
Cortex: API platform for machine learning engineers - ChefboyOG
https://github.com/cortexlabs/cortex/tree/v0.18.0
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ishcheklein
Interesting, what are the benefits of using this vs Sagemaker?

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calebkaiser
Maintainer here. There are a bunch of feature-level differences between Cortex
and SageMaker (spot instance support on Cortex is one that comes up a lot),
but the real core differences are:

1\. Cortex is for ML engineering, SageMaker is for data science.

Cortex is designed specifically for the needs of production-scale ML
applications. Deploying a model as an API with Cortex will be familiar to
anyone who has used Serverless or Beanstalk before--you write your API in a
Python script (Cortex provides a Predictor class for this), you configure your
deployment with a YAML file, and you deploy with the Cortex CLI. You can see a
GIF of a deployment on the repo:
[https://github.com/cortexlabs/cortex](https://github.com/cortexlabs/cortex)

In order to deploy the API, Cortex automates all the necessary cloud
infrastructure. It spins up a k8s cluster, containerizes/deploys your API,
configures autoscaling, implements a load balancer, streams logs, tracks
predictions, handles rolling updates, implements different deployment
strategies, cleans up, etc.

Because Cortex is just focused on serving models, not the data science/model
training side, it can do all of the above automatically in ways that are
familiar to software engineers (e.g. YAML/a CLI instead of notebooks/a Python
SDK). This solves a lot of production challenges with ML, particularly around
reliable deployment processes and collaboration.

Additionally, decoupling serving from training allows Cortex to be agnostic as
to how you develop your model. You can use SageMaker, download a pre-trained
model, whatever you like, so long as it can generate predictions.

2\. Cortex is free and open source.

The obvious benefit here is cost--you don't pay the ~40% premium for EC2
instances that SageMaker charges. Additionally, we believe that there are
fundamental benefits to open source infrastructure, in terms of transparency
and control.

