Freenome is developing next-generation blood tests for early cancer detection, powered by multiomics and machine learning. We are continuously exploring ways to make model development more efficient and scalable because the faster we can evaluate new models, the faster we can test their potential utility for use in new cancer diagnosis products. This presentation will describe our journey using Ray at Freenome to improve the scalability and ease-of-use of our machine learning platform. The presentation is general enough to be applicable to other industries and is appropriate for any group interested in improving machine learning model development. It is especially appropriate for teams that are looking for opportunities to scale and simplify machine learning operations (MLOps).
Zac is passionate about using machine learning to improve health care. He focuses on ways to reduce the iteration time needed to develop machine learning models, thus his excitement about Ray. He is currently developing a machine learning platform for Freenome, a blood-based cancer diagnostics company. Freenome uses multiomics data and machine learning models to identify molecular markers associated with advanced pre-cancerous neoplasia and cancer.
Zac has always loved creating new technologies. In addition to machine learning software development, his curiosity led him to develop technologies for directed evolution, Alzheimer’s disease, and next-generation sequencing.
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