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An overview of the architecture, data, and modeling you need to assess contribution to conversion in multi-touch customer journeys.View the full article

We are excited to announce new pricing for training custom tabular models in Amazon SageMaker Canvas, a no-code tool that enables customers to easily create highly accurate ML models without writing code. SageMaker Canvas supports numeric prediction (regression), 2 category prediction (binary classification), 3+ category prediction (multi-class classification), and time-series forecasting for tabular models. Previously, model training charges were based on the number of cells in the dataset used to train the model. Now, the charges are based on SageMaker training and processing hours used to train the model. View the full article

We are excited to announce that Amazon SageMaker now provides capability to enable existing Studio Classic domains to opt-in to the new SageMaker Studio experience via SageMaker Console. View the full article

Amazon SageMaker Canvas now supports deploying machine learning (ML) models to real-time inferencing endpoints, allowing you take your ML models to production and drive action based on ML powered insights. SageMaker Canvas is a no-code workspace that enables analysts and citizen data scientists to generate accurate ML predictions for their business needs. View the full article

Amazon SageMaker Canvas is a service for business analysts to generate machine learning (ML) and artificial intelligence (AI) predictions without having to write a single line of code. As announced on October 5, customers can access and evaluate foundation models (FMs) to generate and summarize content. View the full article

As the Northern Hemisphere enjoys early fall and pumpkins take over the local farmers markets and coffee flavors here in the United States, we’re also just 50 days away from re:Invent 2023! But before we officially enter pre:Invent sea­­son, let’s have a look at some of last week’s exciting news and announcements. Last Week’s Launches Here are some launches that got my attention: AWS Control Tower – AWS Control Tower released 22 proactive controls and 10 AWS Security Hub detective controls to help you meet regulatory requirements and meet control objectives such as encrypting data in transit, encrypting data at rest, or using strong authentication. For more details and a list of controls, check out the AWS Control Tower user guide. Amazon Bedrock – Just a week after Amazon Bedrock became available in AWS Regions US East (N. Virginia) and US West (Oregon), Amazon Bedrock is now also available in the Asia Pacific (Tokyo) AWS Region. To get started building and scaling generative AI applications with foundation models, check out the Amazon Bedrock documentation, explore the generative AI space at community.aws, and get hands-on with the Amazon Bedrock workshop. Amazon OpenSearch Service – You can now run OpenSearch version 2.9 in Amazon OpenSearch Service with improvements to search, observability, security analytics, and machine learning (ML) capabilities. OpenSearch Service has expanded its geospatial aggregations support in version 2.9 to gather insights on high-level overview of trends and patterns and establish correlations within the data. OpenSearch Service 2.9 now also comes with OpenSearch Service Integrations to take advantage of new schema standards such as OpenTelemetry and supports managing and overlaying alerts and anomalies onto dashboard visualization line charts. Amazon SageMaker – SageMaker Feature Store now supports a fully managed, in-memory online store to help you retrieve features for model serving in real time for high throughput ML applications. The new online store is powered by ElastiCache for Redis, an in-memory data store built on open-source Redis. The SageMaker developer guide has all the details. Also, SageMaker Model Registry added support for private model repositories. You can now register models that are stored in private Docker repositories and track all your models across multiple private AWS and non-AWS model repositories in one central service, simplifying ML operations (MLOps) and ML governance at scale. The SageMaker Developer Guide shows you how to get started. Amazon SageMaker Canvas – SageMaker Canvas expanded its support for ready-to-use models to include foundation models (FMs). You can now access FMs such as Claude 2, Amazon Titan, and Jurassic-2 (powered by Amazon Bedrock) as well as publicly available models such as Falcon and MPT (powered by SageMaker JumpStart) through a no-code chat interface. Check out the SageMaker Developer Guide for more details. For a full list of AWS announcements, be sure to keep an eye on the What's New at AWS page. Other AWS News Here are some additional blog posts and news items that you might find interesting: Behind the scenes on AWS contributions to open-source databases – This post shares some of the more substantial open-source contributions AWS has made in the past two years to upstream databases, introduces some key contributors, and shares how AWS approaches upstream work in our database services. Fast and cost-effective Llama 2 fine-tuning with AWS Trainium – This post shows you how to fine-tune the Llama 2 model from Meta on AWS Trainium, a purpose-built accelerator for LLM training, to reduce training times and costs. Code Llama code generation models from Meta are now available via Amazon SageMaker JumpStart – You can now deploy Code Llama FMs, developed by Meta, with one click in SageMaker JumpStart. This post walks you through the details. Upcoming AWS Events Check your calendars and sign up for these AWS events: Build On Generative AI – Season 2 of this weekly Twitch show about all things generative AI is in full swing! Every Monday, 9:00 US PT, my colleagues Emily and Darko look at new technical and scientific patterns on AWS, invite guest speakers to demo their work, and show us how they built something new to improve the state of generative AI. In today’s episode, Emily and Darko discussed how to translate unstructured documents into structured data. Check out show notes and the full list of episodes on community.aws. AWS Community Days – Join a community-led conference run by AWS user group leaders in your region: DMV (DC, Maryland, Virginia) (October 13), Italy (October 18), UAE (October 21), Jaipur (November 4), Vadodara (November 4), and Brasil (November 4). AWS Innovate: Every Application Edition – Join our free online conference to explore cutting-edge ways to enhance security and reliability, optimize performance on a budget, speed up application development, and revolutionize your applications with generative AI. Register for AWS Innovate Online Americas and EMEA on October 19 and AWS Innovate Online Asia Pacific & Japan on October 26. AWS re:Invent (November 27 – December 1) – Join us to hear the latest from AWS, learn from experts, and connect with the global cloud community. Browse the session catalog and attendee guides and check out the re:Invent highlights for generative AI. You can browse all upcoming in-person and virtual events. That’s all for this week. Check back next Monday for another Weekly Roundup! — Antje This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS! View the full article

