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.Weaviate AI Database is an AI-native vector database that allows you to store data objects and vector embeddings from your favorite machine learning models while scaling seamlessly into billions of data objects. AWS and NVIDIA work together in an ecosystem to deliver cost performance and efficiency for Weaviate. Weaviate...

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AWS Startups

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1,472,993 просмотров • 15 дней назад •via X (Twitter)

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.Snorkel AI helps organizations leverage their own data and expertise to build high-quality, specialized AI systems orders of magnitude faster than fully manual approaches. The company drives value in partnership with AWS and NVIDIA to help their customers. Available in AWS Marketplace, Snorkel’s AI Data Development platform enables enterprises to turn their proprietary data and domain knowledge into expert evaluation and training data, with options for in house data development or white glove dataset delivery.
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.Snorkel AI helps organizations leverage their own data and expertise to build high-quality, specialized AI systems orders of magnitude faster than fully manual approaches. The company drives value in partnership with AWS and NVIDIA to help their customers. Available in AWS Marketplace, Snorkel’s AI Data Development platform enables enterprises to turn their proprietary data and domain knowledge into expert evaluation and training data, with options for in house data development or white glove dataset delivery.

AWS Startups

2,760,137 просмотров • 21 дней назад

New short course: Building Multimodal Search and RAG", by Weaviate AI Database's Sebastia(N_) Witalec ✊🏽✊🏾✊🏿. Contrastive learning is used to train models to map vectors into an embedding space by pulling similar concepts closer together and pushing dissimilar concepts away from each other. This technique is also used to train multimodal embedding models that capture semantic similarity across different modalities like text, images, and audio. These multimodal embeddings can be used to build multimodal search and RAG systems. In this course, you'll learn how contrastive learning works, and how to add multimodality to RAG – so your models can draw on diverse, relevant context to answer questions. For example, a query about a financial report might synthesize information from text snippets, graphs, tables, and slides. You will also learn how visual instruction tuning lets you integrate image understanding into language models, and build a multi-vector recommender system using Weaviate’s open-source vector database. Please sign up here:
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New short course: Building Multimodal Search and RAG", by Weaviate AI Database's Sebastia(N_) Witalec ✊🏽✊🏾✊🏿. Contrastive learning is used to train models to map vectors into an embedding space by pulling similar concepts closer together and pushing dissimilar concepts away from each other. This technique is also used to train multimodal embedding models that capture semantic similarity across different modalities like text, images, and audio. These multimodal embeddings can be used to build multimodal search and RAG systems. In this course, you'll learn how contrastive learning works, and how to add multimodality to RAG – so your models can draw on diverse, relevant context to answer questions. For example, a query about a financial report might synthesize information from text snippets, graphs, tables, and slides. You will also learn how visual instruction tuning lets you integrate image understanding into language models, and build a multi-vector recommender system using Weaviate’s open-source vector database. Please sign up here:

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104,371 просмотров • 2 лет назад

$AMZN AWS launched Amazon Quick which is a desktop AI assistant that works across your apps and data to surface what matters and take action. This is agentic AI turning the inbox into an archive and becoming the layer that prioritizes and handles the busywork.
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$AMZN AWS launched Amazon Quick which is a desktop AI assistant that works across your apps and data to surface what matters and take action. This is agentic AI turning the inbox into an archive and becoming the layer that prioritizes and handles the busywork.

Shay Boloor

44,596 просмотров • 2 месяцев назад

Build a RAG Application from Scratch 🐕 This video covers the architecture behind Verba 0.3.0! The design behind Verba is to have an explicit manager for each core component of RAG pipelines (Read, Chunk, Embed, Retrieve, Generate) 1. ReaderManager: Load in your data (GitHubReader, PDFReader, SimpleReader, etc.) 2. ChunkManager: Chunk up your data into smaller bits (avoids hitting the token limit and results in better retrieval) 3. EmbeddingManager: Convert your data into embeddings (use OpenAI, Cohere, SentenceTransformer, etc.) 4. RetrieverManager: Retrieve the relevant context from your query 5. GenerationManager: Generate an answer from the retrieved chunks Test out Verba on the Weaviate AI Database docs/blogs here: Verba repo:
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Build a RAG Application from Scratch 🐕 This video covers the architecture behind Verba 0.3.0! The design behind Verba is to have an explicit manager for each core component of RAG pipelines (Read, Chunk, Embed, Retrieve, Generate) 1. ReaderManager: Load in your data (GitHubReader, PDFReader, SimpleReader, etc.) 2. ChunkManager: Chunk up your data into smaller bits (avoids hitting the token limit and results in better retrieval) 3. EmbeddingManager: Convert your data into embeddings (use OpenAI, Cohere, SentenceTransformer, etc.) 4. RetrieverManager: Retrieve the relevant context from your query 5. GenerationManager: Generate an answer from the retrieved chunks Test out Verba on the Weaviate AI Database docs/blogs here: Verba repo:

