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How to Evaluate MLOps Platforms

Companies that have pioneered the application of AI at scale did so using their own in-house ML platforms (uber, LinkedIn, Facebook, Airbnb). Many vendors are now making these capabilities available to purchase as off-the-shelf products. There's also a range of open-source tools addressing MLOps. The rush to the space has created a new problem — too much choice. There are now hundreds of tools and at least 40 platforms available:

(Timeline image from Thoughtworks Guide to Evaluating MLOps Platforms.)

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How to Setup/Install MLFlow and Get Started

In this post, you will learn about how to setup/install MLFlow right from your Jupyter Notebook and get started tracking your machine learning projects. This would prove to be very helpful if you are running an enterprise-wide AI practice where you have a bunch of data scientists working on different ML projects. MLFlow will help you track the score of different experiments related to different ML projects.

Install MLFlow Using Jupyter Notebook

In order to install/set up MLFlow and do a quick POC, you could get started right from within your Jupyter notebook. Here are the commands to get set up. MLFlow could be installed with the simple command: pip install mlflow. Within Jupyter notebook, this is what you would do: