1. They want to build, train, and deploy a machine learning model into production. Presumably as a microservice, part of a web application, etc
2. They don't know how to program
I honestly can't imagine a less useful product than drag and drop ML.
I do know how to program.
I still want this tool. Badly. So badly. Just because I can program doesn't mean I want to use programming to solve every problem. I want simple tools that do the complex things for me for the 90% of cases where they are good enough.
Let me spend my time on the 10% of problems that simple tools can't solve!
It seems like this would be the worst of both worlds. Too simple to be of any use to any ML engineer, too complicated for the uninitiated, not customizable enough for a domain expert.
I haven't used this tool, but it seems reasonable that the people who it might be useful for (regardless if Microsoft PR recognizes this or not) are for scientists who have an idea for an algorithm, but don't want to spend too much time thinking about how to write and deploy python/C++ code to their clusters.
Sure, it may not seem like that much effort to many programmers, but as the Fortran discussion stressed, just because you can code and think logically, doesn't mean you're a programmer.
I’m not interested in deploying a service or anything, but it would be a great way to take a first pass at analyzing some of the huge and pretty complex datasets that we generate, like metagenomic DNA sequences of microbiotas that are paired with health related information that could also be fed into the model.
Even just narrowing down a list of potential targets would be pretty darn useful.
Not a lot of these people can program.
I know a lot of programmers who want to work with ML, but in my experience, very few programmers are good enough at math or statistics to do so, and even fewer have the business skills to actually translate their results to management in non-tech based organisations.
I’m sure a lot of programmers will make excellent data-scientists, but I’m not entirely convinced why I would bring ML to my programmers rather than my people who have degrees in applied statistics and organisations.
I work in the public sector. One of the reasons ML hasn’t found it’s golden case yet, is largely because no one have figured out how to use ML in a way that is better than the decades worth of data-related work we have already done, and part of the reason behind this, is that companies who sell ML are programmers. They know how to use ML to identify, but none of them, not even IBM seem to know how to use ML for something they can actually get us to buy. And lord knows both sides of the tables have tried, I mean, even our political leadership has heard of the ML hype, and want us to use it. So I’m rather hopeful these tools for non-programmers will bring ML into the hands of people who will know what to use it for.
"Anyone smart enough to use this isn't dumb enough to need it."
Those were the words that come to my mind everytime I see stuff like this. Clickers want solutions, not complicated toolkits in a GUI.
I don't see what the market would be for implementing this into production, like you say to make live in production needs to know how to code. Perhaps though I could see Segment offer a similar tool which I guess would be 'in production' without code.