Alert door widget

When Not to Use Machine Learning

I decided last weekend not to use a learning algorithm. Sometimes, a non-machine learning method works best.Now that my daughter is a little over two years old and highly mobile, I want to make sure the baby gate that keeps her away from the stairs is always shut.
Pictures of Robert Crowe, Andrew Ng and Laurence Moroney (from left to right)

Introducing the Machine Learning Engineering for Production (MLOps) Specialization

So you’ve trained an accurate neural network model in a Jupyter notebook. You should celebrate! But . . . now what? Machine learning engineering in production is an emerging discipline that helps individual engineers and teams put models into the hands of users.
Machine Learning project lifecycle

Data-Centric-AI Development: The Platform Approach

It can take 6 to 24 months to bring a machine learning project from concept to deployment, but a specialized development platform can make things go much faster.My team at Landing AI has been working on a platform called LandingLens for efficiently building computer vision models.
Iteration workflow

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

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

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

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

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

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

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

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

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

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

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!"

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.

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