| Summary: | With the popularity of GenAI increasing by the day, good data engineering is more essential than ever. AI is transforming industries, but without high-quality data, even the most sophisticated models and AI agents can go awry. Data engineering is the backbone of AI workloads, as data engineers maintain and implement the infrastructure that houses data required for model training, build data pipelines that ensure accurate and accessible data, and monitor and enable model deployment. Connect with leading-edge data engineering specialists to learn how to support AI and ML use cases, understand the emerging technologies you'll need to bridge the gap between data engineering and AI development, and pick up the latest best practices for dealing with common challenges and pitfalls. What you'll learn and how you can apply it Get an overview on how AI is bringing about a change in data engineering Learn how to use knowledge infrastructures and the Ontology Pipeline to build AI agents and systems Explore practical approaches for building streaming infrastructure and integrating AI agents using MCP Find out how to build scalable Gen AI pipelines with Spark NLP Discover the importance of open table formats, catalogs, and embedded systems as means for effective, governed Al development Understand the ethical considerations of working with data and AI Recommended follow-up: Read Fundamentals of Data Engineering (book) Read AI Engineering (book) Read AI Agents with MCP (book) Take Solving Data Preparation Tasks with AI (on-demand course) Take Generative AI for Data Management and Remediation (on-demand course).
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