News

EPFL Researchers Introduce MEMOIR: A Scalable Framework for Lifelong Model Editing...

AI Observer
News

How to Use python-A2A to Create and Connect Financial Agents with...

AI Observer
News

From Fine-Tuning to Prompt Engineering: Theory and Practice for Efficient Transformer...

AI Observer
News

Building High-Performance Financial Analytics Pipelines with Polars: Lazy Evaluation, Advanced Expressions,...

AI Observer
News

Building High-Performance Financial Analytics Pipelines with Polars: Lazy Evaluation, Advanced Expressions,...

AI Observer
News

Hugging Face partners with Groq for ultra-fast AI model inference

AI Observer
News

Ren Zhengfei: China’s AI future and Huawei’s long game

AI Observer
News

We’re expanding our Gemini 2.5 family of models

AI Observer
News

Gemini 2.5: Updates to our family of thinking models

AI Observer
News

When AIs bargain, a less advanced agent could cost you

AI Observer
News

AI copyright anxiety will hold back creativity

AI Observer

Featured

News

Building High-Performance Financial Analytics Pipelines with Polars: Lazy Evaluation, Advanced Expressions,...

AI Observer
News

Hugging Face partners with Groq for ultra-fast AI model inference

AI Observer
News

Ren Zhengfei: China’s AI future and Huawei’s long game

AI Observer
News

We’re expanding our Gemini 2.5 family of models

AI Observer
AI Observer

Building High-Performance Financial Analytics Pipelines with Polars: Lazy Evaluation, Advanced Expressions,...

In this tutorial, we delve into building an advanced data analytics pipeline using , a lightning-fast DataFrame library designed for optimal performance and scalability. Our goal is to demonstrate how we can utilize Polars’ lazy evaluation, complex expressions, window functions, and SQL interface to process large-scale financial datasets efficiently....