Generative AI has revolutionized AI, finding applications in text generation, code generation, summarization, and more. One evolving area is natural language processing (NLP) for intuitive SQL queries, aiming to make database querying more accessible to non-technical users. Key considerations include prompt engineering, architecture patterns, and optimization for efficient text-to-SQL systems using Large Language Models (LLMs).…
The Mixtral-8x7B large language model, developed by Mistral AI, is now available for customers through Amazon SageMaker JumpStart, allowing for one-click deployment for running inference. The model provides significant performance improvements for natural language processing tasks and supports multiple languages, making it suitable for various NLP applications.
The blog describes TruEra’s collaboration in co-writing with Josh Reini, Shayak Sen, and Anupam Datta from TruEra. It highlights Amazon SageMaker JumpStart’s provision of pretrained foundation models, outlines the need for adapting foundation models to new tasks or domains, and mentions TruLens’ framework for extensible, automated evaluations. Additionally, it details the processes of deploying and…
Summary: This post details the development and deployment of a generative AI financial services agent powered by Amazon Bedrock. The agent can assist with account information, loan applications, and natural language queries, and is designed as a launchpad for developers creating conversational agents. The post also discusses deployment automation, testing, cleanup, and considerations for production…
Generative AI in contact centers is becoming increasingly crucial, driving customer experience excellence and operational efficiency. The “SageMaker Canvas” tool, embedded with Amazon Bedrock and JumpStart models, empowers the creation of customer-centric, compliance-improved call scripts. Combined with Amazon Connect features, this facilitates seamless, AI-enhanced customer-agent interactions, ensuring prompt issue resolution and personalized support.
The Llama Guard model is now available within SageMaker JumpStart, an ML hub of Amazon SageMaker providing access to foundation models, including the Llama Guard model, with input and output safeguards for large language models (LLMs) and extensive content moderation capabilities. The model is intended to provide developers with a pretrained model to help defend…
The text discusses the increasing security threats faced by customers and the need to centralize and standardize security data. It introduces a novel approach using Amazon Security Lake and Amazon SageMaker for security analytics. The solution involves enabling Amazon Security Lake, processing log data, training an ML model, and deploying the model for real-time inference.…
The text is a collaboration with Ankur Goyal and Karthikeyan Chokappa from PwC Australia’s Cloud & Digital business, discussing the integration of artificial intelligence and machine learning into systems and processes. It emphasizes the challenges of deploying machine learning models at scale and introduces PwC’s Machine Learning Ops Accelerator, which automates the deployment and maintenance…
AWS recognizes the transformative potential of AI and emphasizes responsible use through collaboration with customers and adherence to ISO 42001. The international standard provides guidelines for managing AI systems within organizations, promoting responsible AI practices. AWS actively contributes to the standard’s development, aiming to foster global cooperation in implementing responsible AI solutions and demonstrate commitment…
The text outlines the challenges faced by industries without real-time forecasts and introduces the integration of MongoDB’s time series data management capabilities with Amazon SageMaker Canvas for overcoming these challenges. It details the solution architecture, prerequisites, and step-by-step processes for setting up the solution using MongoDB Atlas and Amazon SageMaker Canvas. The post concludes with…
Amazon announced the integration of Amazon DocumentDB (with MongoDB compatibility) with Amazon SageMaker Canvas, enabling users to develop generative AI and machine learning models without coding. This integration simplifies analytics on unstructured data, removing the need for data engineering and science teams. The post details steps to implement and utilize the solution within SageMaker Canvas.
Amazon SageMaker Studio offers fully managed integrated development environments (IDEs) like JupyterLab, Code Editor, and RStudio for machine learning development. The introduction of JupyterLab Spaces allows flexible customization of compute, storage, and runtime resources to improve ML workflow efficiency, with enhanced control over storage and capabilities for collaborative work. SageMaker Studio also integrates generative AI-powered…
ICL, a multinational corporation based in Israel, faced challenges monitoring industrial equipment at their mining sites due to harsh conditions and costly manual monitoring. They partnered with AWS to develop in-house capabilities using machine learning for computer vision, leading to a successful prototype for monitoring mining screeners. This collaboration enabled ICL to build and deploy…
Amazon Comprehend is a natural-language processing (NLP) service offering pre-trained and custom APIs for deriving insights from textual data. It allows training custom named entity recognition (NER) models to extract business-specific entities from documents. The pre-labeling tool automates document annotation using existing tabular entity data, reducing manual effort. The tool accelerates custom entity recognition model…
Text-to-image generation is a fast-growing field in AI, finding applications in media, gaming, e-commerce, advertising, design, art, and medical imaging. Stable Diffusion and Retrieval Augmented Generation (RAG) are innovative models that simplify and enhance prompt creation for text-to-image generation, increasing efficiency and creativity across various industries. AWS provides diverse LLM options, facilitating the construction of…
Talent.com, founded in 2011, offers a unified job search platform covering 75+ countries, 30M+ job listings, and various languages and industries. It collaborates with AWS to develop a job recommendation engine using deep learning. The large-scale data processing pipeline handles JSON Lines from S3, extracting and refining features for the recommendation engine. The pipeline significantly…
This post outlines a solution for using Amazon Transcribe and Amazon Bedrock to automatically generate concise summaries of video or audio recordings. By leveraging a combination of speech-to-text capability and generative AI models, the solution aims to simplify and automate the note-taking process, enhancing collaboration and saving time. The post provides instructions for deploying, running,…
This post showcases fine-tuning a large language model (LLM) using Parameter-Efficient Fine-Tuning (PEFT) and deploying the fine-tuned model on AWS Inferentia2. It discusses using the AWS Neuron SDK to access the device and deploying the model with DJLServing. It also details the necessary steps, including prerequisites, a walkthrough for fine-tuning the LLM, and hosting it…
The text describes the importance of Machine Learning Operations (MLOps) in integrating ML models into production systems. It explains Amazon SageMaker MLOps features like Projects, Pipelines, and Model Registry. The process of creating a custom project template for CI/CD pipelines using AWS services and GitHub is detailed, along with a summary of the implementation.
The rise of ChatGPT and generative AI’s popularity on AWS has sparked interest in leveraging this technology for creating enterprise chatbots. By deploying a solution known as Chat Studio, users can engage with foundation models available in Amazon SageMaker, such as Llama 2 and Stable Diffusion, through a web interface. Additional integrations and deployment options…