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In Part 3 of “Machine Learning Lifecycle,” the author explores the nuts and bolts of data collection, from defining data sets to data splits.

Read below for a roundup of interesting sites, resources, and articles from around the web, curated and contextualized by unbiased analytics experts at IIA. Highlights include an article from HBR arguing for consolidation of tech-related C-suite roles, an article on resolving common AI pain points, and a webinar on fueling AI success with the right data. Follow us on Twitter and LinkedIn to receive daily updates on IIA content and curated content as it becomes available.

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.