Applying machine learning in test data analytics has been researched for many years. In the field of semiconductor test, data seems to be abundant and opportunities to take advantage of modern ML technologies seem to be many. Nonetheless, we have not observed a similar level of adoption of modern ML in our industry as that in applications related to computer vision and language understanding. In view of this gap, this talk discusses promises and barriers for realizing a ML solution in test data analytics. To overcome the potential barriers, this talks advocates taking a top-down approach that starts with questions at the operation intelligence level and then sketches a system to provide a specification for the required machine learning services. It is after such a system view is specified can one better understands what ML components are needed and whether they can be realized with the current ML technologies and the available data. Practical examples and experiment results are used to illustrate the top-down approach and its key considerations.