Where are you in your analytic journey?


Article: The secrets to successfully accomplishing each step in your analytic journey

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!


You might also enjoy

Introducing Enterprise Safeguards for Generative AI

ModelOp released version 3.2, which includes cutting-edge capabilities to govern and monitor Large Language Models (LLMs) and Generative AI — including internal and third-party models — helping de-risk enterprises while delivering value-generating AI at scale.

Get the Latest News in Your Inbox

Further Reading