Launch Amazon SageMaker Autopilot experiments directly from within Amazon SageMaker Pipelines to easily automate MLOps workflows

Amazon SageMaker Autopilot, a low-code machine learning (ML) service that automatically builds, trains, and tunes the best ML models based on tabular data, is now integrated with Amazon SageMaker Pipelines, the first purpose-built continuous integration and continuous delivery (CI/CD) service for ML. This enables the automation of an end-to-end flow of building ML models using … Read more

AI21 Jurassic-1 foundation model is now available on Amazon SageMaker

Today we are excited to announce that AI21 Jurassic-1 (J1) foundation models are available for customers using Amazon SageMaker. Jurassic-1 models are highly versatile, capable of both human-like text generation, as well as solving complex tasks such as question answering, text classification, and many others. You can easily try out this model and use it … Read more

Introducing AWS AI Service Cards: A new resource to enhance transparency and advance responsible AI

Artificial intelligence (AI) and machine learning (ML) are some of the most transformative technologies we will encounter in our generation—to tackle business and societal problems, improve customer experiences, and spur innovation. Along with the widespread use and growing scale of AI comes the recognition that we must all build responsibly. At AWS, we think responsible … Read more

Introducing Amazon Kendra tabular search for HTML Documents

Amazon Kendra is an intelligent search service powered by machine learning (ML). Kendra reimagines enterprise search for your websites and applications so your employees and customers can easily find the content they’re looking for, even when it’s scattered across multiple locations and content repositories within your organization. Amazon Kendra users can now quickly find the … Read more

Enterprise administrative controls, simple sign-up, and expanded programming language support for Amazon CodeWhisperer

Amazon CodeWhisperer is a machine learning (ML)-powered service that helps improve developer productivity by generating code recommendations based on developers’ prior code and comments. Today, we are excited to announce that AWS administrators can now enable CodeWhisperer for their organization with single sign-n (SSO) authentication. Administrators can easily integrate CodeWhisperer with their existing workforce identity … Read more

Optimize hyperparameters with Amazon SageMaker Automatic Model Tuning

Machine learning (ML) models are taking the world by storm. Their performance relies on using the right training data and choosing the right model and algorithm. But it doesn’t end here. Typically, algorithms defer some design decisions to the ML practitioner to adopt for their specific data and task. These deferred design decisions manifest themselves … Read more

How JPMorgan Chase & Co. uses AWS DeepRacer events to drive global cloud adoption

This is a guest post by Stephen Carrad, Vice President at JP Morgan Chase & Co. JPMorgan & Chase Co. started its cloud journey four years ago, building the integrations required to deploy cloud-native applications into the cloud in a resilient and secure manner. In the first year, three applications tentatively dipped their toes into … Read more

Apply fine-grained data access controls with AWS Lake Formation and Amazon EMR from Amazon SageMaker Studio

Amazon SageMaker Studio is a fully integrated development environment (IDE) for machine learning (ML) that enables data scientists and developers to perform every step of the ML workflow, from preparing data to building, training, tuning, and deploying models. Studio comes with built-in integration with Amazon EMR so that data scientists can interactively prepare data at … Read more

AWS Cloud technology for near-real-time cardiac anomaly detection using data from wearable devices

Cardiovascular diseases (CVDs) are the number one cause of death globally: more people die each year from CVDs than from any other cause. The COVID-19 pandemic made organizations change healthcare delivery to reduce staff contact with sick people and the overall pressure on the healthcare system. This technology enables organizations to deliver telehealth solutions, which … Read more

Identifying landmarks with Amazon Rekognition Custom Labels

Amazon Rekognition is a computer vision service that makes it simple to add image and video analysis to your applications using proven, highly scalable, deep learning technology that does not require machine learning (ML) expertise. With Amazon Rekognition, you can identify objects, people, text, scenes, and activities in images and videos and detect inappropriate content. … Read more

Implementing Amazon Forecast in the retail industry: A journey from POC to production

Amazon Forecast is a fully managed service that uses statistical and machine learning (ML) algorithms to deliver highly accurate time-series forecasts. Recently, based on Amazon Forecast, we helped one of our retail customers achieve accurate demand forecasting, within 8 weeks. The solution improved the manual forecast by an average of 10% in regards to the … Read more

Accelerate multilingual workflows with a customizable translation solution built with Amazon Translate

Enterprises often need to communicate effectively to a large base of customers, partners, and stakeholders across several different languages. They need to translate and localize content such as marketing materials, product content assets, operational manuals, and legal documents. Each business unit in the enterprise has different translation workloads and often manages their own translation requirements … Read more

ByteDance saves up to 60% on inference costs while reducing latency and increasing throughput using AWS Inferentia

This is a guest blog post co-written with Minghui Yu and Jianzhe Xiao from Bytedance. ByteDance is a technology company that operates a range of content platforms to inform, educate, entertain, and inspire people across languages, cultures, and geographies. Users trust and enjoy our content platforms because of the rich, intuitive, and safe experiences they … Read more

