My employer, Microsoft, has started a new blog around ML and also announced a new product for ML.
The blog is exciting, as there are multiple ML luminaries at Microsoft who will hopefully contribute. Joseph Sirosh is also involved so there will presumably be a healthy mix of application oriented content as well.
The product is also exciting. However if you are an ML expert already comfortable with a particular toolchain, you might wonder why the world needs this product. Those who work at large companies like Microsoft, Google, Facebook, or Yahoo are presumably aware that there is an army of engineers who maintain and improve the systems infrastructure underlying the data science (e.g., data collection, ingest and organization; automated model retraining and deployment; monitoring and quality assurance; production experimentation). However if you've never worked at a startup then you aren't really aware of how much work all those people are doing to enable data science. If those functions become available as part of a service offering, than an individual data scientist with a hot idea has a chance of competing with the big guys. More realistically, given my experience at startups, the individual data scientist will have a chance to determine that their hot idea is not so hot before having to invest large amount of capital developing infrastructure :)
Of course there is a lot more that has to happen for “Machine Learning as a Service” to be fully mature, but this product announcement is a nice first step.