Erika Shorten

155,273 просмотров • 2 лет назад

Last week at NVIDIA GTC, the AWS 2026 AI Pitch Competition offered 6 early-stage AI companies the opportunity to compete for up to $25,000 in AWS credits, dedicated mentoring support from AWS and NVIDIA (through their Inception program), and exposure to senior leaders from AWS, NVIDIA, and top-tier VC firms. Startups Keyframe Labs, Kuvia, @NomadicML, Tynapse, Saptiva, and Bindwell pitched to a panel of judges, presenting their technical and business models to the audience that gathered at the AWS booth. Nomadic took first place, followed by Kuvia in second and Bindwell in third. Each founder brought their A-game to the table, and we're excited to continue supporting them as they grow!
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Last week at NVIDIA GTC, the AWS 2026 AI Pitch Competition offered 6 early-stage AI companies the opportunity to compete for up to $25,000 in AWS credits, dedicated mentoring support from AWS and NVIDIA (through their Inception program), and exposure to senior leaders from AWS, NVIDIA, and top-tier VC firms. Startups Keyframe Labs, Kuvia, @NomadicML, Tynapse, Saptiva, and Bindwell pitched to a panel of judges, presenting their technical and business models to the audience that gathered at the AWS booth. Nomadic took first place, followed by Kuvia in second and Bindwell in third. Each founder brought their A-game to the table, and we're excited to continue supporting them as they grow!

AWS Startups

8,706,102 просмотров • 3 месяцев назад

How to Train Your AI NFT (Introducing TolyAI) On-chain Clone We gathered Toly's Twitter activity from 2023 to 2025 and used it to create our SANA AI NFT. This allows you to ask questions and get answers that reflect Toly's Twitter style. The data is stored in a vector database, with the indexes secured on-chain via Metaplex Core APP DATA. We've made this agent openly available for everyone to use. Soon, anyone will be able to train their own AI NFT and store it on-chain. Toly Agent: Core Data: Dataset:
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How to Train Your AI NFT (Introducing TolyAI) On-chain Clone We gathered Toly's Twitter activity from 2023 to 2025 and used it to create our SANA AI NFT. This allows you to ask questions and get answers that reflect Toly's Twitter style. The data is stored in a vector database, with the indexes secured on-chain via Metaplex Core APP DATA. We've made this agent openly available for everyone to use. Soon, anyone will be able to train their own AI NFT and store it on-chain. Toly Agent: Core Data: Dataset:

OctonetAI

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Is your data center ready for AI? In this Transform Talks interview, Philippe Nicolas and Huawei's Yuan Yuan explain why moving to an all-flash data center is becoming inevitable for unified data access, higher efficiency, and AI-ready performance.
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Is your data center ready for AI? In this Transform Talks interview, Philippe Nicolas and Huawei's Yuan Yuan explain why moving to an all-flash data center is becoming inevitable for unified data access, higher efficiency, and AI-ready performance.

Huawei

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🚀I am excited to announce that Nile is live today! Nile is a Postgres platform to ship multi-tenant AI applications - fast, safe, and limitless Nile decouples storage from compute, virtualizes tenants, and supports vertical and horizontal scaling globally to provide 1. Unlimited Postgres databases, Unlimited virtual tenant databases 2. Customer-specific vector embeddings at 10x lower cost 3. Secure isolation for customer's data and embeddings 4. Autoscale to millions of tenants and billions of embedding 5. Place tenants on serverless or provisioned compute - globally in one database 6. Tenant-level branching, backups, schema migration, and insights
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🚀I am excited to announce that Nile is live today! Nile is a Postgres platform to ship multi-tenant AI applications - fast, safe, and limitless Nile decouples storage from compute, virtualizes tenants, and supports vertical and horizontal scaling globally to provide 1. Unlimited Postgres databases, Unlimited virtual tenant databases 2. Customer-specific vector embeddings at 10x lower cost 3. Secure isolation for customer's data and embeddings 4. Autoscale to millions of tenants and billions of embedding 5. Place tenants on serverless or provisioned compute - globally in one database 6. Tenant-level branching, backups, schema migration, and insights

