Anyscale updates streamline cloud scaling for AI and ML developers

Anyscale updates streamline cloud scaling for AI and ML developers

Distributed computing startup Anyscale Inc. told AWS re:Invent today that it is introducing a number of updates to its platform aimed at making it easier to develop and scale the load. artificial intelligence and machine learning working tools for developers.

Anyscale is the company behind the open-source Python framework Ray, which is used to run distributed computing projects. Ray includes both a universal serverless computing application programming interface and an extensive ecosystem of libraries. They allow developers to build scalable applications that can run on multicloud platforms without having to worry about the underlying infrastructure.

One of Ray’s main advantages is that it eliminates the need for internally distributed IT expertise.

The Anyscale cloud platform, on the other hand, is a managed version of Ray aimed at making it more accessible. The Ray platform requires a fair amount of expertise that usually only a few high-level developers and IT specialists possess. Anyscale’s platform, which runs on AWS, solves the difficulty of taking an AI prototype built on a laptop and scaling that model on hundreds of machines in the cloud.

New features announced at re:Invent include the early access availability of the new Anyscale Workspaces environment, which is meant to provide a unified and more seamless experience for developers when scaling a machine’s machine learning workloads portable to the cloud, without making a significant difference. code changes. Developers now have a single environment to build machine learning workloads and move them to production, Anyscale said.

One of the main benefits of Anyscale Workspaces is that it allows developers to use the same set of familiar tools throughout, including VS Code and Jupyter, while reducing context switching when they bring new cloud-scale machine learning models.

In a second update, the Anyscale platform gets the ability to start clusters up to five times faster than with the open source Ray platform. Developers can therefore accelerate the iteration, experimentation and deployment of machine learning models, Anyscale said. Finally, Anyscale adds new job scheduling automation features. With this, developers now have a native way to schedule jobs and integrate them with third-party orchestration tools like Airflow and Prefect, with auto-scaling, alerts, auto-retries and other features available.

The updates aim to make machine learning developers faster and more productive, and early testers say they’re having the desired effect.

“In the time it took to run our original workload – one week – we were able to effortlessly migrate all of our Python workloads to the Anyscale platform, quickly refine jobs for release scale and move to full-scale production effortlessly,” said Jake Carter director of data, machine learning and technology at Biolexis Therapeutics LLC. “It was remarkable and literally saved us a week from start to finish.”

AWS Vice President and Global Head of Startups Howard Wright said enabling innovations like the Anyscale platform is exactly what the AWS Cloud was built for. “Making it easy for businesses to build mature, reliable, and scalable machine learning models with as little as two lines of code is the kind of value we’re excited to help bring to market with Anyscale and Ray,” did he declare. said.

Image: mamewmy/Freepik

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