Nomic Atlas

Generative AI may be new, but we've been building for this day for the past 5 years. Deep Lake thus combines the power of both data lakes and vector databases to build and fine-tune enterprise-grade, LLM-based solutions, and iteratively improve them over time. Vector search does not resolve retrieval. To solve it, you need a serverless query for multi-modal data, including embeddings or metadata. Filter, search, & more from the cloud or your laptop. Visualize and understand your data, as well as the embeddings. Track & compare versions over time to improve your data & your model. Competitive businesses are not built on OpenAI APIs. Fine-tune your LLMs on your data. Efficiently stream data from remote storage to the GPUs as models are trained. Deep Lake datasets are visualized right in your browser or Jupyter Notebook. Instantly retrieve different versions of your data, materialize new datasets via queries on the fly, and stream them to PyTorch or TensorFlow.

The AI Knowledge Platform. The Pinecone Database, Inference, and Assistant make building high-performance vector search apps easy. Developer-friendly, fully managed, and easily scalable without infrastructure hassles. Once you have vector embeddings, manage and search through them in Pinecone to power semantic search, recommenders, and other applications that rely on relevant information retrieval. Ultra-low query latency, even with billions of items. Give users a great experience. Live index updates when you add, edit, or delete data. Your data is ready right away. Combine vector search with metadata filters for more relevant and faster results. Launch, use, and scale your vector search service with our easy API, without worrying about infrastructure or algorithms. We'll keep it running smoothly and securely.