Celebrating MosaicML
as they came out of stealth mode last night. Future Ventures was part of their first seed round, and Playground Global led the Series A soon thereafter.
The Problem, from WIRED:
“The cost of training AI is absolutely going up,” says David Kanter, executive director of MLCommons, an organization that tracks the performance of chips designed for AI. The idea that larger models can unlock valuable new capabilities can be seen in many areas of the tech industry, he says. It may explain why Tesla is designing its own chips just to train AI models for autonomous driving.
Some worry that the rising cost of tapping the latest and greatest tech could slow the pace of innovation by reserving it for the biggest companies, and those that lease their tools.
“I think it does cut down innovation,” says Chris Manning, a Stanford professor who specializes in AI and language. “When we have only a handful of places where people can play with the innards of these models of that scale, that has to massively reduce the amount of creative exploration that happens.”
Kanter of MLCommons says MosaicML’s technology may help companies take their models to the next level, but it could also help democratize AI for companies without deep AI expertise. “If you can cut the cost, and give those companies access to expertise, then that will promote adoption,” he says."
And Forbes:
"Open.ai used thousands of high-performance GPU’s for over three months to train the 175-billion-parameter GPT-3 transformer model, at an estimated expense of nearly $12 million. So, while AI models are exploding in size, the costs are becoming prohibitive."
The Solution, from NextPlatform:
"MosaicML came out of stealth today with $37 million in funding from a wide range of VC partners, including Lux Capital, Future Ventures, E14, and others.
The real product is open source and is split between two related efforts. First is MosaicML’s “Composer”which is a library of methods for optimal training that can be strung together as “recipes” based on benchmarked findings and published works. By the way, for those who are actually doing machine learning at scale, this little tool on MosaicML’s site provides what is completely missing in the market—either from analysts, benchmarks like MLPerf, or even anecdotes.
According to MosaicML, “The compositions in this library have allowed us to achieve training speedups and cost reductions of 2.9X on ResNet-50 on ImageNet, 3.5X on ResNet-101 on ImageNet, and 1.7X on the GPT-125 language models (as compared to the optimized baselines on 8xA100s on AWS), all while achieving the same model quality as the baselines. To make sure that these results reflect fair comparisons, all of these data come from training on a fixed hardware configuration on publicly available clouds, and none of these methods increase the cost of inference.”
The other side is the Explorer interface that allow users to do what’s seen above. You set a desired tradeoff between accuracy, cost, or speed to result and get a visualization of those tradeoffs across thousands of training runs on standard benchmarks.
“We believe that unfettered growth of computing is not a sustainable path towards a future powered by artificial intelligence. Our mission is to reduce the time, cost, energy, and carbon impact of AI / ML training so that better AI models and applications can be developed,” the company says.
We tackle this problem at the algorithmic and systems level. MosaicML makes machine learning more efficient through composing a mosaic of methods that together accelerate and improve training."
And Forbes:
"Naveen Rao should bring ML Optimization as a service to a new level of capabilities. If the team can consistently deliver 5-10X performance improvements as Rao believes, they will quickly find themselves with a long line of customers waiting to get their foot in the door."
Company: www.mosaicml.com
Celebrating MosaicML
as they came out of stealth mode last night. Future Ventures was part of their first seed round, and Playground Global led the Series A soon thereafter.
The Problem, from WIRED:
“The cost of training AI is absolutely going up,” says David Kanter, executive director of MLCommons, an organization that tracks the performance of chips designed for AI. The idea that larger models can unlock valuable new capabilities can be seen in many areas of the tech industry, he says. It may explain why Tesla is designing its own chips just to train AI models for autonomous driving.
Some worry that the rising cost of tapping the latest and greatest tech could slow the pace of innovation by reserving it for the biggest companies, and those that lease their tools.
“I think it does cut down innovation,” says Chris Manning, a Stanford professor who specializes in AI and language. “When we have only a handful of places where people can play with the innards of these models of that scale, that has to massively reduce the amount of creative exploration that happens.”
Kanter of MLCommons says MosaicML’s technology may help companies take their models to the next level, but it could also help democratize AI for companies without deep AI expertise. “If you can cut the cost, and give those companies access to expertise, then that will promote adoption,” he says."
And Forbes:
"Open.ai used thousands of high-performance GPU’s for over three months to train the 175-billion-parameter GPT-3 transformer model, at an estimated expense of nearly $12 million. So, while AI models are exploding in size, the costs are becoming prohibitive."
The Solution, from NextPlatform:
"MosaicML came out of stealth today with $37 million in funding from a wide range of VC partners, including Lux Capital, Future Ventures, E14, and others.
The real product is open source and is split between two related efforts. First is MosaicML’s “Composer”which is a library of methods for optimal training that can be strung together as “recipes” based on benchmarked findings and published works. By the way, for those who are actually doing machine learning at scale, this little tool on MosaicML’s site provides what is completely missing in the market—either from analysts, benchmarks like MLPerf, or even anecdotes.
According to MosaicML, “The compositions in this library have allowed us to achieve training speedups and cost reductions of 2.9X on ResNet-50 on ImageNet, 3.5X on ResNet-101 on ImageNet, and 1.7X on the GPT-125 language models (as compared to the optimized baselines on 8xA100s on AWS), all while achieving the same model quality as the baselines. To make sure that these results reflect fair comparisons, all of these data come from training on a fixed hardware configuration on publicly available clouds, and none of these methods increase the cost of inference.”
The other side is the Explorer interface that allow users to do what’s seen above. You set a desired tradeoff between accuracy, cost, or speed to result and get a visualization of those tradeoffs across thousands of training runs on standard benchmarks.
“We believe that unfettered growth of computing is not a sustainable path towards a future powered by artificial intelligence. Our mission is to reduce the time, cost, energy, and carbon impact of AI / ML training so that better AI models and applications can be developed,” the company says.
We tackle this problem at the algorithmic and systems level. MosaicML makes machine learning more efficient through composing a mosaic of methods that together accelerate and improve training."
And Forbes:
"Naveen Rao should bring ML Optimization as a service to a new level of capabilities. If the team can consistently deliver 5-10X performance improvements as Rao believes, they will quickly find themselves with a long line of customers waiting to get their foot in the door."
Company: www.mosaicml.com