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
Microsoft Ignite 2018 Links to Training
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