Remove Data Ingestion Remove Deep Learning Remove Metadata
article thumbnail

How Deltek uses Amazon Bedrock for question and answering on government solicitation documents

AWS Machine Learning Blog

Deltek is continuously working on enhancing this solution to better align it with their specific requirements, such as supporting file formats beyond PDF and implementing more cost-effective approaches for their data ingestion pipeline. The first step is data ingestion, as shown in the following diagram. What is RAG?

article thumbnail

MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

Core features of end-to-end MLOps platforms End-to-end MLOps platforms combine a wide range of essential capabilities and tools, which should include: Data management and preprocessing : Provide capabilities for data ingestion, storage, and preprocessing, allowing you to efficiently manage and prepare data for training and evaluation.

professionals

Sign Up for our Newsletter

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

article thumbnail

First ODSC Europe 2023 Sessions Announced

ODSC - Open Data Science

In this session, you’ll explore the following questions Why Ray was built and what it is How AIR, built atop Ray, allows you to easily program and scale your machine learning workloads AIR’s interoperability and easy integration points with other systems for storage and metadata needs AIR’s cutting-edge features for accelerating the machine learning (..)

article thumbnail

Achieve operational excellence with well-architected generative AI solutions using Amazon Bedrock

AWS Machine Learning Blog

Additionally, you can enable model invocation logging to collect invocation logs, full request response data, and metadata for all Amazon Bedrock model API invocations in your AWS account. Leveraging her expertise in Computer Vision and Deep Learning, she empowers customers to harness the power of the ML in AWS cloud efficiently.

article thumbnail

Implement unified text and image search with a CLIP model using Amazon SageMaker and Amazon OpenSearch Service

AWS Machine Learning Blog

In this phase, you submit a text search query or image search query through the deep learning model (CLIP) to encode as embeddings. The dataset is a collection of 147,702 product listings with multilingual metadata and 398,212 unique catalogue images. We use the first metadata file in this demo. contains image metadata.

Metadata 111
article thumbnail

Power recommendations and search using an IMDb knowledge graph – Part 3

AWS Machine Learning Blog

This mapping can be done by manually mapping frequent OOC queries to catalog content or can be automated using machine learning (ML). In this post, we illustrate how to handle OOC by utilizing the power of the IMDb dataset (the premier source of global entertainment metadata) and knowledge graphs.

Metadata 101
article thumbnail

Introducing the Topic Tracks for ODSC East 2025: Spotlight on Gen AI, AI Agents, LLMs, & More

ODSC - Open Data Science

Deep Learning & Multi-Modal Models TrackPush Neural NetworksFurther Dive into the latest advancements in neural networks, multimodal learning, and self-supervised models. This track provides practical guidance on building and optimizing deep learningsystems.