The rapid rise of generative artificial intelligence is driving significant changes in the enterprise landscape, and companies such as Anyscale Inc. are at the forefront of this transformation. With the complexity of AI infrastructure increasing, businesses are turning to innovative solutions to help them scale AI efficiently.

At the heart of this evolution is Anyscale Ray, a platform designed to streamline the scaling of AI infrastructure and manage diverse workloads, according to Robert Nishihara (pictured), co-founder of Anyscale

“There’s clearly going to be a massive infrastructure build-out for AI with any major technology, and with any infrastructure build-out, there’s a hardware piece and a software piece,” said Robert Nishihara, co-founder of Anyscale, in an interview with theCUBE. “On the hardware side, we all know how successful Nvidia is. But the software piece, there’s a lot of work to do. There’s a lot of complexity to rein in that’s growing in AI, and that’s a lot of what we’re trying to do.”

During this week’s Anyscale Ray Summit event, theCUBE Research’s John Furrier and Savannah Peterson, co-hosts of theCUBE, SiliconANGLE Media’s livestreaming studio, spoke with machine learning experts, software engineers and analysts to discuss the innovations redefining API management for scaling AI infrastructure. (* Disclosure below.)

Here are three key insights you may have missed from theCUBE’s coverage of Ray Summit:

1. AI infrastructure: Anyscale Ray tackles the AI complexity wall.

One of the major challenges in AI adoption today is the growing complexity of the infrastructure needed to support it. Companies face a fragmented environment of tools, cloud platforms and accelerators, which slows down AI development. Companies need unified solutions that can handle both classical machine learning and generative AI workloads, according to Nishihara. Anyscale Ray offers such a solution by simplifying AI infrastructure for enterprises, empowering teams that may not have the internal expertise to build from scratch.

Anyscale’s Robert Nishihara and Keerti Melkote talk with theCUBE about AI infrastructure and driving AI innovation.

“A lot of the people we work with are the AI platform engineers and infrastructure engineers, the people who are providing AI capabilities throughout their company and enabling all the other teams to move quickly,” Nishihara said.

These teams have been critical in supporting AI workloads for years, helping their companies evolve from predictive models to deep learning and now generative AI, he added.

Anyscale’s managed services can bridge the gap for companies lacking in-house AI development capabilities, according to Keerti Melkote, chief executive officer of Anyscale, during an interview with theCUBE. By providing a comprehensive solution that integrates both hardware and software needs, it simplifies the AI infrastructure process and accelerates time-to-market for enterprises.

“The real enterprise challenge is how do you take that expertise and put it out there?” Melkote said. “What we see as the commercialization opportunity is to take that, offer it to the managed service and wrap it around professional services so customers can take their business problem and come up with a real solution as opposed to just tinkering with tools.”

Here’s theCUBE’s complete video interview with Robert Nishihara and Keerti Melkote:

2. Uber leverages scalable GPU clusters powered by Anyscale Ray.

Uber Technologies Inc. is a prime example of a company leveraging scalable AI infrastructure to enhance its platform. With the adoption of Anyscale Ray, Uber has upgraded its AI platform to handle diverse workloads more efficiently, allowing it to process over 20,000 model training jobs every month. This would have been impossible without Ray, according to Zhitao Li, director of engineering, AI and model infrastructure, at Uber Technologies Inc.

Uber’s Zhitao Li talks about upgrading Uber’s AI platform.

“Before Ray, we were operating distributed Spark clusters that trie[d] to run machine learning model training on the inside, but it’s very difficult to retrofitted those things to run on the GPU native cluster, managing the life cycle of the workload,” Li told theCUBE. “It’s almost a decade of history of the containers. So, these containers provide the best isolation of the workload and allowed us to utilize the GPUs and run heterogeneous computation cluster in the most maintainable and scalable way.”

Uber manages numerous clusters and runs over 20,000 model training jobs monthly, which wouldn’t be achievable without Ray, Li explained. He also noted that open source plays a key role in Uber’s approach to AI infrastructure.

“Uber is entering hybrid cloud setup,” Li said. “Evolving the data lake, especially ML aspect of the data lake in the multicloud, multi-region, multi-provider environment, is going to be an interesting challenge … we are always thinking about open source. We want to open source the great innovations, those elastic resource sharing stuff — some of the tools we develop for responsible AI like explainability on top of Ray, some of workflow stuff. We want to always look around about great innovations in open source and absorb them in our platform.”

Here’s theCUBE’s complete video interview with Zhitao Li:

3. Runway AI revolutionizes filmmaking with generative AI.

In the world of entertainment, AI-driven filmmaking is proving to be a game-changer, enhancing creativity and audience engagement. Runway AI Inc. has embraced generative AI to produce compelling visuals and personalized content for major events, such as Madonna’s live concert tour. The company’s video-to-video tool, which allows users to transform video styles while maintaining structure, has revolutionized visual effects and video production.

Runway’s Anastasis Germanidis talks to theCUBE about the revolutionary effect of AI-driven filmmaking.

“Most recently we released our video-to-video tool that allows you to take an input video and translate it while maintaining the structure, completely transforming the style of that video,” said Anastasis Germanidis, co-founder and chief technology officer of Runway AI Inc.

Runway’s tools are versatile and offer filmmakers the control they need to generate stylistically unique content, he added. The company also introduced Gen-3 Alpha, a video foundation model that powers tools such as text-to-video and image-to-video. This technology is driving innovation in AI filmmaking, enabling filmmakers to experiment more freely while reducing production costs. Germanidis highlighted the company’s collaboration with Lionsgate to explore how generative media can be used in film production workflows.

“Our role is to essentially become translators between the language of AI and technology and the language of art and creative tools,” he explained.

Runway’s use of Anyscale Ray enables the company to scale its multimodal data processing, offering enhanced versatility and developer experience for building video models.

Here’s theCUBE’s complete video interview with Anastasis Germanidis:

To watch more of theCUBE’s coverage of Ray Summit, here’s our complete event video playlist:

(* Disclosure: TheCUBE is a paid media partner for the Ray Summit 2024. Neither Anyscale, the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE