Remove Architecture Remove Machine Learning Remove Serverless Remove Storage
article thumbnail

Leveraging Serverless and Generative AI for Image Captioning on GCP

Xebia

Leveraging Serverless and Generative AI for Image Captioning on GCP In today’s age of abundant data, especially visual data, it’s imperative to understand and categorize images efficiently. TL;DR We’ve built an automated, serverless system on Google Cloud Platform where: Users upload images to a Google Cloud Storage Bucket.

article thumbnail

Natural Language Processing & Machine Learning in Higher Education

Mentormate

In this article, we will discuss how MentorMate and our partner eLumen leveraged natural language processing (NLP) and machine learning (ML) for data-driven decision-making to tame the curriculum beast in higher education. The primary data sources used in eLumen Insights are on the left-hand side of the architecture.

Insiders

Sign Up for our Newsletter

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

article thumbnail

Build a contextual text and image search engine for product recommendations using Amazon Bedrock and Amazon OpenSearch Serverless

AWS Machine Learning - AI

In this post, we show how to build a contextual text and image search engine for product recommendations using the Amazon Titan Multimodal Embeddings model , available in Amazon Bedrock , with Amazon OpenSearch Serverless. Amazon SageMaker Studio – It is an integrated development environment (IDE) for machine learning (ML).

article thumbnail

Why You Want To Use Looker Studio For Data Visualization on BigQuery

Perficient

If you have built or are building a Data Lake on the Google Cloud Platform (GCP) and BigQuery you already know that BigQuery is a fully managed enterprise data warehouse that helps you manage and analyze your data with built-in features like machine learning, geospatial analysis, and business intelligence.

Data 111
article thumbnail

Cloud Computing Trends and Innovations

Apiumhub

The Evolution of Cloud Computing Trends Edge Computing Redefining Latency Edge computing is poised to revolutionize cloud architecture by decentralizing computing power. As serverless frameworks mature, they are becoming integral components of cloud ecosystems, enabling developers to focus on code rather than infrastructure management.

Trends 52
article thumbnail

Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

Knowledge Bases is completely serverless, so you don’t need to manage any infrastructure, and when using Knowledge Bases, you’re only charged for the models, vector databases and storage you use. The OpenSearch Serverless collection. This process comes with intelligent diffing, throughput, and failure management. The S3 bucket.

article thumbnail

High-performance computing on AWS

Xebia

Key features of AWS Batch Efficient Resource Management: AWS Batch automatically provisions the required resources, such as compute instances and storage, based on job requirements. This enables you to build end-to-end workflows that leverage the full range of AWS capabilities for data processing, storage, and analytics.

AWS 147