Deploy, manage, and scale machine learning applications

Deploy machine learning applications without worrying about setting up infrastructure, managing dependencies, or orchestrating data pipelines. Demo video:
Thank you @chrismessina for hunting us! Hi everyone, I’m Omer, one of the co-founders of Cortex Labs. Cortex deploys your machine learning pipelines to your cloud infrastructure. You define models in TensorFlow and data processing steps in PySpark and Cortex runs them at scale on your AWS account. We started thinking about ML deployment about a year ago, when @davideliahu and I wanted to transition from backend engineering to machine learning engineering. We tried to build simple apps and found ourselves writing a lot more glue code than machine learning code. Most of our time was spent figuring out how to process data into feature columns, writing lots of TensorFlow boilerplate code, and configuring infrastructure to deploy our models as JSON APIs. Until now, we focused on a narrow stack so that we can automate as much of the grunt work as possible. Our next steps are to support more ML frameworks like PyTorch and cloud providers like GCP. We’d love to hear your feedback!