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Deploy Machine Learning & NLP Models with Dockers (DevOps)

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Deploy Machine Learning & NLP Models with Dockers (DevOps)


Machine Learning, as we know it is the new buzz word in the Industry today. This is practiced in every sector of business imaginable to provide data driven solutions to complex business problems.This poses the challenge of deploying the solution, built by the Machine Learning technique so that it can be used across the intended Business Unit and not operated in silos.

This is a extensive and well thought course created & designed by UNP’s elite team of Data Scientists from around the world to focus on the challenges that are being faced by Data Scientists and Computational Solution Architects across the industry  which is summarized the below  sentence :


This course will help you create a solid foundation of the essential topics of data science along with a solid foundation of deploying those created solutions through Docker containers  which eventually will expose your model as a service (API) which can be used by all who wish for it.

At the end of this course, you will be able to:

  • Learn about Docker , Docker Files, Docker Containers
  • Learn Flask Basics & Application Program Interface (API)
  • Build a Random Forest Model and deploy it.
  • Build a Natural Language Processing based Test Clustering Model (K-Means) and visualize it.
  • Build an API for Image Processing and Recognition with an Deep Learning Model under the hood (Convolutional Neural Network : CNN)

This course is a perfect blend of foundations of data science, industry standards, broader understanding of machine learning and practical applications and most importantly deploying them.

Who this course is for:

  • Anyone willing to venture into the realm of data science
  • Anyone who would be interested in deploying a Data Science Solution, can be Regression, NLP or even Deep Learning Models


  • Basic programming in any language
  • Basic Mathematics
  • Some exposure to Python (but not mandatory)

Last updated 6/2018


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