If you’re about to dive into a machine learning or AI project, here’s a checklist for you to cover before you dive into algorithms, data, and engineering. Think of it as your friendly consultant-in-a-box.
Don’t waste your time on AI for AI’s sake. Be motivated by what it will do for you, not by how sci-fi it sounds.
This is a super-short version of my 18 minute monster Ultimate Guide to Starting AI. If you’re about to embark on ML/AI, here’s hoping you can answer “yes” to all of these questions.
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If you answer “no” to any of the checklist questions, this might be a portrait of your project.
Step 1 of ML/AI in 22 parts: Outputs, objectives, and feasibility
Correct delegation: Does the person running your project and completing this checklist really understand your business? Delegate decision-making to the business-savvy person, not the garden-variety algorithms nerd.
Output-focused ideation: Can you explain what your system’s outputs will be and why they’re worth having? Focus first on what you’re making, not how you’re making…
