overview
In the third post we completed a sample end to end analytics workflow using python, ml flow databricks and a few basic aws tools. However, because databricks is spark based,
recalling part 3 limitations
part 4 - preferred stack
- python + dask - language of choice; package for improving compute
- github - version control
- github actions - ci
- s3 - distributed storage
- ec2 - distributed compute
- flask - python api framework
- mlflow - ml lifecycle management
- docker - container service
- power bi - visualization
general flow
rationale for changes”
- automated model integration
known limitations
notes