1. Do software engineering principles apply to Machine Learning development
2. How is an ML system different from traditional application?
3. How important is data versioning?
4. What are the next logical steps in the development of the data science
engineering tool chains?
5. How will the data ecosystem evolve over the next few years?
These are interesting times and the topics in question are extremely
important for anyone who's interested in the fields. As an alumnus of
Scribble Data, I've had a chance to witness first-hand the complexities
data - structured or unstructured - can pose and the way disciplined
engineering can make a difference in the workflow of an ML system.