Ram

39,541 просмотров • 1 год назад

Made in San Francisco with AWS: David Twizer, CEO & Co-founder, xpander.ai. Why SF? “For the AI agent space, this city is mission zero.” xpander enables teams to build and deploy AI agents with any framework, in any environment. With Amazon Bedrock, the startup can train models and AWS solutions, scaling their business much faster.
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Made in San Francisco with AWS: David Twizer, CEO & Co-founder, xpander.ai. Why SF? “For the AI agent space, this city is mission zero.” xpander enables teams to build and deploy AI agents with any framework, in any environment. With Amazon Bedrock, the startup can train models and AWS solutions, scaling their business much faster.

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AI-ready data, all under one roof. 🏠☁️💰 Introducing preview of Amazon S3 Vectors—the first cloud object store with built-in vector support. Store vectors at scale with up to 90% total savings and query with ease, all in one place. #AWS #AWSSummit 👉
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AI-ready data, all under one roof. 🏠☁️💰 Introducing preview of Amazon S3 Vectors—the first cloud object store with built-in vector support. Store vectors at scale with up to 90% total savings and query with ease, all in one place. #AWS #AWSSummit 👉

Amazon Web Services

50,729 просмотров • 1 год назад

New short course on Building Applications with Vector Databases, taught by Pinecone’s Tim Tully! At the heart of a vector database is the ability to store a collection of vectors and then query against that, meaning input a new vector and find similar ones. This is useful for many AI applications. In this course, you'll learn how to use vector databases to build: (i) Semantic Search: Create a text search tool that goes beyond keyword matching, and instead focuses on the meaning of content. (ii) RAG (retrieval augmented generation): Enhance your LLM output by incorporating context from sources the model wasn't trained on. (iii) Recommender System: Combine semantic search and RAG to recommend topics, and demonstrate it with a news article recommender. (iv) Hybrid Search: Build an application that finds items using both images and descriptive text -- by combining both sparse and dense vector representations of the data -- using an eCommerce dataset as an example. (v) Image Similarity: Use image vector embeddings to create an app to compare facial features, using a database of public figures to determine the likeness between them. (vi) Anomaly Detection: Build an anomaly detection app that identifies unusual patterns in network communication logs. I hope you’ll enjoy learning how to build all these types of applications! Please sign up here:
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New short course on Building Applications with Vector Databases, taught by Pinecone’s Tim Tully! At the heart of a vector database is the ability to store a collection of vectors and then query against that, meaning input a new vector and find similar ones. This is useful for many AI applications. In this course, you'll learn how to use vector databases to build: (i) Semantic Search: Create a text search tool that goes beyond keyword matching, and instead focuses on the meaning of content. (ii) RAG (retrieval augmented generation): Enhance your LLM output by incorporating context from sources the model wasn't trained on. (iii) Recommender System: Combine semantic search and RAG to recommend topics, and demonstrate it with a news article recommender. (iv) Hybrid Search: Build an application that finds items using both images and descriptive text -- by combining both sparse and dense vector representations of the data -- using an eCommerce dataset as an example. (v) Image Similarity: Use image vector embeddings to create an app to compare facial features, using a database of public figures to determine the likeness between them. (vi) Anomaly Detection: Build an anomaly detection app that identifies unusual patterns in network communication logs. I hope you’ll enjoy learning how to build all these types of applications! Please sign up here:

Andrew Ng

137,073 просмотров • 2 лет назад

Amazon Redshift RG, powered by AWS Graviton, deliver better performance - running data warehouse and data lake workloads up to 2.4x as fast as previous generation RA3 at 30% lower cost per vCPU. Run SQL analytics across your data warehouse and data lake from a single engine — including on open formats such as Apache Iceberg and Apache Parquet. Built to meet the demands of today’s analytics and agentic AI workloads.
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Amazon Redshift RG, powered by AWS Graviton, deliver better performance - running data warehouse and data lake workloads up to 2.4x as fast as previous generation RA3 at 30% lower cost per vCPU. Run SQL analytics across your data warehouse and data lake from a single engine — including on open formats such as Apache Iceberg and Apache Parquet. Built to meet the demands of today’s analytics and agentic AI workloads.

