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Artificial intelligence may be the most important thing since sliced bread. But this does not mean that it has become easier to develop and operate. According to According to a recent Boston Consulting Group survey, 74% of organizations struggle to extract value from their AI investments.
One of the biggest mistakes companies make is underestimating the amount of legwork involved in coordinating AI, says William Falcon, creator of PyTorch Lightning, a popular open source AI framework. “Building your own AI platform today is like building your own Slack — it’s complex, expensive, and not core to your company,” he told TechCrunch. “The value to organizations lies in their data, domain knowledge and unique models – not in maintaining the AI infrastructure.”
Falcone, a former Navy SEAL and Facebook AI research intern, began developing PyTorch Lightning while he was an undergraduate at Columbia University. The framework provides a high-level interface to the PyTorch AI library, abstracting code for setting up and maintaining AI systems.
After leaving his Ph.D. at New York University. After the program ended, Falcon decided to team up with Luis Capello, former data product manager at Forbes, to commercialize PyTorch Lighting. their project, Lightning artificial intelligencetakes the open source framework and brings enterprise-focused services and tools to the forefront.
“We have thousands of developers single-handedly training and deploying models (using Lightning AI) at a scale that would otherwise require teams of developers without Lightning,” Falcone said.
Lightning AI handles typically tedious tasks such as distributing AI workloads across servers and providing the infrastructure needed to evaluate and train AI. The company’s flagship product, AI Studios, allows customers to fine-tune and run AI models in their preferred cloud environments.
Businesses can also use Lightning AI to host AI-powered applications running on their own private cloud infrastructure or on-premises data centers. Pricing is on a pay-as-you-go basis, with a free tier that includes 22 “GPU clocks” per month.
Lightning AI’s goal is to make AI development “as intuitive as using an iPhone,” Falcon says. He claims the platform helped Cisco cut infrastructure setup time to two days, and enabled researchers at his alma mater, Columbia, to complete hundreds of experiments in 12 hours.
“Most people don’t know this, but many of the world’s leading AI products have been trained or built on Lightning,” Falcone said. “For example, Nvidia’s modeling suite, NeMo, is built using Lightning tools – Stable Diffusion by Stability AI is another.”
Lightning AI certainly has momentum. More than 230,000 AI developers and 3,200 organizations use the platform today, and the company recently raised $50 million in a funding round.
There is competition, though. Comet, Galileo, FedML, Arize, Deepset, Diveplane, Weights & Biases, and InfuseAI offer a similar mix of paid and free AI coordination services.
For his part, Falcon believes that the market for managed AI solutions is large enough to support multiple players. He is likely not wrong. According to Fortune Business Insights, the machine learning operations sector – the Lightning AI sector – could be as well He deserves About $13 billion by 2030.
With the recent $50 million investment, led by Cisco with participation from JP Morgan and Nvidia, the total Lightning AI war chest reaches $103 million. The 50-person New York-based company plans to spend the proceeds on recruiting new clients, including government clients, and expanding the Lightning platform to new markets.
“With a lean, high-performing team and a gross margin product of over 90%, we are on track to reach $10 million to $20 million in annual recurring revenue by the end of next year and achieve profitability soon after,” Falcone said.
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