diff --git a/content/posts/2024-07-29-ai-ml-lifecycle-models-versus-real-world-mess/ai-ml-lifecycle-steps.png b/content/posts/2024-07-29-ai-ml-lifecycle-models-versus-real-world-mess/ai-ml-lifecycle-steps.png new file mode 100644 index 000000000..8e0400def Binary files /dev/null and b/content/posts/2024-07-29-ai-ml-lifecycle-models-versus-real-world-mess/ai-ml-lifecycle-steps.png differ diff --git a/content/posts/2024-07-29-ai-ml-lifecycle-models-versus-real-world-mess/index.md b/content/posts/2024-07-29-ai-ml-lifecycle-models-versus-real-world-mess/index.md index 3d49b5144..62bfb8182 100644 --- a/content/posts/2024-07-29-ai-ml-lifecycle-models-versus-real-world-mess/index.md +++ b/content/posts/2024-07-29-ai-ml-lifecycle-models-versus-real-world-mess/index.md @@ -94,7 +94,7 @@ I like the simplicity of the diagram, but it's missing some arrows. For example, I went through a couple of iterations of refining this idea and landed on the following diagram: Each stage is a step up the AI/ML lifecycle stairs, but analysing the problems that arise can send you tumbling down. It's a bit like a game of [snakes and ladders](https://en.wikipedia.org/wiki/Snakes_and_ladders) that is not based purely on luck. -{{
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}} ## Experimentation versus productionisation: Why not both?