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Propagating Machine Learning/AI Development Environments to Production with MLFlow

PassionBytes > Blog > Machine Learning > Propagating Machine Learning/AI Development Environments to Production with MLFlow
  • February 22, 2020
  • passionbytes
  • Machine Learning

Objective

Python is not a strongly typed language like Java or C++. However as a language that evolved taking good amount of time and the easiness to manage it, Python definitely is a language of choice for many, especially in the field of machine learning. The vast availability of libraries for algorithms and visualization. Following are some of reasons why I like Python, though it’s not my favourite:

  • Ease of Use. Python is one of the easiest languages to learn because the syntax is simple.
  • Documentation. Python has excellent and extensive documentation. It’s also very well covered on StackOverflow, YouTube, etc.
  • Libraries. Python has awesome libraries for web development, machine learning, big data analytics, data visualization, statistics, scientific computing, and so on. So it is very popular in many domains.
  • Supports multiple programming paradigms — functional, object oriented, scripting, etc. It’s a handy language for very short scripts as well as full web apps.

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  • Propagating Machine Learning/AI Development Environments to Production with MLFlow
  • Online and Batch Based ML Execution from Same Python Code Preserving Pre and Post Transformation States and Affinity
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