Python + DS fwdays'24 conference
2024-04-20
-- online --
as the organisers put it
Python + Data Science fwdays'24 is a conference for Python developers and Data Science engineers.
who it's for
Python developers, data science engineers, machine learning engineers, MLOps engineers, data engineers, platform engineers, site reliability engineers
topics
retrieval augmented generation with Qdrant; OpenAI embeddings usage; LangChain and LlamaIndex tooling; pandas ETL with Parquet on S3; Snowflake and Databricks pipelines; feature store and one-click MLOps; debugpy DAP with kubectl debug; Apollo Federation with Rust router; production ML pipelines with Jenkins; Stable Diffusion with LoRA/DreamBooth; federated learning with Flower/NVIDIA Flare; PyScript WebGL and three.js integration
| title | author | length |
|---|---|---|
| What Is a RAG System and How to Build One | Dmytro Spodarets | 44m |
Explains what Retrieval-Augmented Generation (RAG) is and how to build a RAG system using components like retrieval/indexing and LLMs. Compares MLOps practices… | ||
| Rethinking the Existing Data Ingestion and Processing Pipeline | Yehor Nazarkin | 46m |
A practical example of using pandas to implement an ETL pipeline: extracting data from multiple sources, applying validation, filtering and conversions… | ||
| How Preply Reduced ML Model Development Time from One Month to One Day | Yevhen Yevsiuhov | 29m |
A Preply senior engineer explains how a small team inherited an existing ranking model and reworked their ML development workflow so new model iterations now… | ||
| Debugging python applications inside k8s environment | Andrii Soldatenko | 34m |
Concrete recipes and tools replace platitudes: run a debug server with debugpy + DAP, build images with an entrypoint toggle and baked git SHA, and attach from… | ||
| Distributed Graphs and Microservices at Prom.ua | Maksym Kindritskyi | 36m |
Prom.ua migrated from a monolith to a microservices architecture using GraphQL; the talk explains the migration path, how they integrated distributed graphs… | ||
| ML in Production | Oleksandr Bahan | 44m |
A concrete account of shipping ML: a COVID forecasting pipeline for HHS that predicts multiweek peaks for 3,000 locations by running daily full retrains and… | ||
| GenAI Apps: Our Journey from Ideas to Production Excellence | Danilo Topchiy | 37m |
A technical founder describes building and scaling GenAI-centric applications beyond simple GPT wrappers, sharing hands-on experience taking projects from 0 to… | ||
| Federated Learning: So Close and Yet So Out of Reach | Oleksandr Lapshyn | 31m |
Federated learning is presented as an algorithmic approach to training models on private data, with technical details demonstrated using NVIDIA FLARE. The talk… | ||
| Forcing unittest to function | Brandon Rhodes | 33m |
Claims that unittest can accept plain test functions by using Python introspection and shows two concrete implementations: one building test classes… | ||
| Subclassing vs Composition: A Survey of Trade-offs in Python | Khainek Shlavak | 41m |
Examines trade-offs between subclassing and composition in Python, naming different forms of subclassing and patterns used in the language. Uses Python… | ||
| Intro to WebGL with Python examples | Łukasz Langa | 41m |
Shows a working pipeline: initialize raw WebGL with PyScript, compile and load GLSL shaders, feed vertex buffers and render a triangle, then animate… | ||
| Large Language Models for Python Specialists | Oleksandr Krakovetskiy | 56m |
Demonstrates how large language models (ChatGPT, Gemini, Claude, Microsoft/GitHub Copilot) can automate data processing for Python engineers, covering features… | ||
| nothing matches | ||