With only about 35% of machine learning models making into production in the enterprise (IDC), it’s no wonder that production machine learning has become one of the most important focus areas for data scientists and ML engineers alike. As you may remember, we recently announced a full set of MLOps capabilities in Cloudera Machine Learning, our cloud native machine learning tool for the cloud.
Today, we announced the acquisition of the assets and IP of Knarr Analytics, an innovative start-up that provides real-time collaboration, sophisticated data exploration and insight capture capabilities, to complement Qlik’s cloud data and analytics platform.
Depending on your projects and your QA team’s nature, your choice of automation testing tool must be compatible with many different aspects, such as project scope and requirements, in addition to its reputation. The best tool available does not guarantee the best testing outcome. It must be the right one.
In the last blog with Deloitte’s Marc Beierschoder, we talked about what the hybrid cloud is, why it can benefit a business and what the key blockers often are in implementation. You can read it here.
If your company has already started getting into machine learning / deep learning, you will quickly relate to the following story. If your company is taking its first steps into data-science, here is what is about to be dropped on you. If none of the above strikes a chord, well it’s probably good to know what’s out there because data-science is all the rage now, and it won’t be long until it gets you too 🙂
Fivetran offers increased transparency and understanding of all data pipeline actions.
