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Photo by Sam Moqadam on Unsplash

Getting Started

How do the principles of Lean Software Development apply to Machine Learning projects? Let’s take a look!

Introduction

  • Eliminate waste
  • Build quality in
  • Accelerate learning
  • Defer decisions
  • Deliver fast
  • Empower your team
  • Optimize the whole

Get your board going


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Photo by Stefano Pollio on Unsplash

Similar to Docker containers, kernels are the key to making sure your ML application can run anywhere.

Introduction


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Photo by Charles Etoroma on Unsplash

It’s easy to get lured into doing an endless stream of shallow ML models — don’t shy away from the deep end of the pool

Introduction


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Photo by Thomas Vimare on Unsplash

Model selection can make or break an ML project

Introduction

Stages of model selection

  • How would I do this by hand?
  • Workflow and steps
  • Inherent challenges


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Photo by Michael Dziedzic on Unsplash

How does Machine Learning (ML) fit into the wonderful world of Artificial Intelligence (AI)?

Artificial intelligence (AI)

John Mackie

Seasoned software architect & developer who thrives in complex business domains. Specializes in projects involving machine learning technologies.

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