Data

Databricks open sources MLflow, simplifying end-to-end Machine Learning Lifecycle

2 min read

Machine Learning has energised software applications with highly accurate predictions thereby upsurging the product demand of tech driven companies. However, while developing such smart applications, numerous machine learning challenges and software development issues are been faced by data scientist and machine learning professionals.

Today, Databricks open sources their newly developed framework MLflow, with an aim to simplify their complex machine learning experiments with smart automation and numerous accessibility in deploying your machine learning models across any platform.

With MLflow, Machine Learning users can simply standardize their complex processes while building and deploying their machine learning and predictive models. With this framework, data scientists are fueled with lots of automation accessibility through which they can track experiments, package their machine learning codes and manage their models on any of the popular machine learning frameworks.

The current platform offers following three components:

  • MLflow Tracking: This component allows you to log codes, data files, config and results. It also allows to query your experiments through which you visualize and compare your experiments and parameters swiftly without much hassle.
  • MLflow Projects: It provides structured format for packaging machine learning codes along with useful API and CLI tools.This allows data scientists to reuse and reproduce their codes and easily chain their projects and workflows together.
  • MLflow Models: It is a standard format for packaging and distributing machine learning models across different downstream tools. Azure ML compatible models, Deploying with Amazon Sagemaker or deploying on a local REST API are some of the examples of distributing models.

The current version is just an Alpha release and more features would be added to its full release. To get more details on its core offerings, APIs and command-line interfaces, read the official documentation at mlflow.org.

Read Next

MachineLabs, the browser based machine learning platform, goes open source

Microsoft Open Sources ML.NET, cross-platform machine learning framework

Google announces Cloud TPUs on the Cloud Machine Learning Engine (ML Engine)

Pravin Dhandre

Category Manager and tech enthusiast. Previously worked on global market research and lead generation assignments. Keeps a constant eye on Artificial Intelligence.

Share
Published by
Pravin Dhandre

Recent Posts

Top life hacks for prepping for your IT certification exam

I remember deciding to pursue my first IT certification, the CompTIA A+. I had signed…

3 years ago

Learn Transformers for Natural Language Processing with Denis Rothman

Key takeaways The transformer architecture has proved to be revolutionary in outperforming the classical RNN…

3 years ago

Learning Essential Linux Commands for Navigating the Shell Effectively

Once we learn how to deploy an Ubuntu server, how to manage users, and how…

3 years ago

Clean Coding in Python with Mariano Anaya

Key-takeaways:   Clean code isn’t just a nice thing to have or a luxury in software projects; it's a necessity. If we…

3 years ago

Exploring Forms in Angular – types, benefits and differences   

While developing a web application, or setting dynamic pages and meta tags we need to deal with…

3 years ago

Gain Practical Expertise with the Latest Edition of Software Architecture with C# 9 and .NET 5

Software architecture is one of the most discussed topics in the software industry today, and…

3 years ago