For these project management use cases, here are some types of machine learning to consider. 
And here is a specific type of ML model you could apply for each case #project
Here are examples of accessible environments for applying these ML methods
Recap:
1. Identify possible use cases
2. Map to business area/lifecycle
3. Narrow down to a generic ML method
4. Select a straightforward ML model
5. Choose a suitable model environment
Next up:
6. Choose a code library or algorithm to apply that model
7. Select data-set
1. Identify possible use cases
2. Map to business area/lifecycle
3. Narrow down to a generic ML method
4. Select a straightforward ML model
5. Choose a suitable model environment
Next up:
6. Choose a code library or algorithm to apply that model
7. Select data-set
Of course to get started, one also starts at 6 and 7, and works back up to suitable business use cases.
find a suitable code library for the model selected
Select a data-set, which is either one from the business or is a standard data-set / reference bench-mark / toy data that is enough to mock something up.
This gives something to play with, and get something to work. bit.ly/2OIcj4h has an AI canvas for planning development at high level, understanding purpose and constraints. Then there are plenty of ML workflows to provide structure for developing the prototype.
@threadreaderapp unroll
