Audience
Companies that need an advanced Data Mapping solution
About Nomic Atlas
Atlas integrates into your workflow by organizing text and embedding datasets into interactive maps for exploration in a web browser. You shouldn’t have to scroll through Excel files, log Dataframes and page through lists to understand your data. Atlas automatically reads, organizes and summarizes your collections of documents surfacing trends and patterns. Atlas’ pre-organized data interface allows you to quickly surface pathologies and dirty data that can jeopardize your AI projects. Label and tag your data while you clean it with immediate sync to your Jupyter Notebook. Vector databases enable powerful applications such as recommendation systems but are notoriously hard to interpret. Atlas stores, visualizes and lets you search through all of your vectors in the same API.
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AtlasMap is a data mapping solution with interactive web based user interface, that simplifies configuring integrations between Java, XML, CSV and JSON data sources. You can design your data mapping with AtlasMap Data Mapper UI canvas, and then run that data mapping via runtime engine. In addition to plain Java API provided by runtime engine, camel-atlasmap Component is also available to perform data mapping as a part of Apache Camel route. There is also a Camel Quarkus extension available. The easiest way to use AtlasMap Data Mapper UI is the standalone mode. Or you can use it through a VS Code plugin. Historically, the AtlasMap Data Mapper UI was designed to work within Syndesis UI and it’s still a best way to experience full benefits of integrated typed data mapping with UI. You can install and run Syndesis by following the Syndesis Developer Handbook. You will find the AtlasMap Data Mapper UI under the integrations panel after selecting or adding an integration with a Data Mapper.
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Deep Lake
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.
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Pinecone
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.
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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.
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Embeddinghub
Operationalize your embeddings with one simple tool. Experience a comprehensive database designed to provide embedding functionality that, until now, required multiple platforms. Elevate your machine learning quickly and painlessly through Embeddinghub.
Embeddings are dense, numerical representations of real-world objects and relationships, expressed as vectors. They are often created by first defining a supervised machine learning problem, known as a "surrogate problem." Embeddings intend to capture the semantics of the inputs they were derived from, subsequently getting shared and reused for improved learning across machine learning models. Embeddinghub lets you achieve this in a streamlined, intuitive way.
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Pricing
Starting Price:
$50 per month
Free Version:
Free Version available.
Company Information
Nomic AI
atlas.nomic.ai/
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