ClearML hits 1.0

May 3rd 2021 – With over 11 man-years of working, and tinkering, long into the night, I am pleased to announce we have hit version 1.0. Following quickly after the release of ClearML 0.17.5, we added the last remaining features we felt 1.0 needed. Namely multi-model support, as well as improved batch operations. With these in place, the choice was clear. The next version released should be the baseline moving forward.


5 Ways AI Is Changing B2B Marketing and Customer Support

Artificial Intelligence has had a massive influence on everything related to business. It’s not just the tech industry that has felt its impact, but also pretty much any other industry thanks to the versatility that is so characteristic of AI. Likewise, the B2B sector has also been affected by the spread of AI and its common usage by business owners and marketers alike.

The Clear SHOW - S02E03 - Your Code == Feature Store

Ariel and T.Guerre discussing the reasoning behind features stores. Should you get one for your production pipeline? First time hearing about us? Go to -! ClearML: One open-source suite of tools that automates preparing, executing, and analyzing machine learning experiments. Bring enterprise-grade data science tools to any ML project.

How Can Companies Integrate Ethical AI? | Splunk's Ram Sriharsha & Dr. Rumman Chowdhury

Organizations use AI to be more competitive, deliver better business outcomes and avoid falling behind. However, business leaders should know they pose their organizations’ serious risk if they do not comply with ethical standards. Leadership must enable teams to practice ethical business strategies, up-level talent strategy, and enable organizational resilience. Dr. Rumman Chowdhury and Ram Sriharsha, Head of Machine Learning at Splunk, discuss the challenges companies will face if they do not comply with ethical standards and how to solve for fairness and privacy.

AI/ML without DataOps is just a pipe dream!

Let’s start with a real-world example from one of my past machine learning (ML) projects: We were building a customer churn model. “We urgently need an additional feature related to sentiment analysis of the customer support calls.” Creating the data pipeline to extract this dataset took about 4 months! Preparing, building, and scaling the Spark MLlib code took about 1.5-2 months!