michael Jordan, Zoubin Ghahramani, Oriol Vinyals, and many other speakersContinue reading “One Simple Chart: job postings that mention “serverless””
ML platform designers need to meet current challenges and plan for future workloads. By Ben Lorica and Ion Stoica. [This post originally appeared on the Anyscale blog.] As machine learning gains a foothold in more and more companies, teams are struggling with the intricacies of managing the machine learning lifecycle. The typical starting point isContinue reading “Five Key Features for a Machine Learning Platform”
As companies increase their investments in artificial intelligence (AI), there is growing pressure on developers and engineers to deploy AI projects more quickly and at greater scale across the enterprise.