Remove 2020 Remove K-nearest Neighbors Remove ML
article thumbnail

Talk to your slide deck using multimodal foundation models hosted on Amazon Bedrock and Amazon SageMaker – Part 2

AWS Machine Learning Blog

We perform a k-nearest neighbor (k-NN) search to retrieve the most relevant embeddings matching the user query. As per the AI/ML flywheel, what do the AWS AI/ML services provide? Based on the summary, the AWS AI/ML services provide a range of capabilities that fuel an AI/ML flywheel.

AWS 132
article thumbnail

Identifying defense coverage schemes in NFL’s Next Gen Stats

AWS Machine Learning Blog

Through a collaboration between the Next Gen Stats team and the Amazon ML Solutions Lab , we have developed the machine learning (ML)-powered stat of coverage classification that accurately identifies the defense coverage scheme based on the player tracking data. In this post, we deep dive into the technical details of this ML model.

ML 91
professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Boosting RAG-based intelligent document assistants using entity extraction, SQL querying, and agents with Amazon Bedrock

AWS Machine Learning Blog

Another driver behind RAG’s popularity is its ease of implementation and the existence of mature vector search solutions, such as those offered by Amazon Kendra (see Amazon Kendra launches Retrieval API ) and Amazon OpenSearch Service (see k-Nearest Neighbor (k-NN) search in Amazon OpenSearch Service ), among others.

SQL 133
article thumbnail

Coactive AI’s CEO: quality beats quantity for data selection

Snorkel AI

I’m Cody Coleman and I’m really excited to share my research on how careful data selection can make ML development faster, cheaper, and better by focusing on quality rather than quantity. First, “Selection via Proxy,” which appeared in ICLR 2020. I’m super excited to chat with you all today.

article thumbnail

Coactive AI’s CEO: quality beats quantity for data selection

Snorkel AI

I’m Cody Coleman and I’m really excited to share my research on how careful data selection can make ML development faster, cheaper, and better by focusing on quality rather than quantity. First, “Selection via Proxy,” which appeared in ICLR 2020. I’m super excited to chat with you all today.

article thumbnail

Coactive AI’s CEO: quality beats quantity for data selection

Snorkel AI

I’m Cody Coleman and I’m really excited to share my research on how careful data selection can make ML development faster, cheaper, and better by focusing on quality rather than quantity. First, “Selection via Proxy,” which appeared in ICLR 2020. I’m super excited to chat with you all today.