1. Wasting time

   Setting up your development environment, keeping data in sync across a bunch of machines, reproducing a colleague's result; lots of time is wasted in typical ML projects.

2. Not collaborating

   Data scientists often avoid collaborating because it's difficult with current tools, but the benefits of avoiding siloes are well documented.

3. Manual tracking

   If you are tracking results of experiments, or mappings from data to models in a spreadsheet, you're likely to make mistakes and not capture the full picture.

4. No reproducibility or provenance

   By overwriting data or emailing notebooks you're creating future risk of not being able to fix a model that is buggy.

5. No automated testing

   Without automated tests it's easy to accidentally deploy broken models into production, as described in What's your ML test score?

6. Snowflake deployments

   If you're manually deploying models into production by copying files, you risk losing track of what's running where and how it was created.

7. Unmonitored models

   If you're not testing model drift and statistical behavior in production, your models could go haywire and you might not notice until real damage has been done.

8. Technical debt

   As Google describes in The High Interest Credit Card of Technical Debt, ML systems are particularly susceptible to new and interesting forms of tech debt.

These issues make ML projects fail and create financial and reputational risk. Solving them promises more productive, effective AI teams and better and safer models.