deliahu

6 ms·

1. 1.▲
   by deliahu 7y ago

   Hi, I'm one of the maintainers of Cortex >> how does your system work in the IRIS or other examples you have? It looks like it hides under the hood a few things compared to this implementation - type of the model (tensorflow) and
2. 2.▲
   by deliahu 7y ago

   Cortex contributor here - you're right, I would say we can be compared to SageMaker model deployment. We are currently working on supporting spot instances for serving, and training is on our roadmap.
3. 3.▲
   by deliahu 8y ago

   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 work
4. 4.▲
   by deliahu 8y ago

   We use TesnorFlow serving ( https://www.tensorflow.org/serving ) for serving the trained models. We also run Flask to transform the incoming JSON to match the way the data has been transformed at training time.
5. 5.▲
   by deliahu 8y ago

   Thanks for letting us know, we'll take a look
6. 6.▲
   by deliahu 8y ago

   Thank you!
7. 7.▲
   by deliahu 8y ago

   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) becaus
8. 8.▲
   Show HN: Cortex – machine learning infrastructure for developers(github.com)

   86 points by deliahu 8y ago|14 comments