APIs are the backbone of digital transformation. Via APIs, you can securely share data and functionality with developers both inside and outside of your organizational boundaries, letting you build applications faster, seamlessly connect and interact with partners, and drive new business revenue. Because APIs encompass business-critical information, any downtime or performance degradation can lead to significant loss in revenue, customers, and brand value.

Artificial intelligence is transforming software development. From the code to the deployment, AI is slowly but surely upping its game and helping us discover a brand new paradigm for inventing technology. Algorithm-based machine learning is being used to accelerate the software development lifecycle and AI is supporting developers to optimize software workflow at every stage of the development process.

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.

Few things in life are certain, least of all roadmaps. There is a saying I love, apart from the one above, which says “if you want to hear God laugh, tell him/her your plans”. Nowhere is that more true than in software development in a startup. There are grand ideas put forward, people often vie with one another, in short, life happens.

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!

Although the title might sound like a collaboration of two music bands with really bad names, this blog is all about understanding how computer vision and machine learning can be used to improve safety and security in a harsh and dangerous environment of a construction site. The construction industry is one of the most dangerous industries according to the common stats from OSHA.

Telecom companies monitor their network using a variety of monitoring tools. There are separate fault management and performance management platforms for different areas of the network (core, RAN, etc.), and infrastructure is monitored separately. Although these solutions monitor network functions and logic – something that would seem to make sense — in practice this strategy fails to produce accurate and effective monitoring or reduce time to detection of service experience issues.

One of the most leading questions we often receive is, “How does ClearML Compare to..”. I am sure this is the same for any Open Source product. People always want to find the best. The sad truth is, of course, there usually is no “right answer”. What one person needs, another may not. I am sure that, whichever language you speak natively, there is some saying. In English it would be “one mans rubbish, is another mans gold”.

Few people can understand the difference between theory and practice more clearly than a chess Grandmaster. Our little 64-square laboratory has space for centuries of ideas. With more moves than atoms in the solar system, my ancient boardgame has limitless complexity for the human mind—and was even enough to stump the world’s fastest computers for decades.

Artificial Intelligence is in the news a lot, and it’s hyped as a cure for all ills in the same breath it’s suspected of spelling doom for us all. What’s the truth behind all the noise? What does artificial intelligence do, seeing as it is simply everywhere. The truth of the matter is that whatever the would-be prophets say, artificial intelligence and machine learning is here, now, and has applications to your day-to-day.