Startup Anyscale Simplifies DevOps AI Coding/Scaling Experience - SiliconANGLE

Startup Anyscale Simplifies DevOps AI Coding/Scaling Experience – SiliconANGLE

Artificial intelligence is all around us: from CCTV face detection technology to connected home devices, robots and even video games.

Gartner Inc. estimated the value of AI software at $62 billion for 2022, an increase of 21.3% from 2021. And the entire AI industry will grow at an annual rate by 33.2% from 2020 to 2027.

With AI’s immense potential to transform every industry, it’s no surprise that skilled developers are challenging their own valuable visions and ideas. However, coding AI software requires a myriad of compute resources, from servers to GPUs, in addition to infrastructure expertise to scale the product in the cloud and generate real value. The Anyscale Inc. Unified Compute Platform tackles this problem head-on so developers can focus on what they know.

“What we build at Anyscale really try to the point where, as a developer, if you know how to program on your laptop, in Python for example, so that’s enough,” said Robert Nishihara (pictured), co-founder and CEO of Anyscale. “TWhen you can do AI, you can get value out of it, scale it and build the types of incredibly powerful AI applications that companies like Google, Facebook and others can build.

Nishihara spoke with theCUBE Industry Analyst John Walls during the AWS re:Invent 2022 Global Startup Program during an exclusive broadcast on theCUBE, SiliconANGLE Media’s live streaming studio. They discussed Anyscale’s unique AI problem statement and the value it hopes to create by solving it. (*Disclosure below.)

Delve deeper into the many complexities of AI

When we see a nice car driving, we just ogle at its immaculate design, tuning, and ergonomic qualities, forgetting that a lot of things have to go right for it to even run. The same can be said for AI initiatives; several hurdles, such as scaling and transitioning to production, must first be overcome, according to Nishihara.

“A lot of AI initiatives, something like 80 or 90%, don’t worry the research or prototyping phase and into production,” he said.So some of the things that are hard about AI and why AI initiatives can fail, one is the required scale. It’s one thing to develop something on your laptop, it’s quite another to run it on thousands of machines. Another is the transition between development and prototyping in manufacturing. »

For better or worse, businesses today are mostly segmented and as a result development teams must follow specific handoff protocols, which often involve varying technology stacks and programming software and can create complexity. Another issue is flexibility…or lack thereof, Nishihara added.

“Many of the teams we work with have infrastructure built and are use products to do AI, but they found that it kind of blocked them in rigid workflows or specific tools and they don’t have the flexibility adopt new algorithms, strategies or approaches as they are developed and released,” he said. “Bbut their developers want flexibility use the latest tools and strategies.

Decomposing Ray, Anyscale’s open source project

Anyscale was launched three years ago. What puts this figure into perspective, however, is Nishihara’s previous work on machine learning during his PhD. years at the University of California at Berkeley. A disreputable part of the job was creating a host of ad hoc tools to take advantage of the computing power required. Nishihara also noticed that other researchers and practitioners in the fields of AI and ML were facing the same problem. It became clear that this paradigm had the potential to hold back the industry, and together with his team, Nishihara created Ray, an open-source framework for distributed machine learning.

Designed primarily for the Python programming language, Ray allows users to scale the most compute-intensive ML workloads with dramatically reduced effort through native libraries, such as Ray Serve and Ray Tune. What started as a community-focused university project has, over time, grown into a full-fledged business, Nishihara explained.

“We were really excited to solve this problem to make distributed computing easy,” he said. “It’s only later when we were graduating from Berkeley and we wanted to keep pushing this project forward and solve this problem that we realized it made sense to start a business.

Here’s the full video interview, which is part of SiliconANGLE and theCUBE’s coverage of the AWS re:Invent 2022 Global Startup program:

(* Disclosure: Anyscale Inc. sponsored this segment of theCUBE. Neither Anyscale nor other sponsors have editorial control over the content of theCUBE or SiliconANGLE.)

Photo: SiliconANGLE

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