I've been highly inactive for like a year it seems, but here is my input.
I am still 100% invested in Azure Data Warehouse and Azure Data Lake Store with Azure Data Lake Analytics.
Data -> Store -> Analytic Engine -> Warehouse -> SQL DB -> Power BI
When it comes to activity, got to remember, a lot that applies to Azure Data Warehouse also applies to SQL Server. There are a few differences in what is available between cloud and on-prem. But, there is many common things too. Thus, you see a lot of related questions that may be tied to on-prem, but actually equate to both.
For Azure Machine Learning, I have used it a lot. The main benefit of Azure Machine Learning is taking the ML out of your application (e.g.: hard coded) and putting it somewhere else where your app can interact with it via API's (or embedding it). When I went down the path of exploring Azure Machine Learning, I quickly realized I am not developing applications for my data. It's mostly for analytical and operational reporting use cases.
Many of the tools we use have ML features now. Power BI for example has plenty of ML features that do not require Azure ML to thrive on. Azure Data Lake Analytics (the analytics engine in my flow) also has ML features such as a couple of options to wrap or upload full ML modules/code as part of the U-SQL jobs. Again, not needing Azure ML to function.
Outside of that, I do love Azure ML. It allows for similar approaches that you may take with utilizing stored procs versus other approaches with your apps. Having the ML completely separate, outside of the raw code, allows for the data scientist to update and maintain that ML package easier. It's a really nice feature if you really want to enable your applications to have supervised or unsupervised learning on top of just providing ML to Excel and other apps your team may be using.
The only downside is extracting the coefficients seem non-existent with Azure ML, which can be a pain for the DS teams.