We are excited to announce PyCaret 2.2 — update for the month of Oct 2020.

PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows. It is an end-to-end machine learning and model management tool that speeds up the machine learning experiment cycle and makes you more productive.

In comparison with the other open-source machine learning libraries, PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few words only. This makes experiments exponentially fast and efficient.

Release Notes: https://github.com/pycaret/pycaret/releases

Documentation: https://pycaret.readthedocs.io/en/latest/

Installing PyCaret

Installing PyCaret is very easy…

We are excited to announce PyCaret 2.1 — update for the month of Aug 2020.

PyCaret is an open-source, low-code machine learning library in Python that automates the machine learning workflow. It is an end-to-end machine learning and model management tool that speeds up the machine learning experiment cycle and makes you 10x more productive.

In comparison with the other open-source machine learning libraries, PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few words only. This makes experiments exponentially fast and efficient.

If you haven’t heard or used PyCaret before…

You may be wondering since when did GitHub get into the business of Automated Machine Learning. Well, it didn’t but you can use it for testing your personalized AutoML software. In this tutorial, we will show you how to build and containerize your own Automated Machine Learning software and test it on GitHub using Docker container.

We will use PyCaret 2.0, an open source, low-code machine learning library in Python to develop a simple AutoML solution and deploy it as a Docker container using GitHub actions. If you haven’t heard about PyCaret before, you can read official announcement for PyCaret…

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. …

We are excited to announce the second release of PyCaret today.

PyCaret is 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 makes you more productive.

In comparison with the other open source machine learning libraries, PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few words only. This makes experiments exponentially fast and efficient.

See detailed release notes for PyCaret 2.0.

Why use PyCaret?

PyCaret

PyCaret is an open source machine learning library in Python to train and deploy supervised and unsupervised machine learning models in a low-code environment. It is known for its ease of use and efficiency.

In comparison with the other open source machine learning libraries, PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with a few words only.

If you haven’t used PyCaret before or would like to learn more, a good place to start is here.

“After talking to many data scientists who use PyCaret on a daily basis, I have…

RECAP

In our last post, we demonstrated how to develop a machine learning pipeline using PyCaret and serve it as a Streamlit web application deployed onto Google Kubernetes Engine. If you haven’t heard about PyCaret before, you can read this announcement to get started.

In this tutorial, we will use the same web app and machine learning pipeline that we had built previously and demonstrate how to deploy it using AWS Fargate which is a serverless compute for containers.

By the end of this tutorial, you will be able to build and host a fully functional containerized web app on AWS…

RECAP

In our last post on deploying a machine learning pipeline in the cloud, we demonstrated how to develop a machine learning pipeline in PyCaret and deploy a trained model on Heroku PaaS as a web application built using a Streamlit open-source framework. If you haven’t heard about PyCaret before, you can read this announcement to learn more.

In this tutorial, we will use the same machine learning pipeline and Streamlit app and demonstrate how to containerize and deploy them onto Google Kubernetes Engine.

By the end of this tutorial, you will be able to build and host a fully functional…