Today, we’re excited to announce the general availability of Continual, the missing AI layer for the modern data stack. We’ve also raised a $14.5M Series A, led by Innovation Endeavors and joined by Amplify Partners, Illuminate Ventures, Inspired Capital, Data Community Fund, Activation, New Normal, GTMfund, and angels Tomer Shiran, the founder of Dremio, and Tristan Handy, the founder of dbt Labs.
For our latest machine learning specialist interview on our blog, we’ve welcomed Ivan Goncharov, a machine learning engineer on the growth team at a unicorn MLOps startup, Weights & Biases.
Scammers exist in all forms of commerce. With the advancement of e-commerce, fraud has taken on new forms and become more powerful than ever before. Fraudsters take full advantage of any loophole in any system. Preventing, detecting, and eliminating fraud is one of the major focus areas of the e-commerce and banking industries at present. Banks and other financial institutions are investing in new ways to meet the challenge of preventing fraud.
This is part 3 of our 3-part Hyperparameter Optimization series, if you haven’t read the previous 2 parts where we explain ClearML’s approach towards HPO, you can find them here and here. In this blog post, we will focus on applying everything we learned to a “real world” use case.
Deciding to adopt an AI-first strategy is the easy part. Figuring out how to implement it takes a little more effort. It requires a clear-eyed vision built around well-defined goals and a realistic execution plan. Being AI-first means setting up your organization for the future. By leveraging data, analytics, and automation, a company can gain a better understanding of where it is and where it needs to go.
