Spark Sessions 001

Spark Sessions 001

The Spark Sessions are a recurring event of the Data Science Initiative at the Faculty of Computer and Information Science. They are designed to facilitate the exchange of ideas and inspiration. The event consists of several snappy (5-7 min) presentations, followed by a casual gathering with refreshments:

1. LLMDataForge: Framework that Leverages Large Language Models (LLMs) to Generate High-quality Datasets Tailored to Your Needs by Gal Petkovšek (Data Scientist at Medius) and Tadej Justin (Chief Data Scientist at Medius)
   This talk explores generating high-quality synthetic datasets using LLMs, utilising the LLMDataForge framework for filtering and prompt adjustments to enhance training smaller models for NLP tasks.
   
2. One Transformation to Rule Them All: Automated Search for Feature Transformations at Scale by Mark Žnidar (Data Science Intern at Outbrain)
   Automated feature search for field-aware factorization models, generating and evaluating many interactions. This method boosts efficiency and accuracy, enabling scalable, robust AutoML model search.
   
3. Generating Synthetic Relational Data by Valter Hudovernik (Data Science Student at FRI)
   An introduction to the emerging field of relational data generation, focusing on the strengths and limitations of current methods in preserving the characteristics of the original datasets.
   
4. Weighing in on Evaluating LLM System Performance by Greta Gašparac (Data Scientist at Sportradar)
   Since the release of ChatGPT, there has been a surge of interest in various LLM-powered solutions across industries. However, discussions on evaluating these systems deserve equal, if not greater, attention. In this talk, we explore the critical aspects of LLM system evaluation through a practical example of developing a customer support AI assistant.
   
5. Next-Generation AI for Intelligent Waste Management: Leveraging LLMOps and Semantic Entity Linking by dr. Stevanče Nikoloski (Head of Data & AI at Result)
   We present a next-gen intelligent waste management solution powered by AI, focusing on semantic entity linking. Using LLMOps, we enhance data collection and fine-tune our LLMs (Mistral, Zephyr, Llama2, Falcon), ensuring local infrastructure compatibility and data privacy. The architecture integrates vector databases with advanced chunking (map-reduce, refine) for applications like summarization, chatbots, and entity linking, enabling continuous improvement and adaptability in waste management.
   
6. Value-Based Conversion Tracking in Online Controlled Experiments: Frequentist vs. Bayesian Approach by Aljoša Vodopija (Data Scientist at Outbrain)
   This talk explores value-based conversion tracking in online controlled experiments, contrasting frequentist and Bayesian approaches to optimize revenue from differently valued conversions.
   
7. TBA Interested in filling this spot? !.
   
8. Snacks and drinks and discussions.

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Event Venue & Nearby Stays

Faculty of Computer and Information Science, 113 Večna pot, Ljubljana, Slovenia