Amazon QuickSight now supports predictive analytics using machine learning (ML) models created in Amazon SageMaker Canvas, without writing a single line of code. QuickSight authors can now export data to SageMaker Canvas, build ML models, and share them back to QuickSight for consumption. This allows you to build predictive dashboards for better insights. With this new capability, you can evolve your analytics from descriptive to predictive capabilities, enabling the entire organization with a forward-looking view of the business. View the full article

Amazon SageMaker Canvas is a service for business analysts to generate predictions without having to write a single line of code. Starting today, business analysts can access and evaluate foundation models (FMs) to generate and summarize content using foundation models, in addition to the ready-to-use models for common use cases such as sentiment analysis, object and text detection, and data extraction from documents. View the full article

We’re excited to announce expanded capabilities for data preparation and analysis in Amazon SageMaker Canvas including replacing missing values, replacing outliers, and the flexibility to choose different sample sizes for your datasets. Amazon SageMaker Canvas is a visual point-and-click interface that enables business analysts to generate accurate ML predictions on their own — without requiring any machine learning (ML) experience or having to write a single line of code. SageMaker Canvas makes it easy to access and combine data from a variety of sources, automatically clean data, and build ML models to generate accurate predictions with a few clicks. View the full article

Amazon SageMaker Automatic Model Tuning now supports specifying multiple alternate SageMaker training instance types to make tuning jobs more robust when the preferred instance type is not available due to insufficient capacity. View the full article

You can now use Amazon SageMaker Model Building Pipelines with AWS Resource Access Manager (AWS RAM) to securely share pipeline entities across AWS accounts and access shared pipelines through direct API calls. A multi-account strategy helps achieve data, project, and team isolation while supporting software development lifecycle steps. Cross-account pipeline sharing can support a multi-account strategy without the added hassle of logging in and out of multiple accounts. For example, cross-account pipeline sharing can improve machine learning testing and deployment workflows by sharing resources across staging and production accounts. View the full article

We’re excited to announce the support for encryption at rest for datasets and machine learning (ML) models on Amazon SageMaker Canvas using customer managed keys with AWS Key Management Service (KMS). Amazon SageMaker Canvas is a visual point-and-click interface that enables business analysts to generate accurate ML predictions on their own — without requiring any machine learning experience or having to write a single line of code. SageMaker Canvas makes it easy to access and combine data from a variety of sources, automatically clean data, and build ML models to generate accurate predictions with a few clicks. View the full article

Amazon SageMaker expands access to new ML instances so customers can deploy models on the best instance for their workloads. Now, customers can use ml.g5, ml.p4d, and ml.c6i instances for Asynchronous and Real-time model deployment options. View the full article

