[pscsbs]

This is awesome! I've been waiting for someone to release an Excel-type ML product to make machine learning more accessible. This looks right up that alley, and will probably "democratize" access to ML in a number of fields that tend to be less coding-savvy.

[coleca]

Something about me feels that these efforts by IBM and Microsoft around AI are less around providing Open Source tools to democratize AI and more around providing "big data" style tools to "big enterprise". Both companies made TONS of $$$ selling Business Intelligence tools (SQL Server Analysis Services, Cognos, etc). They are smart enough to see the danger in open source tools like TensorFlow, Spark, etc. cutting into their lucrative revenue streams in the enterprise.

In particular, Microsoft has always been great about providing tools for no to low cost at the entry level, to get you (or more likely your company) hooked into the ecosystem. Not making a criticism, they have made some great stuff over the years (see the Visual Studio ecosystem for example).

The other angle is providing these tools, which can be complex to install/configure/manage, as a service offering via a subscription as part of the Azure platform. Recently MS has been hiring every superstar/rockstar evangelist/advocate/architect/engineer/etc to help design/build/promote/advocate for Azure that they can find (See Jessie Frazelle, @catie, and a ton of key people in the Golang world). Microsoft isn't just coming to play, they're playing to win.

[zitterbewegung]

IBM and Microsoft can embrace the open source tools Tensorflow and Spark by offering Enterprise Support (I know IBM has made a large investment in Spark). Their competitors would be databricks and Google. By being simply not Google that could win over people. Also, both of them need something to differentiate their cloud offering from Amazon.

[badminton1]

Access to AI/ML is already very democratic. Takes little time to set run Tensorflow from a docker image, or learn from Jupyter notebooks, etc... with fully open source projects where you can consult the source code and see how an algorithm is implemented.

In contrast this involves running a proprietary operating system, IDE, closed source, etc... Quite the contrary to anything that could be considered democratic.

Then, ML is all about volume. Open a spreadsheet with more than 10000 rows in Excel and see it squirm in pain.

[pscsbs]

Takes little time to set run Tensorflow from a docker image, or learn from Jupyter notebooks, etc... with fully open source projects where you can consult the source code and see how an algorithm is implemented.

This is exactly the problem -- what you're describing is not easy for anyone outside of tech to do. If you want to, say, run a simple text classification task and have thousands of labels, this is way overkill. Machine learning has the opportunity to become a common place utility for automating repetitive tasks, and the barrier to entry does not need to be learning Tensorflow, Docker, and Jupyter.