Inference Llama 2 models with real-time response streaming using Amazon SageMaker

With the rapid adoption of generative AI applications, there is a need for these applications to respond in time to reduce the perceived latency with higher throughput. Foundation models (FMs) are often pre-trained on vast corpora of data with parameters ranging in scale of millions to billions and beyond. Large language models (LLMs) are a … Read more

Create a document lake using large-scale text extraction from documents with Amazon Textract

AWS customers in healthcare, financial services, the public sector, and other industries store billions of documents as images or PDFs in Amazon Simple Storage Service (Amazon S3). However, they’re unable to gain insights such as using the information locked in the documents for large language models (LLMs) or search until they extract the text, forms, … Read more

Modernizing data science lifecycle management with AWS and Wipro

This post was written in collaboration with Bhajandeep Singh and Ajay Vishwakarma from Wipro’s AWS AI/ML Practice. Many organizations have been using a combination of on-premises and open source data science solutions to create and manage machine learning (ML) models. Data science and DevOps teams may face challenges managing these isolated tool stacks and systems. … Read more

Generating value from enterprise data: Best practices for Text2SQL and generative AI

Generative AI has opened up a lot of potential in the field of AI. We are seeing numerous uses, including text generation, code generation, summarization, translation, chatbots, and more. One such area that is evolving is using natural language processing (NLP) to unlock new opportunities for accessing data through intuitive SQL queries. Instead of dealing … Read more

Amazon SageMaker model parallel library now accelerates PyTorch FSDP workloads by up to 20%

Large language model (LLM) training has surged in popularity over the last year with the release of several popular models such as Llama 2, Falcon, and Mistral. Customers are now pre-training and fine-tuning LLMs ranging from 1 billion to over 175 billion parameters to optimize model performance for applications across industries, from healthcare to finance … Read more

Mixtral-8x7B is now available in Amazon SageMaker JumpStart

Today, we are excited to announce that the Mixtral-8x7B large language model (LLM), developed by Mistral AI, is available for customers through Amazon SageMaker JumpStart to deploy with one click for running inference. The Mixtral-8x7B LLM is a pre-trained sparse mixture of expert model, based on a 7-billion parameter backbone with eight experts per feed-forward … Read more

Deploy foundation models with Amazon SageMaker, iterate and monitor with TruEra

This blog is co-written with Josh Reini, Shayak Sen and Anupam Datta from TruEra Amazon SageMaker JumpStart provides a variety of pretrained foundation models such as Llama-2 and Mistal 7B that can be quickly deployed to an endpoint. These foundation models perform well with generative tasks, from crafting text and summaries, answering questions, to producing … Read more

Build generative AI agents with Amazon Bedrock, Amazon DynamoDB, Amazon Kendra, Amazon Lex, and LangChain

Generative AI agents are capable of producing human-like responses and engaging in natural language conversations by orchestrating a chain of calls to foundation models (FMs) and other augmenting tools based on user input. Instead of only fulfilling predefined intents through a static decision tree, agents are autonomous within the context of their suite of available … Read more

Overcoming common contact center challenges with generative AI and Amazon SageMaker Canvas

Great customer experience provides a competitive edge and helps create brand differentiation. As per the Forrester report, The State Of Customer Obsession, 2022, being customer-first can make a sizable impact on an organization’s balance sheet, as organizations embracing this methodology are surpassing their peers in revenue growth. Despite contact centers being under constant pressure to … Read more

Llama Guard is now available in Amazon SageMaker JumpStart

Today we are excited to announce that the Llama Guard model is now available for customers using Amazon SageMaker JumpStart. Llama Guard provides input and output safeguards in large language model (LLM) deployment. It’s one of the components under Purple Llama, Meta’s initiative featuring open trust and safety tools and evaluations to help developers build … Read more

Identify cybersecurity anomalies in your Amazon Security Lake data using Amazon SageMaker

Customers are faced with increasing security threats and vulnerabilities across infrastructure and application resources as their digital footprint has expanded and the business impact of those digital assets has grown. A common cybersecurity challenge has been two-fold: Consuming logs from digital resources that come in different formats and schemas and automating the analysis of threat … Read more

Driving advanced analytics outcomes at scale using Amazon SageMaker powered PwC’s Machine Learning Ops Accelerator

This post was written in collaboration with Ankur Goyal and Karthikeyan Chokappa from PwC Australia’s Cloud & Digital business. Artificial intelligence (AI) and machine learning (ML) are becoming an integral part of systems and processes, enabling decisions in real time, thereby driving top and bottom-line improvements across organizations. However, putting an ML model into production … Read more

ISO 42001: A new foundational global standard to advance responsible AI

Artificial intelligence (AI) is one of the most transformational technologies of our generation and provides opportunities to be a force for good and drive economic growth. The growth of large language models (LLMs), with hundreds of billions of parameters, has unlocked new generative AI use cases to improve customer experiences, boost employee productivity, and so … Read more

Accelerating time-to-insight with MongoDB time series collections and Amazon SageMaker Canvas

This is a guest post co-written with Babu Srinivasan from MongoDB. As industries evolve in today’s fast-paced business landscape, the inability to have real-time forecasts poses significant challenges for industries heavily reliant on accurate and timely insights. The absence of real-time forecasts in various industries presents pressing business challenges that can significantly impact decision-making and … Read more

Use Amazon DocumentDB to build no-code machine learning solutions in Amazon SageMaker Canvas

