{"categories":[{"label":"Database","url":"https://skillfed.io/packages/category/database/4"}],"enrichment":{"capability":"Deep Lake is a serverless database for storing, searching, and streaming multi-modal data (images, videos, text, embeddings) alongside vector search capabilities, designed for AI applications and deep learning workflows.","skillfed_tags":["vector-search","multi-modal-ai","rag-pipeline"],"use_cases":["Build RAG pipelines and LLM applications with vector search over embeddings and raw documents stored in one place.","Manage and version large datasets for training deep learning models with efficient streaming and lazy loading.","Store and search multi-modal datasets (images, videos, text) with instant visualization in the Deep Lake App.","Deploy serverless vector stores locally or in your own cloud without managing infrastructure.","Fine-tune large language models using Deep Lake's performant dataloaders for PyTorch or TensorFlow."],"what_it_does":"Deep Lake is a serverless, multi-cloud database optimized for storing and querying AI datasets. It handles raw data types\u2014images, videos, audio, text, embeddings, PDFs, and more\u2014in a single unified storage layer, with built-in vector search for retrieval-augmented generation and LLM applications. Unlike traditional vector databases that store only embeddings plus light metadata, Deep Lake stores the raw data itself in native compression, enabling lazy loading and efficient streaming to training pipelines.\n\nThe package integrates with popular ML frameworks and tools to simplify deployment of LLM-based products and deep learning workflows. It supports multi-cloud backends (S3, GCP, Azure, Activeloop cloud, local, in-memory) and includes dataset versioning, visualization, and dataloaders for common frameworks. Core dependencies are numpy, deepframe, and requests.","worth_installing":"Yes, with conditions. Deep Lake is actively maintained and well-suited for AI/ML workflows requiring multi-modal storage and vector search. However, install it only after verifying the license terms directly in the repository (metadata is unclear) and confirming Python version compatibility for your environment. Medium install friction is acceptable for projects that need its specific combination of raw data storage, vector search, and cloud flexibility."},"id":"deeplake","links":{"html":"https://skillfed.io/packages/deeplake","md":"https://skillfed.io/packages/deeplake.md","pypi":"https://pypi.org/project/deeplake/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-08","license_spdx":null,"license_treatment":"unclear","name":"deeplake","python_support":"unspecified","summary":"Data Lake for Multi-Modal AI Search"},"popularity":{"monthly_downloads":194602,"position":9827,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"4.7.1"}