Iteration workflow
Letters

Data-Centric AI Development, Part 3: Limit Data Collection Time

How much data do you need to collect for a new machine learning project? If you’re working in a domain you’re familiar with, you may have a sense based on experience or from the literature.
Calendar pages flying away
Letters

Make Every Day Count

Last Sunday was my birthday. That got me thinking about the days leading to this one and those that may lie ahead.As a reader of The Batch, you’re probably pretty good at math. But let me ask you a question, and please answer from your gut, without calculating.
Lifecycle of an Machine Learning project
Letters

Iteration in AI Development

Machine learning development is highly iterative. Rather than designing a grand system, spending months to build it, and then launching it and hoping for the best, it’s usually better to build a quick-and-dirty system, get feedback...
Chart with percent trust in tech sector vs. business
Letters

Can Tech Regain the Public’s Trust?

Each year, the public relations agency Edelman produces a report on the online public’s trust in social institutions like government, media, and business. The latest Edelman Trust Barometer contains a worrisome finding...
Celebration of Coursera being a publicly listed company
Letters

Coursera Goes Public

I have a two-year-old daughter, and am expecting my son to be born later this week. When I think about what we can do to build a brighter future for our children, the most important thing is to create a foundation for education.
System explaining an AI system
Letters

Data-Centric AI Development, Part 2: A Critical Shift in Perspective

Earlier today, I spoke at a DeepLearning.AI event about MLOps, a field that aims to make building and deploying machine learning models more systematic. AI system development will move faster if we can shift from being model-centric to being data-centric.
Tents in the street
Letters

Privilege and Obligation

Over the past weekend, I happened to walk by a homeless encampment and went over to speak with some of the individuals there. I spoke with a homeless man who seemed to be partially speaking with me, and partially speaking with other people that I could not see.
Blue speaking bubble in front of dozens of other speaking bubbles
Letters

There’s No Substitute for Communication Skills

Engineers need strong technical skills to be successful. But many underestimate the importance of developing strong communication skills as well.
How to scope AI projects slide
Letters

Five Steps to Scoping AI Projects

One of the most important skills of an AI architect is the ability to identify ideas that are worth working on. Over the years, I’ve had fun applying machine learning to manufacturing, healthcare, climate change, agriculture, ecommerce, advertising, and other industries.
Process of diagnosing a medical patient slide
Letters

Choose the Right Point On the Automation Spectrum

AI-enabled automation is often portrayed as a binary on-or-off: A process is either automated or not. But in practice, automation is a spectrum, and AI teams have to choose where on this spectrum to operate.
A/B Test loop for building human insight
Letters

A Different Approach to A/B Testing

When a lot of data is available, machine learning is great at automating decisions. But when data is scarce, consider using the data to augment human insight, so people can make better decisions.
Speech bubble that says "It did well on the test set!"
Letters

High Test-Set Accuracy Is Not Enough

Over the last several decades, driven by a multitude of benchmarks, supervised learning algorithms have become really good at achieving high accuracy on test datasets. As valuable as this is, unfortunately maximizing average test set accuracy isn’t always enough.
The price of shares in video game retailer GameStop (NYSE: GME)
Letters

Trading on AI: The GameStop Phenomenon

The price of shares in video game retailer GameStop (NYSE: GME) gyrated wildly last week. Many people viewed the stock’s rapid ascent as a David-versus-Goliath story...
Slide that says "Proof of concept - production"
Letters

Don't Confuse Proof of Concept With Production Deployment

Last week, I talked about how best practices for machine learning projects are not one-size-fits-all, and how they vary depending on whether a project uses structured or unstructured data, and whether the dataset is small or big.
Table with information related to data and datasets
Letters

Structured and Unstructured Data: Implications for AI Development

Experience gained in building a model to solve one problem doesn’t always transfer to building models for other problems. How can you tell whether or not intuitions honed in one project are likely to generalize to another?

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