AWS Machine Learning Blog
Category: Integration & Automation
AI Workforce: using AI and Drones to simplify infrastructure inspections
Inspecting wind turbines, power lines, 5G towers, and pipelines is a tough job. It’s often dangerous, time-consuming, and prone to human error. This post is the first in a three-part series exploring AI Workforce, the AWS AI-powered drone inspection system. In this post, we introduce the concept and key benefits. The second post dives into the AWS architecture that powers AI Workforce, and the third focuses on the drone setup and integration.
Revolutionizing business processes with HAQM Bedrock and Appian’s generative AI skills
AWS and Appian’s collaboration marks a significant advancement in business process automation. By using the power of HAQM Bedrock and Anthropic’s Claude models, Appian empowers enterprises to optimize and automate processes for greater efficiency and effectiveness. This blog post will cover how Appian AI skills build automation into organizations’ mission-critical processes to improve operational excellence, reduce costs, and build scalable solutions.
How Crexi achieved ML models deployment on AWS at scale and boosted efficiency
Commercial Real Estate Exchange, Inc. (Crexi), is a digital marketplace and platform designed to streamline commercial real estate transactions. In this post, we will review how Crexi achieved its business needs and developed a versatile and powerful framework for AI/ML pipeline creation and deployment. This customizable and scalable solution allows its ML models to be efficiently deployed and managed to meet diverse project requirements.
Implementing advanced prompt engineering with HAQM Bedrock
In this post, we provide insights and practical examples to help balance and optimize the prompt engineering workflow. We focus on advanced prompt techniques and best practices for the models provided in HAQM Bedrock, a fully managed service that offers a choice of high-performing foundation models from leading AI companies such as Anthropic, Cohere, Meta, Mistral AI, Stability AI, and HAQM through a single API. With these prompting techniques, developers and researchers can harness the full capabilities of HAQM Bedrock, providing clear and concise communication while mitigating potential risks or undesirable outputs.
Build an end-to-end RAG solution using HAQM Bedrock Knowledge Bases and AWS CloudFormation
Retrieval Augmented Generation (RAG) is a state-of-the-art approach to building question answering systems that combines the strengths of retrieval and foundation models (FMs). RAG models first retrieve relevant information from a large corpus of text and then use a FM to synthesize an answer based on the retrieved information. An end-to-end RAG solution involves several […]
Accelerate deep learning training and simplify orchestration with AWS Trainium and AWS Batch
In large language model (LLM) training, effective orchestration and compute resource management poses a significant challenge. Automation of resource provisioning, scaling, and workflow management is vital for optimizing resource usage and streamlining complex workflows, thereby achieving efficient deep learning training processes. Simplified orchestration enables researchers and practitioners to focus more on model experimentation, hyperparameter tuning, […]
Develop and train large models cost-efficiently with Metaflow and AWS Trainium
This is a guest post co-authored with Ville Tuulos (Co-founder and CEO) and Eddie Mattia (Data Scientist) of Outerbounds. To build a production-grade AI system today (for example, to do multilingual sentiment analysis of customer support conversations), what are the primary technical challenges? Historically, natural language processing (NLP) would be a primary research and development […]
Achieve DevOps maturity with BMC AMI zAdviser Enterprise and HAQM Bedrock
This blog post discusses how BMC Software added AWS Generative AI capabilities to its product BMC AMI zAdviser Enterprise. The zAdviser uses HAQM Bedrock to provide summarization, analysis, and recommendations for improvement based on the DORA metrics data.
Automatically redact PII for machine learning using HAQM SageMaker Data Wrangler
Customers increasingly want to use deep learning approaches such as large language models (LLMs) to automate the extraction of data and insights. For many industries, data that is useful for machine learning (ML) may contain personally identifiable information (PII). To ensure customer privacy and maintain regulatory compliance while training, fine-tuning, and using deep learning models, […]
Implement smart document search index with HAQM Textract and HAQM OpenSearch
In this post, we’ll take you on a journey to rapidly build and deploy a document search indexing solution that helps your organization to better harness and extract insights from documents. Whether you’re in Human Resources looking for specific clauses in employee contracts, or a financial analyst sifting through a mountain of invoices to extract payment data, this solution is tailored to empower you to access the information you need with unprecedented speed and accuracy.