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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.

The Secret To High Quality Code with Dr. Michaela Greiler and Liran Haimovitch

Delivering high-quality code quickly is the heart and soul of software development. The rise of open-source software has seen the widespread adoption of the Pull-Request (or Merge-Request) method of operation. Using this shift-left-oriented approach, we put our code through the wringer before pushing it into the main line of development. Evaluation of a new piece of code may include code reviews, static analysis, automated testing, and more.

How to get started with chaos engineering (k6 Office Hours)

Chaos engineering and performance testing ultimately have the same goal: improving software reliability. But chaos engineering hasn't really been picked up by many testers yet. Nicole shares her experiences in running her first chaos experiments using k6 and New Relic. k6 Office Hours is a weekly livestream hosted by Nicole van der Hoeven and Simon Aronsson to talk about performance and reliability testing, best practices, and all things k6.

Build interactive analytics in your React App with ThoughtSpot Everywhere

ThoughtSpot has revolutionized access to analytics for business users through search and AI. In addition to being a general purpose analytics tool that allows unprecedented access to business users, product builders can now use ThoughtSpot to deliver search-based analytics to customers. Today, we are launching a brand new SDK that allows you to embed ThoughtSpot into your own web app in literally minutes.

Iguazio Named A Fast Moving Leader by GigaOm in the 'Radar for MLOps' Report

At Iguazio, we’ve spoken and written at length about the challenges of bringing data science to production. The complexity of operationalizing ML can generate huge costs in terms of work hours and compute resources, especially as successful projects get scaled up and expanded. We’re proud to share that the Iguazio Data Science Platform has been named a fast moving leader in the GigaOm Radar for MLOps report.