OctoML Announces Early Access for Its Machine Learning Platform for Automated Model Optimization and Deployment
OctoML, the MLOps automation company for superior model performance, portability and productivity, announced early access to Octomizer. Octomizer brings the power and potential of Apache TVM, an open source deep learning compiler project that is becoming a de facto industry standard, to machine learning engineers challenged by model deployment timelines, inferencing and throughput performance issues or high inferencing cloud costs.
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Industry analysts estimate that machine learning model costs will double from $50 billion in 2020 to more than $100 billion by 2024. Many machine learning models put into production today cost hundreds of thousands to millions of dollars to train, and training costs represent only a fraction of the ongoing inferencing costs that businesses take on to provide cutting-edge capabilities to their end users.
“In our early engagements with some of the world’s leading tech companies, they’ve been excited about our ability to provide unparalleled model performance improvement,” said Luis Ceze, OctoML co-founder and CEO. “Now we’re excited to open the Octomizer up for early access to a select set of customers and partners with similar model performance, inferencing cost savings or edge deployment needs.”
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OctoML has demonstrated the potential of the Octomizer with early customer engagements across model architectures and hardware targets. OctoML’s early partners include Computer Vision (CV) and Natural Language Processing (NLP) machine learning teams focused on improving model performance on various targets such as NVIDIA’s V100, K80, and T4 GPU platforms, Intel’s Cascade Lake, Skylake, and Broadwell x86 CPUs, and AMD’s EPYC Rome x86 CPUs. Model performance improvements were at an order-of-magnitude level – for example, a Computer Vision based team worked with OctoML to decrease model latency from 95 milliseconds to 10 milliseconds, unlocking higher throughput and enabling new product feature development.