04-28, 15:00–15:45 (America/Los_Angeles), Rainier
In this talk we will look at how to use the Open Source Libraries papermill, origami, and genai linking IPython with LLMs (such as GPT-X) to build data projects data from A to Z with natural language only.
In this talk will look at how to use the Open Source library papermill to link with Noteable's enterprise platform and iterate, refresh, and share data outcomes with rich visualizations against scaling sources. If you do any data engineering, or support data engineering efforts this talk will show some tools available in the market and how open source solutions can be adapted to make use of those capabilities.
If you do any data EDA, ETL, or ML this talk will show some tools available in the market and how open-source solutions can be adapted to use those LLMs capabilities within your own cloud-based Python workflows. By integrating these tools, we can create and automate data science workflows that can be used for analysis, modeling, and visualization. Papermill allows us to parameterize and execute Jupyter notebooks, while Origami provides an interface to interact with Noteable's APIs; and Genai is a Python library that allows us to interface with LLMs in IPython.
Noteable's api support enables easy adoption and familiarity with the common notebook patterns that power ETL orchestration, report sharing, and other data exploration problems. We'll walk through how we register into the papermill interface to control remote execution patterns with a single package install and an api token. We'll also walk through how this is used in integrations with several other data platforms to enable deep integrations between commonly used software companies. This unlocks the ability to reuse one's established technology while benefiting from Noteable's rich visualization capabilities and real time collaboration as well as allow for an ETL data debugging experience that's unmatched by any other combination of tools.
No previous knowledge expected
Pierre is a co-founder, CEO of Noteable. Pierre Brunelle led Amazon’s notebook initiatives both for internal use as well as for SageMaker. He also worked on many open source initiatives including a standard for Data Quality work and an open source collaboration between Amazon and UC Berkeley to advance AI and machine learning. Pierre helped launch the first Amazon online car leasing store in Europe. At Amazon Pierre also launched a Price Elasticity Service and pushed investments in Probabilistic Programming Frameworks. And Pierre represented Amazon on many occasions to teach Machine Learning or at conferences such as NeurIPS. Pierre also writes about Time in Organization Studies. Pierre holds an MS in Building Engineering from ESTP Paris and an MRes in Decision Sciences and Risk Management from Arts et Métiers ParisTech.