Pretty much every week, we saw one IT giant after another coming up with “open-source” frameworks for ML / Advanced Analytics.
- IBM most recently announced SystemML (http://researcher.watson.ibm.com/researcher/view_group.php?id=3174) – specializing on abstraction and model generation.
- Microsoft announced just 2 weeks ago: DMLT – Distributed Machine Learning Toolkit — http://www.dmtk.io/
- Intel announced just before their TAP (Trusted Analytics Platform) – http://trustedanalytics.org/ – specializing on a secure data platform.
- Google did similar with TensorFlow (https://tensorflow.org/) – specializing on deep learning and flexible approaches to parallelization.
All this happened within a few weeks … this can’t be a coincidence.. But i dont think it is “me too”-philosophy either. My personal take is, that it is the new cool thing to do… but first and foremost, there is the expectation in the air that from the ca 10 million software engineers world-wide, about 1 million are well-positioned to become strong (engineering-type) citizen data scientists. So this enables the vendors to get more reach. Oracle, SAP, HP and some others maybe seen as increasingly legacy and outdated by the many “innovators”. The real innovation however is med- to long-term more around algorithm marketplaces – which I will blog about in the near future.
Until much more is known (early adopters are very rare and comimg up with mixed feedback) this will create more confusion in an already fragmented marketspace. How much this “open source” exactly means remains also to be seen. Some are likely more closed for external contributions than others.
Also we expect further impact of this – but so far we at Gartner have more questions than answers: What does this mean to start-ups like Dataiku, Dato, H2O, Skytree in the long-run? Will there now be start-ups trying to come up with a unifying layer for those 4 above frameworks to serve as a top-level UI for the different open-source approaches?
Predicts 2019: Data and Analytics Strategy
Data and analytics are the key accelerants of digitalization, transformation and “ContinuousNext” efforts. As a result, data and analytics leaders will be counted upon to affect corporate strategy and value, change management, business ethics, and execution performance.Read Free Gartner Research
Comments or opinions expressed on this blog are those of the individual contributors only, and do not necessarily represent the views of Gartner, Inc. or its management. Readers may copy and redistribute blog postings on other blogs, or otherwise for private, non-commercial or journalistic purposes, with attribution to Gartner. This content may not be used for any other purposes in any other formats or media. The content on this blog is provided on an "as-is" basis. Gartner shall not be liable for any damages whatsoever arising out of the content or use of this blog.