- [Online] Polars for Data Analysis in PythonLink visible for attendees
Discover Polars, the high-performance DataFrame library revolutionizing data analysis in Python. Built on Rust, Polars offers unparalleled speed and efficiency, outperforming pandas, Dask, and even PySpark. Explore its innovative features like lazy evaluation, memory efficiency, and automatic multi-threading, designed to handle large datasets with ease.
In this session, you'll learn practical techniques for data manipulation and advanced transformations. We will demonstrate Polars' syntax and capabilities, making it accessible even if you’re new to Polars. Join us to elevate your Python data analysis to the next level.
This presentation covers:
- Section 1: What is Polars and how does it compare to pandas?
- Section 2: Getting Started with Polars in Python
- Section 3: Advanced Data Analysis with Polars
- Section 4: Should you switch to Polars?
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How to Join the Webinar
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You can join via your browser (no app download required). Use Chrome or Firefox. Pre-register for the webinar:
https://www.bigmarker.com/neo4j/Data-Umbrella-Webinar--------------------------------
Video Recording
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This event will be recorded and placed on our YouTube. We usually have it up within 24 hours of the event. Subscribe to our YT and set your notifications: https://www.youtube.com/c/DataUmbrella/----------------------------------------
Time
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16:00 UTC, 9am PT / 12pm ET/ 7pm EAT/ 9:30pm IST----------------------------------------
Additional Details
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Talk Level: Intermediate
Pre-reqs: Intermediate knowledge of Python and pandas
Prep Work: None
Resources: Polars documentation: https://docs.pola.rs/
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Connect with Data Umbrella
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We invite you to follow Data Umbrella on our social networking sites to keep up to date on the latest news. - [Online] RAGged Edge Box: A Personal AI-Powered Document Search SystemLink visible for attendees
One of the most popular embodiments of Generative AI are information retrieval (IR) augmented generation (RAG). Such systems use an information retrieval engine (based on semantic embeddings or keyword search) and then use a Large Language Model (LLM) to extract answers to a given query.
These systems require a large amount of computation and are usually implemented in the cloud which presents data privacy issues.
In this talk we will present The RAGged Edge Box project in which basic embedding systems and small local LLMs are packaged inside a multi-platform virtual machine (VirtualBox). The system provides a Web interface that runs locally and allows access to the RAG functionality in a completely private manner. The neural networks run on a ONNX runtime and do not require a GPU. RAG code is implemented in PHP and is easy to modify, requiring a much smaller execution environment than a Python alternative.
https://textualization.com/ragged/
Outline
- RAGged Edge Box demo
- AI Concepts (RAG, LLMs, Embeddings)
- RAG Concepts (IR, chunk, prompt)
- RAGged Edge Box (concept, advantages)
- RAGged Edge Box Architecture
- Enabling Technology Bits (ONNX, PHP Semantic Search)
- Extension Points
- VM Packaging
- RAGged Edge Box as a Platform
----------------------------------------
How to Join the Webinar
----------------------------------------
You can join via your browser (no app download required). Use Chrome or Firefox. Pre-register for the webinar:
https://www.bigmarker.com/neo4j/Data-Umbrella-Webinar--------------------------------
Video Recording
--------------------------------
This event will be recorded and placed on our YouTube. We usually have it up within 24 hours of the event. Subscribe to our YT and set your notifications: https://www.youtube.com/c/DataUmbrella/----------------------------------------
Time
----------------------------------------
16:00 UTC, 9am PT / 12pm ET/ 7pm EAT/ 9:30pm IST----------------------------------------
Additional Details
----------------------------------------
Talk Level: Beginner
Pre-reqs: None
Prep Work: None
Resources: https://textualization.com/ragged/
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Connect with Data Umbrella
----------------------------------------
We invite you to follow Data Umbrella on our social networking sites to keep up to date on the latest news.