Gauging how hard a project is, is important in
- 💸 setting freelancer rates
- 🤝 communicating with colleagues
- 📈 and stakeholders
Here's the system I use to classify ML tasks throughout my PhD at DTU and now at ECMWF.
🆓 Safecrackers rule
What's the first thing you do, when you crack a safe? You check if it's open.
For a machine learning project?
You check if it's been done before. Maybe someone posted it on @kaggle or @github.
Feasible in a company - not ideal for research.
🥉 Surrogate models
Sometimes you get lucky.
The data is fairly simple, but maybe the physics is expensive to calculate.
This is building a model of a well-understood process. That means we get great ground truth data and control.
🤖 The advantage is that it's faster!
🥈 Human out of the Loop
Use a machine learning model to perform a task that previously needed human intervention.
Example: Analysing x-ray images for specific diseases. Not possible for classic computers and needs radiologists.
🤖 Advantage is freeing up human 🧠 power.
🥇 Do the Impossible
Machine Learning has enabled applications that simply weren't possible.
DeepMind built a model to predict the weather in a few hours to the satisfaction of Met Office scientists!
What else is possible? Earthquake prediction? Folding proteins? ... wait.
🏆 How difficult are they?
🆓 Go for it! Just tweaking necessary.
🥉 Works with easy models, maybe even a random forest.
🥈 Harder to do. New technologies and probably some GPUs.
🥇 Extremely hard. Needs domain expertise and ML finesse.