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6 posts tagged with "ai"

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· 3 min read
Timo Hagenow
Duncan Blythe

Today, we have two big announcements:

1️⃣ SuperDuperDB is now Superduper!​

2️⃣ Introducing Our Enterprise Solution​


We are excited to announce the launch of our enterprise solution on top of our open-source project, designed for scalable custom AI on major databases.

Rebranding to Superduper​

With our new branding as "Superduper," we emphasize that we are not just a database but a comprehensive platform for integrating AI models and workflows with major databases. Our partnerships include:

  • MongoDB
  • Snowflake
  • Postgres
  • MySQL
  • SQLite
  • DuckDB
  • BigQuery

Superduper supports everything from Generative AI (GenAI) and Large Language Models (LLMs) to classic machine learning.

New Enterprise Offering​

Superduper Banner

Our enterprise solution empowers AI teams to deploy and scale AI applications built with our open-source development framework on a single platform. This can be done across any cloud or on-premises environment, with compute running where the data resides to minimize data movement.

Key Features:​

  • Superduper App and Workflow Templates: Ready-to-install on the database and fully configurable. Enterprises can adopt custom AI solutions with minimal development work.
  • Use Cases: Current applications include multi-modal vector search & Retrieval-Augmented Generation (RAG), document extraction & analysis, anomaly detection, visual object detection, image search, and video search.

Transforming AI-Application Development​

Superduper is on a mission to transform AI-application development by eliminating the need for traditional MLOps and ETL pipelines. Instead, developers can simply install AI components directly on their databases. This is a significant shift, allowing developers to focus on selecting the best AI models and crafting optimal queries, without worrying about infrastructure, MLOps, or specialized vector databases.

The Superduper Advantage​

By making the database the central AI platform, Superduper consolidates enterprise AI, removing unnecessary complexity from the AI-data stack. This approach ensures that AI development is secure, efficient, and rapid:

  • No Pipelines or Data Migration: All AI application steps begin and end with the database.
  • Enhanced Data Security: Keeping everything within the database enhances data security.
  • Reduced Time-to-Production: Streamlining the process results in faster deployment.
  • Composability: Superduper's declarative model allows for fully composable, database-backed AI applications. Developers can mix and match open and closed-source components, avoiding vendor lock-in, reducing costs, and maintaining control over their data and AI stack.

Get in Touch​

We're eager to discuss your AI use cases and demonstrate how Superduper can address them. Visit our new website at superduper.io to learn more!

Share this announcement to help us spread the word to the community!

#superduper #ai #mlops #mongodb #snowflake #postgres #mysql

· 4 min read
Duncan Blythe

TL;DR: SuperDuperDB is proud to announce the release of superduperdb v0.2, marking a significant advancement in its AI and database capabilities. This new version addresses critical challenges faced by AI developers, enhancing scalability, portability, extensibility, and modularity of the offering. With these new features, developers can seamlessly integrate AI with databases, scale their applications efficiently, and easily move and customize their database-AI solutions. Check some use-cases and walkthroughs at the bottom of the article.

This new version makes it easier to:

  • Customize how AI and databases work together.
  • Scale your AI projects to handle more data and users.
  • Move AI projects between different environments easily.
  • Extend the system with new AI features and database functionality.

superduperdb v0.2 will help developers unlock heightened performance and versatility in AI deployments. Check here to get started

V0.2 Launch

· 4 min read
Lalith Sagar Devagudi

tl

In this blog post, we will demonstrate how to leverage transfer learning in Database using SuperDuperDB, enabling you to efficiently enhance your AI models and streamline your development process.

Transfer learning has become a cornerstone of modern AI development. By utilizing pre-trained models and fine-tuning them for specific tasks, developers can achieve high performance with less data and computation. However, integrating transfer learning with your data stored in MongoDB presents a unique challenge.

· 3 min read
Duncan Blythe

MongoDB now supports vector-search on Atlas enabling developers to build next-gen AI applications directly on their favourite database. SuperDuperDB now make this process painless by allowing to integrate, train and manage any AI models and APIs directly with your database with simple Python.

Build next-gen AI applications - without the need of complex MLOps pipelines and infrastructure nor data duplication and migration to specialized vector databases:

  • (RAG) chat applications on documents hosted in MongoDB Atlas
  • semantic-text-search & similiarity-search, using vector embeddings of your data stored in Atlas
  • image similarity & image-search on images hosted in or referred to on MongoDB Atlas
  • video search including search within videos for key content
  • content based recommendation based on content hosted in MongoDB Atlas
  • ...and much, much more!

· 6 min read
Duncan Blythe

In this blog-post we show you how to easily operate vector-search in MongoDB Atlas using SuperDuperDB, leading to many savings and efficiencies in your AI development.

In 2023 vector-databases are hugely popular; they provide the opportunity for developers to connect LLMs, such as OpenAI’s GPT models, with their data, as well as providing the key to deploying “search-by-meaning” on troves of documents.