Viewing data governance as a tactical decision layer can help us understand the interplay between data strategy and data management. Ensure your strategic goals are translated into actionable operations.
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.
Building on our four-part series on the machine learning lifecycle, this article captures another data scientist’s take on the six critical steps in creating good data science products.
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 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.
Accelerate Your Data Innovation Journey in Healthcare
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.