How Developing AI Products is Different From Traditional Software
Technical Insights

How Developing AI Products is Different From Traditional Software

With the rise of software engineering over several decades, many principles of how to build traditional software products and businesses are clear. But the principles of how to build AI products and businesses are still developing.
2 min read
Ready, Set, Improve the Data!
Technical Insights

Ready, Set, Improve the Data!

I’m thrilled to announce the first data-centric AI competition! I invite you to participate.For decades, model-centric AI competitions, in which the dataset is held fixed while you iterate on the code, have driven our field forward. But deep learning
1 min read
Data-Centric AI Development: A New Kind of Benchmark
Technical Insights

Data-Centric AI Development: A New Kind of Benchmark

Benchmarks have been a significant driver of research progress in machine learning. But they've driven progress in model architecture, not approaches to building datasets, which can have a large impact on performance in practical applications.
2 min read
When Not to Use Machine Learning
Technical Insights

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.
1 min read
Data-Centric-AI Development: The Platform Approach
Technical Insights

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.
2 min read
Data-Centric AI Development, Part 3: Limit Data Collection Time
Technical Insights

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. But when you’re working on a novel application,
2 min read
Iteration in AI Development
Technical Insights

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, and use
2 min read
Data-Centric AI Development, Part 2: A Critical Shift in Perspective
Technical Insights

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.
2 min read
Five Steps to Scoping AI Projects
Technical Insights

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.
2 min read
Choose the Right Point On the Automation Spectrum
Technical Insights

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.
2 min read
A Different Approach to A/B Testing
Technical Insights

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.
2 min read
High Test-Set Accuracy Is Not Enough
Technical Insights

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.
2 min read
Don't Confuse Proof of Concept With Production Deployment
Technical Insights

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.
2 min read
Structured and Unstructured Data: Implications for AI Development
Technical Insights

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?
2 min read
Data-Centric AI Development: Small-Data Problems
Technical Insights

Data-Centric AI Development: Small-Data Problems

Over the last two weeks, I described the importance of clean, consistent labels and how to use human-level performance (HLP) to trigger a review of whether labeling instructions need to be reviewed.
2 min read

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