Google has introduced LangExtract, an open-source Python library designed to help developers extract structured information from unstructured text using large language models such as the Gemini models. The library simplifies the process of converting free-form text into structured data, offering features like controlled generation, text chunking, parallel processing, and integration with various LLMs.
This article describes a workflow using Large Language Models (LLMs) to automate the process of normalising spreadsheet data, making it tidy and machine-readable for easier analysis and insights.
An article detailing how to build a flexible, explainable, and algorithm-agnostic ML pipeline with MLflow, focusing on preprocessing, model training, and SHAP-based explanations.
An article discussing a simple and free way to automate data workflows using Python and GitHub Actions, written by Shaw Talebi.
- standardization, governance, simplified troubleshooting, and reusability in ML application development.
- integrations with vector databases and LLM providers to support new applications -
provides tutorials on integrating
- integrations with vector databases and LLM providers to support new applications -
provides tutorials on integrating
Notebooks are not enough for ML at scale
