Data scientists are getting automated faster than the rest of the engineers
Imagine that you are either * The Data Scientist, who is going to implement the solution, will require technical feasibility and complexity assessment like is the right data available? how inference will be integrated with the business process? and is the acceptance criteria achievable with the given data set and constraints?
For business-facing (B2B) AI products, it’s often difficult to get the data necessary to build a prototype because a lot of highly specialized data is locked up within the companies that produce it. There are a couple of general ways in which AI teams can get around this problem:
A list of key questions that will help asses business and technical feasibility of a machine learning solution Lets take an example of a machine learning and deep learning based forecasting solution. Imagine that you are the Machine learning engineer and want to asses a forecasting use case from two