We are excited to announce the launch of Amazon DocumentDB (with MongoDB compatibility) integration with Amazon SageMaker Canvas, allowing Amazon DocumentDB customers to build and use generative AI and machine learning (ML) solutions without writing code. Amazon DocumentDB is a fully managed native JSON document database that makes it straightforward and cost-effective to operate critical … Read more

Boost productivity on Amazon SageMaker Studio: Introducing JupyterLab Spaces and generative AI tools

Amazon SageMaker Studio offers a broad set of fully managed integrated development environments (IDEs) for machine learning (ML) development, including JupyterLab, Code Editor based on Code-OSS (Visual Studio Code Open Source), and RStudio. It provides access to the most comprehensive set of tools for each step of ML development, from preparing data to building, training, … Read more

How AWS Prototyping enabled ICL-Group to build computer vision models on Amazon SageMaker

This is a customer post jointly authored by ICL and AWS employees. ICL is a multi-national manufacturing and mining corporation based in Israel that manufactures products based on unique minerals and fulfills humanity’s essential needs, primarily in three markets: agriculture, food, and engineered materials. Their mining sites use industrial equipment that has to be monitored … Read more

Automate PDF pre-labeling for Amazon Comprehend

Amazon Comprehend is a natural-language processing (NLP) service that provides pre-trained and custom APIs to derive insights from textual data. Amazon Comprehend customers can train custom named entity recognition (NER) models to extract entities of interest, such as location, person name, and date, that are unique to their business. To train a custom model, you … Read more

Improve your Stable Diffusion prompts with Retrieval Augmented Generation

Text-to-image generation is a rapidly growing field of artificial intelligence with applications in a variety of areas, such as media and entertainment, gaming, ecommerce product visualization, advertising and marketing, architectural design and visualization, artistic creations, and medical imaging. Stable Diffusion is a text-to-image model that empowers you to create high-quality images within seconds. In November … Read more

Streamlining ETL data processing at Talent.com with Amazon SageMaker

This post is co-authored by Anatoly Khomenko, Machine Learning Engineer, and Abdenour Bezzouh, Chief Technology Officer at Talent.com. Established in 2011, Talent.com aggregates paid job listings from their clients and public job listings, and has created a unified, easily searchable platform. Covering over 30 million job listings across more than 75 countries and spanning various … Read more

Create summaries of recordings using generative AI with Amazon Bedrock and Amazon Transcribe

Meeting notes are a crucial part of collaboration, yet they often fall through the cracks. Between leading discussions, listening closely, and typing notes, it’s easy for key information to slip away unrecorded. Even when notes are captured, they can be disorganized or illegible, rendering them useless. In this post, we explore how to use Amazon … Read more

Fine-tune Llama 2 using QLoRA and Deploy it on Amazon SageMaker with AWS Inferentia2

In this post, we showcase fine-tuning a Llama 2 model using a Parameter-Efficient Fine-Tuning (PEFT) method and deploy the fine-tuned model on AWS Inferentia2. We use the AWS Neuron software development kit (SDK) to access the AWS Inferentia2 device and benefit from its high performance. We then use a large model inference container powered by … Read more

Build an end-to-end MLOps pipeline using Amazon SageMaker Pipelines, GitHub, and GitHub Actions

Machine learning (ML) models do not operate in isolation. To deliver value, they must integrate into existing production systems and infrastructure, which necessitates considering the entire ML lifecycle during design and development. ML operations, known as MLOps, focus on streamlining, automating, and monitoring ML models throughout their lifecycle. Building a robust MLOps pipeline demands cross-functional … Read more

Getir end-to-end workforce management: Amazon Forecast and AWS Step Functions

This is a guest post co-authored by Nafi Ahmet Turgut, Mehmet İkbal Özmen, Hasan Burak Yel, Fatma Nur Dumlupınar Keşir, Mutlu Polatcan and Emre Uzel from Getir. Getir is the pioneer of ultrafast grocery delivery. The technology company has revolutionized last-mile delivery with its grocery in-minutes delivery proposition. Getir was founded in 2015 and operates … Read more

Mitigate hallucinations through Retrieval Augmented Generation using Pinecone vector database & Llama-2 from Amazon SageMaker JumpStart

Despite the seemingly unstoppable adoption of LLMs across industries, they are one component of a broader technology ecosystem that is powering the new AI wave. Many conversational AI use cases require LLMs like Llama 2, Flan T5, and Bloom to respond to user queries. These models rely on parametric knowledge to answer questions. The model … Read more

Techniques for automatic summarization of documents using language models

Summarization is the technique of condensing sizable information into a compact and meaningful form, and stands as a cornerstone of efficient communication in our information-rich age. In a world full of data, summarizing long texts into brief summaries saves time and helps make informed decisions. Summarization condenses content, saving time and improving clarity by presenting … Read more

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

Conversational AI has come a long way in recent years thanks to the rapid developments in generative AI, especially the performance improvements of large language models (LLMs) introduced by training techniques such as instruction fine-tuning and reinforcement learning from human feedback. When prompted correctly, these models can carry coherent conversations without any task-specific training data. … Read more

How Q4 Inc. used Amazon Bedrock, RAG, and SQLDatabaseChain to address numerical and structured dataset challenges building their Q&A chatbot

This post is co-written with Stanislav Yeshchenko from Q4 Inc. Enterprises turn to Retrieval Augmented Generation (RAG) as a mainstream approach to building Q&A chatbots. We continue to see emerging challenges stemming from the nature of the assortment of datasets available. These datasets are often a mix of numerical and text data, at times structured, … Read more