AI

2024

Complex Machinery 003: It's a bot's world, we just live in it


Complex Machinery 002: It's still a wild animal


Complex Machinery 001: Nothing's real but the fakery


Introducing: Complex Machinery / Too much time in the wilderness


Three alternatives to developing a public-facing AI chatbot


2023

A reminder that (AI-Driven) Content Moderation Is Hard


What LLM chatbots teach us about AI in general


The Getty generative AI bot and lawsuit-free datasets


New Radar article: "Structural Evolutions in Data"


Four antipatterns when hiring your first data scientist


Risk management for AI chatbots


AI isn't something you just add to a company


When generated images take on a life of their own


Same name, new face for AI


Congratulations, you are now a data company


Some thoughts on generative AI


The top failure modes of an ML/AI modeling project (Part 2)


New Radar article: Automated machine learning (autoML)


The top failure modes of an ML/AI modeling project (Part 1)


When your ML model is living in the past


2022

When companies ask me: "What should we do with our data?"


ML for Executives, Part 1: High-Dimensional Pattern Matching


Not All Datasets Are Created Equal


The Top Sources of Risk Facing the AI Sector


No Silver Bullets


360-Degree ML/AI


2021

New DSS Podcast episode: Spatial Data and R&D Projects


My take on the Zillow Offers shutdown


New Radar Article: "Remote Teams in ML/AI"


New DSS Podcast episode: Uncertainty in AI Product Management


Reducing Risk in Building ML/AI Models


Human/AI Interaction: Exoskeletons, Sidekicks, and Blinking Lights


Periods, Question Marks, and now Ellipses: The Punctuation Marks of Data Analysis


Business Stakeholders: Three Questions to Improve Your Communications With Data Scientists


When "Constants" ... Aren't


Data Scientists: Four Questions to Improve Your Stakeholder Communications


New Radar article: "Rebranding Data"


Preparing your Chief Data Officer (CDO) for Success


AI-based Automation: Ways to Mix Human and Machine


Our Maturing Expectations of AI


Towards Quantification: Finding Hard and Soft Numbers In Your Business


Are We In An AI Bubble?


Lessons Learned from an AI Submarine


The Lemonade Lesson


Undervalued Practices in ML/AI: Conclusion


Undervalued Practices in ML/AI, Part 4: Project Execution


Undervalued Practices in ML/AI, Part 3: Planning Projects


Undervalued Practices in ML/AI, Part 2: Hiring and Team Structure


Undervalued Practices in ML/AI, Part 1: Getting Started


The Lifecycle of an ML/AI Model


The Importance of Simulating Data


Are You Using ML/AI for Automation? or for Innovation?


Question Marks and Periods in the World of Data


2020

Competitive Advantage in ML/AI


Treating Your ML/AI Projects Like A Stock Portfolio


Misuse of Models: Recent Facial Recognition Failures


Setting Expectations for ML/AI Projects


TCM: Total Cost of (ML/AI) Model


Misuse of Models: IB predicting test scores


Data Lessons from the World of Algorithmic Trading (part 9): "Analyze Your Performance"


Data Lessons from the World of Algorithmic Trading (part 8): "Develop Controls Around Data"


Data Lessons from the World of Algorithmic Trading (part 7): "Monitor Your Risk"


Providing Padding Around ML/AI Models


Data Lessons from the World of Algorithmic Trading (part 6): "Monitor Your Models"


Data Lessons from the World of Algorithmic Trading (part 5): "Monitor Your Data Feeds"


Data Lessons from Algorithmic Trading (part 4): "Develop a Solid Data Infrastructure"


Data Lessons from Algorithmic Trading (part 3): "Think In Terms of Experiments"


Data Lessons from Algorithmic Trading (part 2): "Know Your Objective"


Data Lessons from Algorithmic Trading (part 1): Introduction


A Risk Assessment for Your ML/AI Job Security


Identifying and Handling Risks in AI Businesses


The ML/AI Reality Check


Why don't we talk more about risk in AI?


Academic Qualifications for Data Science Professionals


What should my company do with its data?


2019

How Do I Get More Data?


How Much Data Is Enough?


How to Prepare for That Data Scientist Job Interview


Data Ethics for Leaders: A Risk Approach (Part 5)


Data Ethics for Leaders: A Risk Approach (Part 4)


Data Ethics for Leaders: A Risk Approach (Part 3)


Data Ethics for Leaders: A Risk Approach (Part 2)


Data Ethics for Leaders: A Risk Approach (Part 1)


2018

Business Intelligence: A First Step to Data Science


Common Mistakes in Data Science Hiring : Part 2


Common Mistakes in Data Science Hiring : Part 1


2016

Data Science Hiring as a Sales Process


The Importance of Data Infrastructure


What is a data strategy, and why do I need one?


2015

Hiring on Your Analytics Team


Roles on Your Analytics Team


How Do You Know If Your Company Needs Hadoop?


2014

Good Use of Your Customer Data