PyData Berlin 2025 February Meetup

Welcome to the PyData Berlin January meetup!

Please provide your first and last name, current role and organization name for the registration because this is required for the venue's entry policy. If you cannot attend, please cancel your spot so others are able to join as the space is limited.

We would like to welcome you all starting from 18:45. There will be food and drinks. The talks begin around 19.30 and the doors will close at 19:30. Make sure to arrive on time!

Please provide your first and last name for the registration because this is required for the venue's entry policy. If you cannot attend, please cancel your spot so others are able to join as the space is limited.

Host:
Google is excited to welcome you to this month's version of PyData.
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The Lineup for the evening

Talk 1: Vector Streaming: Memory-efficient Indexing to VectorDB
Abstract: Embedding creation is mostly done synchronously; a lot of time is wasted while the chunks are being created, as chunking is not a compute-heavy operation. As the chunks are being made, passing them to the embedding model would be efficient. This problem further intensifies with late interaction embeddings like CoLBert or ColPali.
The solution is to create an asynchronous chunking and embedding task. We can effectively spawn threads to handle this task using Rust's concurrency patterns and thread safety. This is done using Rust's MPSC (Multi-producer Single Consumer) module, which passes messages between threads. Thus, this creates a stream of chunks passed into the embedding thread with a buffer. Once the buffer is complete, it embeds the chunks and sends the embeddings back to the main thread, where they are sent to the vector database. This ensures no time is wasted on a single operation and no bottlenecks. Moreover, only the chunks and embeddings in the buffer are stored in the system memory. They are erased from the memory once moved to the vector database.
All this is then bound into Python using pyo3 and maturin, so it's easily accessible from Python, but the core is still asynchronous with Rust.

Speaker: Sonam Pankaj
Bio: Sonam is the Generative AI Evangelist at Articul8. She is also the co-creator of EmbedAnything, a Rust-based library that streamlines ingestion, inference, and indexing and provides you blazing speed in your genAI pipeline. She has previously worked as an AI researcher at Saama and has worked in clinical trials. Her work has been published in the ACL Anthology, and she is passionate about speaking at developer conferences and mentoring the tech community.

Talk 2: Fine-Tuning SLMs like Phi-3x-Vision: Insights from Scene Analysis in ADAS
Abstract: Fine-tuning AI models isn’t just for big research labs—it’s a powerful way to address specific real-world problems and improve model performance. In this talk, Amit will discuss the key aspects of fine-tuning small language models (SLMs) like Phi-3x-vision and how one can setup fine-tuning workflow using Hugging Face libraries. He’ll outline key considerations, approaches, essential components, and tools that simplify the process.
Using an Advanced Driver Assistance Systems (ADAS) use case, he will demonstrate how fine-tuning helped improve anomaly detection in road scene analytics. The session will cover practical challenges, lessons learned, and broader takeaways—providing a clear understanding of how fine-tuning can be applied across different domains. Whether you’re a data scientist, ML engineer, developer, or just curious about AI customization, this talk will offer valuable insights into making models more effective for specialized tasks.

Speaker: Amit Tyagi
Bio: Amit is a Lead Applied Scientist at Microsoft Healthcare and Life Sciences, leading the EU expansion of DAX Copilot. He focuses on developing and optimizing AI models for healthcare, ensuring they align with regulatory and operational needs in the region. Before this, he worked in the customer-facing Enterprise team on various AI-driven solutions, including an autonomous driving project where he fine-tuned vision and language models for scene analytics. He also contributed to AI projects in forecasting, retrieval-augmented generation (RAG) chatbots, and other domain-specific SLM fine-tuning efforts.

Lightning talks
There will be slots for 2-3 Lightning Talks (3-5 Minutes for each) between the two main talks.
Kindly let us know if you would like to present something :)

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