The Path to Enterprise Machine Learning

Just like any technical or business IT capability, one pre-requisite for adoption is understanding the WHAT and the WHY; and a clear definition of these aspects at the beginning of the Machine Learning journey is critical to charting the course.



WHAT: Understanding the end-goal
Figure 1 below captures the Machine Learning maturity levels as the organization evolves from a basic reporting/analytics capability, to having full-blown Machine Learning maturity. The illustration also outlines some of the technical ML capabilities achieved (2nd column titled ML Capabilities), the skills needed (3rd column), and the platforms/tools utilized (4th column) at each maturity-level. Each maturity-level (Initial, Subscriber, Model-Driven, and Publisher) is characterized by the specific business capability attained as a result (Descriptive, Cognitive, Predictive, and Prescriptive).

WHY:  Asking the Question
Some example questions that highlight ML requirements and use-cases might look like this:

Financial Services: How can we lower (and possibly eliminate) risk by identifying (or even predicting) fraudulent activity as soon as it occurs?

Manufacturing: How can we diagnose product quality issues and suggest assembly-line improvements and process optimizations while the manufacturing is still ongoing?

The end-to-end Machine Learning process comprises 4 steps: Acquire, Ingest, Model, and Consume. A brief description of each of these steps is included in the illustration.




https://www.linkedin.com/pulse/path-enterprise-machine-learning-adnan-hashmi