Show HN: Cortex – machine learning infrastructure for developers

And as far as SEO goes, ARM Cortex.

Really, if you want people to use your thing, make it easy for them to find.

Thanks for letting us know, we'll take a look

LOL...every other AI related project these days is called some variation or part of: Cortex Mental Mind Neural/Neurons

Which is, of course, hilarious, since none of them have anything to do with the brain or mind whatsoever, being slightly advanced computational statistics programs.

And this from Weaveworks: https://www.weave.works/oss/cortex/

The main thing we try to help with is orchestrating Spark, TensorFlow, TensorFlow Serving, and other workloads without requiring you to manage the infrastructure. You’re right that we have a thin layer around tf.estimator (by design) because our goal is to make it easy to create scalable and reproducible pipelines from building blocks that people are familiar with. We translate the YAML blocks into workloads that run as a DAG on Kubernetes behind the scenes.

curious what kind of infrastructure have you built for deployment, serving and managing models?

We have a lot of respect for the work that the KubeFlow team is doing. Their focus seems to be on helping you deploy a wide variety of open source ML tooling to Kubernetes. We use a more narrow stack and focus more on automating common workflows.

For example, we take a fully declarative approach; the “cortex deploy” command is a request to “make it so”, rather than “run this training job”. Cortex determines at runtime exactly what pipeline needs to be created to achieve the desired state, caching as aggressively as it can (e.g. if a hyperparameter to one model changes, only that model is re-trained and re-deployed, whereas if a transformer is updated, all transformed_columns which use that transformer are regenerated, all models which use those columns are re-trained, etc). We view it as an always-on ML application, rather than a one-off ML workload.

Thank you!