Blog

In Part 2 of “Machine Learning Lifecycle,” the author explores selecting and training models, with data-centric model development at its core.

In Part 15 of our series on data innovation in healthcare, the CRIO at Cincinnati Children’s explores the impact of biomedical research informatics on hospitals, researchers, and patients.

In this multi-part series, the author begins at the final stage of the machine learning lifecycle: deployment. Explore key challenges, deployment patterns, and degrees of automation.

IIA clients want to know more about agentic AI. This article elaborates on the multi-agent system and how it operates

Data literacy is not enough. We need data instrumentation. Read this article to rethink your approach to data literacy in an information economy.

Read the key takeaways from IIA’s roundtable discussion on how companies approached AI in 2024 and their key challenges moving forward.

Read key insights from IIA’s roundtable discussion on defining, measuring, and prioritizing enterprise AI initiatives.

In Part 14 of our series on data innovation in healthcare, Ryan Sousa examines how Children’s Minnesota accelerated their data and analytics capabilities and created a data-driven culture.