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Scalable Machine Learning Infrastructure

We are currently researching frameworks and infrastructure to develop scalable and easy to manage machine learning products.

We are aiming to provide data products for which the time to market is reduced, that is prototyping machine learning models, like deep learning, to deploying them to production. Combining this with open source software will give you the possibility to start with a low investment and following scale out your machine learning product at low cost.

Adrin Jalali shared his journey on Github:

My journey started with this question on StackOverflow. I wanted to be able to do my usual data science stuff, mostly in python, and then deploy them somewhere serving like a REST API, responding to requests in real-time, using the output of the trained models. My original line of thought was this workflow:

  • train the model in python or pyspark or in scala in apache spark.
  • get the model, put it in an apache flink stream and serve.

This was the point at which I had been reading and watching tutorials and attending meetups related to these technologies. I was looking for a solution which is better than:

  • train models in python
  • write a web-service using flask, put it behind a apache2 server, and put a bunch of them behind a load balancer.

This just sounded wrong, or at its best, not scalable. After a bit of research, I came across pipeline.io which seems to promise exactly what I'm looking for.

I also went through these two videos:

You can read the rest on Adrin's Github: 

https://github.com/adrinjalali/pipeline-docs/blob/master/README.md

http://www.ancud.de/data-science/python

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Ancud IT-Beratung GmbH
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90478 Nürnberg 

Tel.: +49 911 2525 68-0