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4 posts tagged with "AI"

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· 4 min read
Lalith Sagar Devagudi

Transfer Learning in MongoDB Using SuperDuperDB


In this blog post, we will demonstrate how to leverage transfer learning in MongoDB 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.