Developing AI Products Part 5: Data Drift, Concept Drift, and Other Maintenance Issues
In earlier letters, I discussed some differences between developing traditional software and AI products, including the challenges of unclear technical feasibility, complex product specification, and need for data to start development.
Developing AI Products Part 4: Getting Data To Start Development
In a recent letter, I mentioned some challenges to building AI products. These problems are distinct from the issues that arise in building traditional software. They include unclear technical feasibility and complex product specification.
Developing AI Products Part 1: How AI Product Development 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.
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.
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.
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.
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