Actual Intelligence

Data Science and AI from the LearnAI Team at Microsoft

LUIS: Notes from the Field of Natural Language Processing

I'm Anna Thomas, an Applied Data Scientist within Microsoft’s AI Engineering team. My focus the past...

Author: BuckWoody Date: 10/30/2018

I recently attended, presented, worked and did interviews at the Microsoft Ignite 2018 Conference in...

Author: BuckWoody Date: 10/02/2018

Applied AI - Using a Bot for Password Reset

Learn AI Team - Rodrigo Souza The topic today is how to use a bot to optimize processes within a...

Author: BuckWoody Date: 09/25/2018

The Data Analysis Maturity Model – Level Three: Distributed, consistent reporting systems

I’m covering a series of data analysis maturity levels, which are essential to performing Advanced...

Author: BuckWoody Date: 06/28/2018

The Data Analysis Maturity Model – Level Two: Reliable Data Storage and Query Systems

I'm in a series defining a Data Analysis Maturity Model. In the first level, I described the...

Author: BuckWoody Date: 06/20/2018

The Data Analysis Maturity Model – Level One: Data Collection Hygiene

Data Science and Advanced Analytics are umbrella terms that usually deal with predictive or...

Author: BuckWoody Date: 06/05/2018

Introduction to the Microsoft AI Platform

I recently recorded an introduction to the Microsoft Artificial Intelligence suite of tools and...

Author: BuckWoody Date: 03/27/2018

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...

Author: BuckWoody Date: 02/27/2018

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...

Author: BuckWoody Date: 02/22/2018

Data Science and Standard Patterns

In Data Science, the word “Pattern” has a specific meaning, involving the patterns that arise from...

Author: BuckWoody Date: 02/15/2018

Analytics is Width. Feature Selection is Depth.

Most organizations don't focus on Data Science or AI or Machine Learning as a single discipline -...

Author: BuckWoody Date: 01/31/2018

DevOps for Data Science – Load Testing and Auto-Scale

In this series on DevOps for Data Science, I’ve explained the concept of a DevOps “Maturity Model” –...

Author: BuckWoody Date: 01/24/2018

DevOps for Data Science – Application Performance Monitoring

In this series on DevOps for Data Science, I’ve explained the concept of a DevOps “Maturity Model” –...

Author: BuckWoody Date: 01/18/2018

DevOps for Data Science – Release Management

In this series on DevOps for Data Science, I’ve explained the concept of a DevOps “Maturity Model” –...

Author: BuckWoody Date: 01/11/2018

DevOps for Data Science – Continuous Delivery

In this series on DevOps for Data Science, I’ve explained the concept of a DevOps “Maturity Model” –...

Author: BuckWoody Date: 01/04/2018

DevOps for Data Science - Automated Testing

I have a series of posts on DevOps for Data Science where I am covering a set of concepts for a...

Author: BuckWoody Date: 12/27/2017

DevOps for Data Science – Continuous Integration

In the previous post in this series on DevOps for Data Science, I covered the first the concept in a...

Author: BuckWoody Date: 12/07/2017

DevOps for Data Science – Infrastructure as Code

In the previous post in this series on DevOps for Data Science, I explained that it’s often...

Author: BuckWoody Date: 11/30/2017

DevOps for Data Science - DevOps Maturity

In this series on DevOps for Data Science, I've explained what DevOps is, and given you lots of...

Author: BuckWoody Date: 11/22/2017

DevOps for Data Science – DevOps isn’t the Toolchain (But you still have to care about the tech)

In this series on DevOps for Data Science, I’m covering the Team Data Science Process, a definition...

Author: BuckWoody Date: 11/16/2017

DevOps for Data Science – Defining DevOps

I’m wading into treacherous waters here. Computing terms often defy explanation, especially newer...

Author: BuckWoody Date: 11/09/2017

DevOps for Data Science – Who needs it?

Data Scientists have often worked in a bit of a “silo” – meaning they were off to the side in an...

Author: BuckWoody Date: 11/02/2017

The Keys to Effective Data Science Projects – Part 10: Project Close-Out with the TDSP

Data Science projects have a lot in common with other IT projects in general, and with Business...

Author: BuckWoody Date: 10/26/2017

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...

Author: BuckWoody Date: 10/24/2017

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...

