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End-to-end solution for training and using ML models in production.
Dockerfile 2.83%
Python 47.43%
Shell 2.70%
HTML 44.12%
JavaScript 2.84%
CSS 0.09%
ml-ops's Introduction
Production level solution for automating training of already developed models
- Easy to setup Kubernetes solution which provides a ready to use environment for training complex ML algorithms.
- High Availiblity API service for fast training and prediction of ML models.
- Resource consumption is optimized by on demand resource allocation.
- Can be easily integrated to pre-existing automation solutions.
- Customized kafka cluster with secure communication protocols.
- Highly scalable and language agnostic.
- Each component of this solution has it's own README file containing the description, setup, and usage.
- Follow the Readme files and setup these components in the following order.
- Then take the
test-user
files and upload it to the SFTP folder of one of the user.
- Execute the training and prediction through the frontend application or the API service provided.
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