Learn about the analytic journey that Data Scientists and IT take to build and deploy their models into production from this new article on InfoWorld from IDG, Open Data Group CTO, Stu Bailey. Stu will discuss the analytic journey and its different stopping points. Each component within this journey is filled with continuous challenges and new learning opportunities.
We like to call these components, “phases”. After breaking down the analytic journey, we have identified five main phases along with their requirements and complications. These phases are:
Phase 1: Define the Problem
Phase 2: Data Collection
Phase 3: Building Your Model
Phase 4: Training and Testing Your Model
Phase 5: Deploying Your Model
While each phase may seem self-explanatory and simple enough, it is crucial that each component is completed as best as possible to ensure a lack of errors. Any error along the way can inhibit further progress made to your models and create more frustrations.
This article will not only give you best practices to completing each step along your analytical journey, but it will also highlight a few of the newest trends that many Data Scientists and IT have been implementing in their journey as well!