Author: BuckWoody Date: 10/24/2017

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...

Author: BuckWoody Date: 10/24/2017

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...

Author: BuckWoody Date: 10/24/2017

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,...

Author: BuckWoody Date: 10/24/2017

The Keys to Effective Data Science Projects – Part 4: Explore the Data

We’re in a series on the “Keys to Effective Data Science Projects”. We’ve identified the question we...

Author: BuckWoody Date: 10/24/2017

The Keys to Effective Data Science Projects – Part 3: Import the Initial Data

We’re in a series on the “Keys to Effective Data Science Projects” – and we’ve completed the hardest...

Author: BuckWoody Date: 10/24/2017

The Keys to Effective Data Science Projects – Part 2: The Question

In a previous post I explained where to start in a Data Science Project. I’ve also given you a...

Author: BuckWoody Date: 10/24/2017

The Keys to an Effective Data Science Project – Part 1: The Team Data Science Process

Processes are great things. They give you a place to start, a roadmap, and a way to explain to your...

Author: BuckWoody Date: 10/24/2017

Data Science: Start at the very Beginning, It’s a very good place to start

I have found that Data Science projects often deal with two types of clients: One of whom...

Author: BuckWoody Date: 10/24/2017

Does Data Science Replace BI?

When a new technology comes around, there’s a temptation to think that it replaces a previous one....

Author: BuckWoody Date: 10/24/2017

Seatbelt Learning with Uncle Buck

I travel a great deal for my work. For instance, in the next two weeks alone, I’ll travel from...

Author: BuckWoody Date: 10/24/2017

A Quick Start for Data Science

I’ve covered the Data Science Body of Knowledge here on this blog, and there’s quite a bit there to...

Author: BuckWoody Date: 10/24/2017

Simplifying (?) Artificial Intelligence

Artificial Intelligence is a vastly complicated topic. It’s not just that it has a lot of components...

Author: BuckWoody Date: 10/24/2017

A Data Science Microsoft Project Template You Can Use in Your Solutions

In most of the Data Science and AI articles, blogs and papers I read, the focus is on a particular...

Author: BuckWoody Date: 10/24/2017

What is the main difference between Business Intelligence and Data Science?

I’ve explained in other articles that Data Science is not a replacement for other data technologies,...

Author: BuckWoody Date: 10/24/2017

Build the House Around the Plumbing

There’s a show on US television called “This Old House“, where a team of craftsmen fix up older...

Author: BuckWoody Date: 10/24/2017

Data Wrangling – Regular Expressions

The first step of deep analysis using the Team Data Science Process is to find the right question....

Author: BuckWoody Date: 10/24/2017

Learning on the Move

Probably the most interesting reactions I get (and that’s saying a lot) is when I tell people that I...

Author: BuckWoody Date: 10/24/2017

Data Wrangling – ELT not ETL

In Data Science, we most often use Extract, Load and Transform (ELT) as opposed to Extract,...

Author: BuckWoody Date: 10/24/2017

Hello Buck – I’d like to work at Microsoft

About once a week or so, after teaching a Data Science class or at an event, I’ll get asked “Hey –...

Author: BuckWoody Date: 10/24/2017

Enabling the Linux Susbsystem in Windows 10

If you’ve upgraded to the Windows 10 Anniversary Edition (writing this as of 2016), you have a new...

Author: BuckWoody Date: 10/24/2017

The Inherent Insecurity of Data Science

Data Science attempts to derive meaning from data. There are a lot of techniques, processes and...

Author: BuckWoody Date: 10/24/2017

Don’t learn to be a Data Scientist

It’s my custom to learn continuously – I keep a technical book, course, or class going at all times,...

Author: BuckWoody Date: 10/24/2017

Is the Microsoft R Client a…client?

Microsoft has recently been on a tear introducing R into, well, everything. And now there are...

Author: BuckWoody Date: 10/24/2017

The Microsoft Business Analytics and AI Platform - What to Use When

I’m just back from trips around the U.S. and Europe teaching Data Scientists and Data Architects how...

Author: BuckWoody Date: 10/24/2017

The Importance of Unit of Analysis

There are two defining difference between Data Science (or Data Mining for that matter) and other...

Author: BuckWoody Date: 10/24/2017

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