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Description

This workshop is designed to equip software engineers with the skills to build and iterate on generative AI-powered applications. Participants will explore key components of the AI software development lifecycle through first principles thinking, including prompt engineering, monitoring, evaluations, and handling non-determinism. The session focuses on using multimodal AI models to build applications, such as querying PDFs, while providing insights into the engineering challenges unique to AI systems. By the end of the workshop, participants will know how to build a PDF-querying app, but all techniques learned will be generalizable for building a variety of generative AI applications.

If you're a data scientist, machine learning practitioner, or AI enthusiast, this workshop can also be valuable for learning about the software engineering aspects of AI applications, such as lifecycle management, iterative development, and monitoring, which are critical for production-level AI systems.


Instructor's Bio

Hugo Bowne-Anderson, PhD

Independent Data and AI Consultant

Hugo Bowne-Anderson is an independent data and AI consultant with extensive experience in the tech industry. He is the host of [Vanishing Gradients](https://vanishinggradients.fireside.fm/) and [High Signal](https://high-signal.delphina.ai/), podcasts exploring developments in data science and AI. Previously, Hugo served as Head of Developer Relations at Outerbounds and held roles at Coiled and DataCamp, where his work in data science education reached over 3 million learners. He has taught at Yale University, Cold Spring Harbor Laboratory, and conferences like SciPy and PyCon, and is a passionate advocate for democratizing data skills and open-source tools. He also regularly teaches courses on [Building LLM Applications for Data Scientists and Software Engineers](https://maven.com/s/course/d56067f338).

Webinar

  • 1

    Workshop "Building LLM-Powered Applications for Data Scientists and Software Engineers"

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