Blog Posts: Methods & Practices

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

Read this article to learn how GenAI is reshaping data science teams, making them indispensable for an organization’s competitive advantage.

The reality is that machine learning is a fantastic tool for creating robust and easy-to-maintain solutions to simple problems. Read this article for a real-world example and lessons to learn.

In this running series, we’ll explore how analytics and AI are shaping the 2025 healthcare ecosystem, starting with a review of 2024 and key trends for 2025.

Read the key takeaways from IIA’s roundtable discussion on practical use cases and integration challenges of agentic AI.

For some business problems, the optimal solution is likely a combination of predictive and generative AI. This article explores when both technologies shine and use cases for a hybrid approach.

In our final piece in this series, we explore the scoping stage, where you define the project’s goals, assess feasibility, and determine the resources required to complete it successfully.

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

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.