ML platform ‘reimagines’ MLOps for edge AI
AI/ML startup OmniML has announced the release of a platform that simplifies and accelerates machine learning operations (MLOps) by bridging the gap between ML models and edge hardware. The Omnimizer platform auto-adapts and optimizes models for hardware, allowing ML engineers to focus on model design and training without worrying about the complex details of hardware deployment.
It gives deployment engineers the confidence that the models will work properly on target devices without the need for redesign and iterations between multiple teams. As a result, says the company, Omnimizer eliminates the inefficiencies that lead to slow deployment, poor performance, and higher costs.
“There is so much promise in using AI closer to where people live, but it is still too inefficient and costly to deliver these tremendous benefits for everyone,” says OmniML Co-Founder and CEO Di Wu, PhD. “Omnimizer solves this by unifying workstreams of ML development and deployment, enabling enterprises to adapt existing models for their hardware based on their specific business needs.”
Supporting most machine learning capabilities for Computer Vision, Natural Language Processing, and many other domains, Omnimizer can intake open-source or a customer’s existing ML models with a few lines of code. It is powered by a cloud-native backend infrastructure that enables hassle-free model adaption and deployment for almost all major chip platforms including CPUs, GPUs, and AI SoCs.
Current Omnimizer customers are leveraging the platform to optimize ML models for autonomous vehicles, robotics, IoT, and mobile devices. The company says that it is also working on proof-of-concept opportunities in industrial automation, smart appliances, and pharmaceuticals, among other industries, as part of its mission to bring the benefits of AI to everyone.
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