Struggled to get in time to read up on AI today but watched a few videos on the train from the “AI for Everyone” course.
Key takeaways:
- The workflow of every good machine learning project has 3 broad steps:
- (1) Collect the data – Not all data is created equal. You can have garbage in, garbage out! So it’s important to determine what you think will be useful data.
- (2) Train the model – Lots goes into this process. It’s part maths/science and part art/craft.
- (3) Deploy & Iterate – Most models don’t work perfectly from the get go. You learn new things when you deploy the model in new areas, and can use those learnings to collect new data, and repeat the process by going back to step 1.