IDC predicts that up to 88 percent of all AI and ML projects will fail during the test phase[1]. A major reason is that AI solutions are difficult to maintain. In this
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
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
