Summer has certainly had its distractions – hope you have enjoyed yours!
I’m excited to be presenting again, this time at Devup 2017 Conference in October. This year it will be at a new location – the Saint Charles Convention Center. It’s a great venue (with decent WiFi – yeah!). It will be held at the same location used for STL SilverLinings Conference 2017 in May of this year, and am looking forward to returning there.
I’ll be presenting on Azure ML Studio – which continues to grow in its offerings and capabilities (now with neural nets)! The biggest challenge will be deciding how much ground can be covered in a little less than an hour <grin>. Click the title to this posting and tell me in the comments if you prefer presentations on a new area to briefly cover everything, or to only mention most features but go in more depth for the core features. I’m kind of torn as to which approach to take – so please provide me with your suggestions!
Azure ML Studio is very exciting. It allows organizations to quickly leverage the power of machine learning to derive value from their data and either enhance operations or improve decisions. The ability to easily generate web services that can be used with Excel or readily incorporated in custom applications or websites puts this capability readily in reach for reasonable costs. An analyst reviewing data for insights can use their Excel spreadsheet to get predictions, classifications, etc. using the models developed and run in Azure against large data sets. Functional programs can run real time on the fly analysis for everything from anomaly detection (like fraud) to classifications (risk, prospect, customer type, etc.) and predictions (future demand, value, or other numeric). This is a very flexible tool.
When presenting on such a topic – particularly in an introductory level presentation -there is a plethora of topics to discuss. Some include:
- Supported algorithms and models
- Data sources
- Data manipulation and cleansing
- Web Service generation and publishing
- R, SQL, and Python integration
- Data exports
- Full life cycle development of the solution
Not to mention just the general “how do you get started and use the thing”.
Share your ideas on what deserves the most attention given a relatively short period for presentation (an hour). I’d really like to hear your thoughts. And if you want to poke around on your own – check out http://studio.azureml.net! Nah, I don’t get any kickbacks for promoting this – I just think its a great tool.