PyCaret 2.0

Last week we have announced PyCaret 2.0, an open source, low-code machine learning library in Python that automates machine learning workflow. It is an end-to-end machine learning and model management tool that speeds up machine learning experiment cycle and helps data scientists become more efficient and productive.

In this post we present a step-by-step tutorial on how PyCaret can be used to build an Automated Machine Learning Solution within Power BI, thus allowing data scientists and analysts to add a layer of machine learning to their Dashboards without any additional license or software costs. PyCaret is an open source and **free to use **Python library that comes with a wide range of functions that are built to work within Power BI.

By the end of this article you will learn how to implement the following in Power BI:

• Setting up Python conda environment and install pycaret==2.0.
• Link the newly created conda environment with Power BI.
• Productionalize / deploy your AutoML solution in Power BI.

Microsoft Power BI

Power BI is a business analytics solution that lets you visualize your data and share insights across your organization, or embed them in your app or website. In this tutorial, we will use Power BI Desktop for machine learning by importing the PyCaret library into Power BI.

What is Automated Machine Learning?

Automated machine learning (AutoML) is the process of automating the time consuming, iterative tasks of machine learning. It allows data scientists and analysts to build machine learning models with efficiency while sustaining the model quality. The final goal of any AutoML solution is to finalize the best model based on some performance criteria.

Traditional machine learning model development process is resource-intensive, requiring significant domain knowledge and time to produce and compare dozens of models. With automated machine learning, you’ll accelerate the time it takes to get production-ready ML models with great ease and efficiency.

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