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Creating an End-to-End Machine Learning Application
A complete, end-to-end ML application, implemented in both TensorFlow 2.0 and PyTorch.
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Building Machine Learning Products: A Problem Well-Defined
In this post, we'll dig deeper into how to develop the requirements for a machine learning project when you're given a vague problem to solve.
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Data Project Checklist
There’s a lot more to creating useful data projects than just training an accurate model!
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Battle-Tested Techniques for Scoping Machine Learning Projects
One of the challenges of managing an ML project is project scoping. Even small changes in data or architecture can create huge differences in model ...
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Checklist for debugging neural networks
Tangible steps you can take to identify and fix issues with training, generalization, and optimization for machine learning models.
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Organizing Machine Learning Projects Project Management Guideline
The goal of this document is to provide a common framework for approaching machine learning projects that can be referenced by practitioners.
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Hidden Technical Debt in Machine Learning Systems
Using the software engineering framework of technical debt, we find it is common to incur massive ongoing maintenance costs in real-world ML systems.
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