Python Library - Deep Experimentation & Flexibility for Data Teams:
The Relevance AI Python SDK/Library is designed for Data Science and Machine Learning practitioners to customize and interact with Relevance's API deeply.
🌎 80% of data in the world is unstructured in the form of text, image, audio, videos, and much more.
Relevance AI unlocks the value of unstructured data by helping you:
- ⚡ Quickly analyze unstructured data with pre-trained machine learning models in a few lines of code.
- ✨ Visualize your unstructured data. Text highlights from Named entity recognition, Word cloud from keywords, Bounding box from images.
- 📊 Create charts for both structured and unstructured.
- 🔎 Drilldown with filters and similarity search to explore and find insights.
- 🚀 Share data apps with your team.
- 🔑 Vectors, storing and querying vectors with flexible vector similarity search, that can be combined with multiple vectors, aggregates and filters.
- 🔮 ML Dataset Evaluation, for debugging dataset labels, model outputs and surfacing edge cases.
- Interact with the API.
- Asynchronous data ingestion, transformation and retrieval.
- Insert data from your pandas dataframe, csv, numpy arrays, local file systems.
- Out-of-the-box integration with popular libraries like HuggingFace, Tensorflow Hub, etc.
- Dynamically deploy and edit data apps.
For SDK Reference, it is hosted at: https://relevanceai.readthedocs.io/en/latest/guides/index.html
Updated 3 months ago