Real-time analysis of customer sentiment using AWS

Companies that sell products or services online need to constantly monitor customer reviews left on their website after purchasing a product. The company’s marketing and customer service departments analyze these reviews to understand customer sentiment. For example, marketing could use this data to create campaigns targeting different customer segments. Customer service departments could use this … Read more

Amazon Rekognition Labels adds 600 new labels, including landmarks, and now detects dominant colors

Amazon Rekognition offers pre-trained and customizable computer vision capabilities to extract information and insights from images and videos. One such capability is Amazon Rekognition Labels, which detects objects, scenes, actions, and concepts in images. Customers such as Synchronoss, Shutterstock, and Nomad Media use Amazon Rekognition Labels to automatically add metadata to their content library and … Read more

Generate cold start forecasts for products with no historical data using Amazon Forecast, now up to 45% more accurate

Now with Amazon Forecast, you can generate up to 45% more accurate forecasts for products with no historical data. Forecast is a managed service that uses machine learning (ML) to generate accurate demand forecasts, without requiring any ML experience. Accurate forecasting is the foundation for inventory optimization, logistics planning, and workforce management and it enables … Read more

Identify key insights from text documents through fine-tuning and HPO with Amazon SageMaker JumpStart

Organizations across industries such as retail, banking, finance, healthcare, manufacturing, and lending often have to deal with vast amounts of unstructured text documents coming from various sources, such as news, blogs, product reviews, customer support channels, and social media. These documents contain critical information that’s key to making important business decisions. As an organization grows, … Read more

Your guide to AI/ML at AWS re:Invent 2022

AWS re:Invent season is upon us again! Just a few days to go until re:Invent takes place for the 11th year in Las Vegas, Nevada. The Artificial Intelligence and Machine Learning team at AWS has been working hard to offer amazing content, an outstanding AWS DeepRacer experience, and much more. In this post, we give … Read more

AlexaTM 20B is now available in Amazon SageMaker JumpStart

Today, we announce the public availability of Amazon’s state-of-the-art Alexa Teacher Model with 20 billion parameters  (AlexaTM 20B) through Amazon SageMaker JumpStart, SageMaker’s machine learning hub. AlexaTM 20B is a multilingual large-scale sequence-to-sequence (seq2seq) language model developed by Amazon. You can use AlexaTM 20B for a wide range of industry use-cases, from summarizing financial reports … Read more

How Yara is using MLOps features of Amazon SageMaker to scale energy optimization across their ammonia plants

Yara is the world’s leading crop nutrition company and a provider of environmental and agricultural solutions. Yara’s ambition is focused on growing a nature-positive food future that creates value for customers, shareholders, and society at large, and delivers a more sustainable food value chain. Supporting our vision of a world without hunger and a planet … Read more

Build high performing image classification models using Amazon SageMaker JumpStart

Image classification is a computer vision-based machine learning (ML) technique that allows you to classify images. Some well-known examples of image classification include classifying handwritten digits, medical image classification, and facial recognition. Image classification is a useful technique with several business applications, but building a good image classification model isn’t trivial. Several considerations can play … Read more

Large-scale feature engineering with sensitive data protection using AWS Glue interactive sessions and Amazon SageMaker Studio

Organizations are using machine learning (ML) and AI services to enhance customer experience, reduce operational cost, and unlock new possibilities to improve business outcomes. Data underpins ML and AI use cases and is a strategic asset to an organization. As data is growing at an exponential rate, organizations are looking to set up an integrated, … Read more

Build a cross-account MLOps workflow using the Amazon SageMaker model registry

A well-designed CI/CD pipeline is essential to scale any software development workflow effectively. When designing production CI/CD pipelines, AWS recommends leveraging multiple accounts to isolate resources, contain security threats and simplify billing-and data science pipelines are no different. At AWS, we’re continuing to innovate to simplify the MLOps workflow. In this post, we discuss some … Read more

Enabling hybrid ML workflows on Amazon EKS and Amazon SageMaker with one-click Kubeflow on AWS deployment

Today, many AWS customers are building enterprise-ready machine learning (ML) platforms on Amazon Elastic Kubernetes Service (Amazon EKS) using Kubeflow on AWS (an AWS-specific distribution of Kubeflow) across many use cases, including computer vision, natural language understanding, speech translation, and financial modeling. With the latest release of open-source Kubeflow v1.6.1, the Kubeflow community continues to … Read more

Get more control of your Amazon SageMaker Data Wrangler workloads with parameterized datasets and scheduled jobs

Data is transforming every field and every business. However, with data growing faster than most companies can keep track of, collecting data and getting value out of that data is a challenging thing to do. A modern data strategy can help you create better business outcomes with data. AWS provides the most complete set of … Read more

Detect multicollinearity, target leakage, and feature correlation with Amazon SageMaker Data Wrangler

In machine learning (ML), data quality has direct impact on model quality. This is why data scientists and data engineers spend significant amount of time perfecting training datasets. Nevertheless, no dataset is perfect—there are trade-offs to the preprocessing techniques such as oversampling, normalization, and imputation. Also, mistakes and errors could creep in at various stages … Read more