Amazon Web Services

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Big news: SnowflakeDB is bringing OpenAI’s latest models to Cortex AI on Microsoft Azure.⚡️ And we’re making Cortex Agents available within Microsoft 365 Copilot and Microsoft365 apps to empower you to talk to your data in conversational language, right where you work. Reliable, powerful AI—seamlessly integrated with your enterprise data in Snowflake, all at your fingertips. The potential for what you can build next is massive. Excited to see what you create!

sridhar

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.Zammoai built an Azure managed application on Microsoft Marketplace for faster time to value with one-click deployment in customer tenants, secure data control, and rapid scaling of AI agent solutions. Learn more:
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.Zammoai built an Azure managed application on Microsoft Marketplace for faster time to value with one-click deployment in customer tenants, secure data control, and rapid scaling of AI agent solutions. Learn more:

Microsoft Azure

11,624 просмотров • 23 дней назад

.Amazon Web Services is helping The Ocean Cleanup find and remove plastic in the Great Pacific Garbage Patch, thanks to AI and cloud computing. Here's the tech behind the mission. ⬇️ Machine learning models analyze satellite, drone, and sensor data to map where plastic accumulates. Then, our systems help to predict where debris will drift next and optimize cleanup routes in near real-time. AWS also powers data-driven maps that track how plastics move through rivers—accelerating the deployment of river plastic interceptors. The goal? Remove 90% of floating plastic by 2040. The Ocean Cleanup has already removed 64 million pounds of trash worldwide. AWS tech helps them work even faster and smarter.
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.Amazon Web Services is helping The Ocean Cleanup find and remove plastic in the Great Pacific Garbage Patch, thanks to AI and cloud computing. Here's the tech behind the mission. ⬇️ Machine learning models analyze satellite, drone, and sensor data to map where plastic accumulates. Then, our systems help to predict where debris will drift next and optimize cleanup routes in near real-time. AWS also powers data-driven maps that track how plastics move through rivers—accelerating the deployment of river plastic interceptors. The goal? Remove 90% of floating plastic by 2040. The Ocean Cleanup has already removed 64 million pounds of trash worldwide. AWS tech helps them work even faster and smarter.

Amazon

34,045 просмотров • 2 месяцев назад

ColPali is changing the game for PDF retrieval by eliminating the need for OCR and chunking methods 🚀 Inspired by ColBERT’s success with text, ColPali splits an image of a document into patches, which are then processed through a vision LLM called PaliGemma. The embeddings for each patch retain contextual information, similarly to text embeddings in methods like ColBERT. During retrieval, user queries are embedded in the same space, and then compared to document patches using the MaxSim operator. ColPali recipe POC Weaviate AI Database: ColPali paper: As always, shoutout to the awesome Daniel Williams for the help! 💙
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ColPali is changing the game for PDF retrieval by eliminating the need for OCR and chunking methods 🚀 Inspired by ColBERT’s success with text, ColPali splits an image of a document into patches, which are then processed through a vision LLM called PaliGemma. The embeddings for each patch retain contextual information, similarly to text embeddings in methods like ColBERT. During retrieval, user queries are embedded in the same space, and then compared to document patches using the MaxSim operator. ColPali recipe POC Weaviate AI Database: ColPali paper: As always, shoutout to the awesome Daniel Williams for the help! 💙

Victoria Slocum

67,833 просмотров • 1 год назад

AWS DevOps Agent is now generally available. Your always-available operations teammate that autonomously investigates incidents the moment an alert comes in, reducing mean time to resolution from hours to minutes across AWS, multicloud, and on-prem environments. Get proactive recommendations that prevent future incidents and strengthen system resilience based on patterns across historical incidents.
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AWS DevOps Agent is now generally available. Your always-available operations teammate that autonomously investigates incidents the moment an alert comes in, reducing mean time to resolution from hours to minutes across AWS, multicloud, and on-prem environments. Get proactive recommendations that prevent future incidents and strengthen system resilience based on patterns across historical incidents.

Amazon Web Services

7,430,051 просмотров • 3 месяцев назад