Event
GenAI meetup - PyData x phospho x Artefact - Utrecht
Next level GenAI innovation to production: image interaction and quality metrics
We’d like to invite you to our meetup hosted at our office in Utrecht on August 7th, directly next to the Utrecht central train station! The meetup theme is centered around how to bring GenAI to production, but not just production, production to the next level. More specifically, our Artefact team will demonstrate how we use GenAI for the generation of customized, production-ready marketing images and the Phospho team will illustrate how to integrate robust ML best practices on scales of quality metrics for GenAI products.
Agenda
[Talk 1]: “GenAI Image Interaction: a next step beyond LLM text chatbots”
We’ve all experienced the capabilities of GenAI chatbots for data interaction. Now, it’s time to explore the new GenAI innovations that Artefact is developing. Discover how GenAI is transforming marketing by accelerating asset creation and reducing costs. In this talk, we’ll demonstrate how we’re using GenAI to generate customized, production-ready marketing images. You’ll also gain insights into automating the processes, enhancing efficiency for creativity based applications and learn about the quality metrics essential for monitoring and enhancing model performance.
[Talk 2]: “Emerging best practices in Analyzing Usage Patterns and Quantifying Quality Metrics for GenAI Products”
Discover how to apply machine learning (ML) emerging best practices to Generative AI (GenAI) applications, specifically focusing on Large Language Models (LLMs) and diffusion models. This talk targets ML engineers and developers aiming to enhance their GenAI products through a quantified evaluation of model quality and user interaction analysis. Learn to implement rigorous, measurable standards to improve and understand GenAI applications.
The rapid advancement in Generative AI technologies, including LLMs and diffusion models, has empowered ML engineers and developers to build new and powerful products. However, the integration of robust ML best practices into the development of these products is still nascent. This session aims to bridge that gap by introducing established methodologies from traditional ML to enhance the reliability and effectiveness of GenAI applications.