Amazon SageMaker Automatic Model Tuning enables you to find the most accurate version of a machine learning (ML) model by finding the optimal set of hyperparameter configurations for your dataset. SageMaker Automatic Model Tuning now supports increased limits for two service quotas, with up to 50% higher number of total training jobs that can be run per tuning job and maximum number of hyperparameters that can be searched per tuning job. View the full article

Amazon SageMaker model training now supports heterogeneous clusters, which enables launching training jobs that use multiple instance types in a single job. This new capability can improve your training cost by running different parts of the model training on the most suitable instance type. For example, we recently trained a ResNet-50 computer vision model on a heterogeneous cluster with ml.g5.xl and ml.c5n.2xl instances. This training job resulted in 13% lower cost than training the same model on a cluster with only ml.g5.xl instances with the same accuracy. View the full article

Today, we’re pleased to announce that Amazon SageMaker Autopilot experiments run up to 2x faster to generate ML models with high model performance. Amazon SageMaker Autopilot is a low-code machine learning (ML) product that automatically builds, trains, and tunes the best ML models based on your data while allowing you to maintain full control and visibility. However, as dataset sizes grow, training and tuning models can become computationally expensive. View the full article

Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, update, search, and share machine learning (ML) features. The service provides feature management capabilities such as enabling easy feature reuse, low latency serving, time travel, and ensuring consistency between features used in training and inference workflows. A feature group is a logical grouping of ML features whose organization and structure is defined by a feature group schema. Until today, customers could add metadata tags only to feature groups which in turn enabled easy search and discovery of a feature group. To search for a specific feature however was more complicated. Customers needed to know which feature group the feature belongs and then scan for the relevant feature in the feature group, leading to additional overhead while searching for features.. View the full article

Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, update, search, and share machine learning (ML) features. The service provides feature management capabilities such as enabling easy feature reuse, low latency serving, time travel, and ensuring consistency between features used in training and inference. Until today, SageMaker Feature Store monitoring was limited to consumed read and write units, which gave a limited view of the operational efficiency of the feature store. View the full article

Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, update, search, and share machine learning (ML) features. The service provides feature management capabilities such as enabling easy feature reuse, low latency serving, time travel, and ensuring consistency between features used in training and inference workflows. A feature group is a logical grouping of ML features whose organization and structure is defined by a feature group schema. Until today, the features in a feature group were defined at the time of feature group creation, and the feature group schema was immutable. View the full article

Amazon SageMaker provides a suite of built-in algorithms, pre-trained models, and pre-built solution templates to help data scientists and machine learning practitioners get started on training and deploying machine learning models quickly. These algorithms and models can be used for both supervised and unsupervised learning. They can process various types of input data including tabular, image, and text. View the full article

Amazon SageMaker Ground Truth helps you build high-quality training datasets for your machine learning (ML) models. With SageMaker Ground Truth, you can use workers from Amazon Mechanical Turk, a vendor company that you choose, or your own private workforce to create labeled datasets for training ML models. View the full article

We are excited to announce that Amazon SageMaker Ground Truth now provides support so you can generate labeled synthetic data without collecting large amounts of real-world, manually labeled data. Amazon SageMaker provides two data labeling offerings, Amazon SageMaker Ground Truth Plus and Amazon SageMaker Ground Truth. You can use both options to identify raw data (such as images, text files, and videos) and add informative labels to create high-quality training datasets for your machine learning (ML) models. View the full article

Amazon SageMaker Canvas now supports VPC endpoints enabling secure, private connectivity to other AWS services. SageMaker Canvas is a visual point-and-click service that enables business analysts to generate accurate ML models for insights and predictions on their own — without requiring any machine learning experience or having to write a single line of code. View the full article

Amazon SageMaker Canvas accelerates onboarding with new interactive product tours and sample datasets for different use cases. Amazon SageMaker Canvas is a visual point-and-click interface that enables business analysts to generate accurate machine learning (ML) models for insights and predictions on their own — without requiring any machine learning experience or having to write a single line of code. View the full article

SageMaker Experiments now supports granular metrics and graphs to help you better understand results from training jobs performed on SageMaker. Amazon SageMaker Experiments is a capability of Amazon SageMaker that lets you organize, track, compare and evaluate machine learning (ML) experiments. With this launch, you can now view precision and recall (PR) curves, receiver operating characteristics (ROC curve), and confusion matrix. You can use these curves to understand false positives/negatives, and tradeoffs between performance and accuracy for a model trained on SageMaker. You can also better compare multiple training runs and identify the best model for your use-case. View the full article