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
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
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
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?
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)
Business Intelligence: A First Step to Data Science
Common Mistakes in Data Science Hiring : Part 2
Common Mistakes in Data Science Hiring : Part 1
Data Science Hiring as a Sales Process
The Importance of Data Infrastructure
What is a data strategy, and why do I need one?
Hiring on Your Analytics Team
Roles on Your Analytics Team
How Do You Know If Your Company Needs Hadoop?
Good Use of Your Customer Data