Last year we made data science a team sport with IBM Data Science Experience, our award-winning IDE for analytics. This summer we brought to market IBM Watson Machine Learning that allows companies to put models into production with easy model management and full workflow automation. And last week, we announced we've grown up those two products into Watson Data Platform, while adding new features.
Imagine you’re interviewing a new job applicant who graduated top of their class and has a stellar résumé. They know everything there is to know about the job, and has the skills that your business needs. There’s just one catch: from the moment they join your team, they’ve vowed never to learn anything new again. You probably wouldn’t make that hire, because you know that lifeMachine Learning Brainlong learning is vital if someone is going to add long-term value to your team. Yet when we turn to the field of machine learning, we see companies making a similar mistake all the time. Data scientists work hard to develop, train and test new machine learning models and neural networks. However, once the models get deployed, they don’t learn anything new. After a few weeks or months, become static and stale, and their usefulness as a predictive tool deteriorates.
In recent years, machine learning has prevailed over two champions on the quiz show, Jeopardy!, and vanquished the world’s number one-ranked player of Go, one of the most complex strategy games humankind has ever devised. You can’t doubt its immense power and reach, but it’s not all about playing games. Machine learning is fundamentally changing the way we approach computing—and it can pay off big time for your business.
Helping your data scientists work more productively is a key priority. The answer is to use automation to give them more time for analysis without compromising the quality of the data they use. IBM Data Catalog, a new beta solution that’s part of Watson Data Platform, offers tools to automate and simplify data discovery, curation, and governance.