Introduction to the Microsoft AI Platform

I recently recorded an introduction to the Microsoft Artificial Intelligence suite of tools and services you can use in your organization, from what is already built  into Microsoft applications you own, through leveraging Cognitive Services, customizing AI, all the way through writing your own AI with Machine Learning, Deep Learning, and Neural Networks. I also…

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Azure Machine Learning and the Team Data Science Process – Part 1

The Team Data Science Process allows you to have a repeatable, controlled progression for analytics projects. You can use it with any Data Science technologies, and Microsoft has a full suite of products you can use for AI programming. Microsoft Azure Machine Learning Services have several components that assist in large-scale AI programming, Deep Learning,…

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The Microsoft Artificial Intelligence Landscape – And What to use When

Artificial Intelligence (AI), at its broadest definition, is simply “a machine that can act using human-style reasoning or perception”. Of course, the technologies used to enable that definition are far from simple themselves. Artificial Intelligence isn’t new – I worked with “Good Old Fashioned AI” (that’s a real thing) back in the late 70’s and…

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Data Science and Standard Patterns

In Data Science, the word “Pattern” has a specific meaning, involving the patterns that arise from data. This type of analysis is quite common in Data Mining and other technologies used by a Data Scientist. In IT practices such as systems architecture and software solutions design, the word “Pattern” has another definition, which is to…

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Analytics is Width. Feature Selection is Depth.

Most organizations don’t focus on Data Science or AI or Machine Learning as a single discipline – they group it together with the entire Analytics function. This includes everything from spreadsheets to Relation data, from documents stored in multiple locations to the structured business data in standard operations. While you might view your team independently,…

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The Keys to Effective Data Science Projects – Part 9: Testing and Validation

We’re continuing our discussion of the series of the Keys to Effective Data Science Projects,  this time focusing on Testing and Validating the Model. We’re in the general phase in the Team Data Science Process called “Customer Acceptance“. “Testing” in the general sense is the same in Data Science projects and any other typical software project – it’s ensuring…

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The Keys to Effective Data Science Projects – Part 8: Operationalize

We’re in part eight on our journey through the series of the Keys to Effective Data Science Projects –“Operationalization” – a term only a marketer could love. It really just means “people using your solution”. And it’s this part of the process that is quite possibly the most complicated, and usually the one done with the…

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The Keys to Effective Data Science Projects – Part 7: Create and Train the Model

We’re in part seven on our series of the Keys to Effective Data Science Projects.  This is the section that most people think of when they think of “Data Science”. It’s where we take the question, the source data which has been turned into the proper Features (and potentially Labels), and select an algorithm or two…

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The Keys to Effective Data Science Projects – Part 6: Feature Selection

We’re in part six on our series of the Keys to Effective Data Science Projects. I won’t cover basic Feature Engineering in this article – it’s a huge topic and central to working in Machine Learning areas. I do recommend you check out as many articles as you can find on the subject, and once you’ve grasped…

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The Keys to Effective Data Science Projects – Part 5: Update the Data

In this series on the “Keys to Effective Data Science Projects”, we’ve seen a process we can use, we’ve determined what we want to know, and we’ve ingested the data. In the last step we explored the data, and in a different way than we might be used to when working with in a database…

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