MongoDB.local NYC 2023 Highlights

MongoDB.local NYC 2023 Highlights

Exciting news about Vector Search and other highlights from MongoDB’s user conference in New York

Last week, Pureinsights attended MongoDB.local in NYC, a user conference for MongoDB’s Developer Data Platform.  We were excited to hear about some of the Vector Search capabilities announced for the platform, which we discuss in this blog along with some other highlights from the event, including the Keynote.

MongoDB.local NYC Highlights

Support for Vector Search and Generative AI Use Cases

MongoDB unveiled several new capabilities at the MongoDB.local NYC event, aimed at helping users and customers build, iterate, and scale their applications with MongoDB. One significant addition to the platform is the introduction of vector search capabilities called Atlas Vector Search.

MongoDB CEO Dev Ittycheria announces vector search

While concepts like vector search are familiar to developers who specialize in search applications and semantic search, most data platform developers are still unfamiliar with the concept.  So, MongoDB offered several demos and presentations to introduce the concept to the attendees.

Vectors are mathematical representations of unstructured data, such as text, images, videos, and audio files, which enable similarity and relevance-based queries without relying on keyword matching. With Atlas Vector Search, users can store, index, and query vectors alongside operational and transactional data in documents, eliminating the need for separate database systems. This addition enables teams to deliver more context-aware results and augment applications built on Large Language Models (LLMs) with proprietary data, improving accuracy and performance. Atlas Vector Search is available in public preview.

Other Key Atlas Search Capabilities Announced

MongoDB announced five new major capabilities in their cloud platform, MongoDB Atlas, of which Vector Search was one. After a private preview of the functionality, Pureinsights provide a quote in support of the announcement.

“We’ve been working with Atlas Vector Search while in private preview and are excited to be partnering with MongoDB to help enable this new capability for our customers. Being able to store and use vectors within the MongoDB Atlas platform powers new workloads and exciting AI-powered experiences that users want, like semantic search and generative answers.”

In terms of search capabilities, Atlas Search has gained new features, including:

  • Search query analytics that provide insight into user search behavior, allowing developers to refine and customize search logic.
  • Programmatic Index Management: developers can now create and manage Atlas Search indexes in language drivers, MongoDB Compass, and the MongoDB Shell, facilitating easier index management.
  • Dedicated search nodes were also announced, enabling teams to independently scale and optimize search workloads for improved performance, availability, and faster index builds.

While available only in preview at the time of this blog, we think all these capabilities make the Atlas Search platform very attractive for search applications that leverage a document model, and situations where customers want to eliminate the “integration tax” by combining search capabilities on the same platform as their data.

Highlighted Vector Search Use Case: Automotive Repair

One of the powerful things about vector search is that it can be applied not just to just full text, but basically anything that can be digitized, enabling totally novel use cases. 

One such use case highlighted by MongoDB CTO, Sahir Azam is an example where engineers can take a sound sample from an engine that is having problems and compare it against a vector database of sounds associated with known problems.  When a problem is diagnosed with high probability, the technician can be directed to the appropriate manuals and repair procedures. Azam likened it to using “Shazam” for engine repair if you will.

MongoDB Vector Search Autorepair Example 1
MongoDB Vector Search Autorepair Example 2

The customer wasn’t named, and we would expect that there are likely some tweaks for the system to work reliably, but the system has the potential to reduce repair diagnostic time from hours to minutes. And as far-fetched as this may seem, applications like this have been conceptualized for quite some time, especially in the field of image recognition.  Computer vision has found applications in manufacturing, and using AI to read X-Rays and MRIs has seen huge strides in recent years.  We’ve only scratched the surface of what is possible with vector search, powered by machine learning models of all kinds.

Keynote Highlights

The MongoDB.local NYC Keynote featured Dev Ittycheria, CEO of MongoDB, and Sahir Azam, Chief Product Officer, along with a few other guest speakers.  We’ve summarized and bookmarked key sections of the keynote related to search topics and use cases below.  You can also watch the entire keynote on YouTube using a link at the end of the blog.

MongoDB CEO, Dev Ittycheria, discusses the impact of AI on data platforms, the new vector search capability on MongoDB, and the semantic search ideas powered by this new capability.  Dev sees an inevitable future where every single important application will integrate AI on a scalable, performant data platform like MongoDB.

Keynote at 11:04 to 15:20

  • AI: The Next Big Thing
  • 1500 companies building AI workloads on MongoDB, 200 new ones in Q1 alone
  • Announcing Atlas Vector Search to power AI-driven semantic search
  • Announcing MongoDB AI Innovators Program
Dev Ittycheria MongoDB Local NYC Keynote slide

Sahir Azam, Chief Product Officer discusses MongoDB Atlas Search, full text search, semantic search, and the new vector search capabilities.  He elaborates on the automotive repair use case mentioned above.

Keynote at 35:25 to 45:12

  • Full text search unified in one platform with Atlas Search
  • Customer examples: Albertsons supermarkets and Fishbowl by Glassdoor
  • New Atlas Search capabilities
    • Atlas Search nodes
    • Programmatic Index Management
    • Search Query Analytics
  • Preview: Vector Search capabilities and use cases
Shahir Azam MongoDB Local NYC Keynote slide

Excited to Partner with MongoDB

On some personal notes, Pureinsights was invited to attend this event as newly minted integration partners in the MongoDB BSI Partner program.  We announced joining this exclusive program just two months ago, and it was exciting to gather and meet all of our partners in business development, marketing and product development at MongoDB.  We also enjoyed meeting with colleagues from other companies in the BSI program.

Pureinsights MongoDB BSI Partner with CEO Dev Ittycheria

PHOTO: From L to R – Mike Moss, VP of Global Systems Integrators, MongoDB; Graham Gillen, VP of Marketing at Pureinsights; Kamran Khan, CEO of Pureinsights; Dev Ittycheria, CEO of MongoDB; John Back, VP of Sales, Americas at Pureinsights, Prasad Pashte, Director, and Global Lead for BSI Partners at MongoDB.

Wrapping Up

It was great to gather in person again at MongoDB.local NYC 2023 to see all the exciting highlights and developments going on with the MongoDB platform.  We are particularly excited to explore use cases and applications we can build using the new capabilities in Atlas Search, including vector search capabilities.

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