Artificial Intelligence
Author: Davide Gallitelli
Time series forecasting with Amazon SageMaker AutoML
In this blog post, we explore a comprehensive approach to time series forecasting using the Amazon SageMaker AutoMLV2 Software Development Kit (SDK). SageMaker AutoMLV2 is part of the SageMaker Autopilot suite, which automates the end-to-end machine learning workflow from data preparation to model deployment.
Announcing support for Llama 2 and Mistral models and streaming responses in Amazon SageMaker Canvas
Launched in 2021, Amazon SageMaker Canvas is a visual, point-and-click service for building and deploying machine learning (ML) models without the need to write any code. Ready-to-use Foundation Models (FMs) available in SageMaker Canvas enable customers to use generative AI for tasks such as content generation and summarization. We are thrilled to announce the latest […]
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 […]
Optimizing costs for Amazon SageMaker Canvas with automatic shutdown of idle apps
Amazon SageMaker Canvas is a rich, no-code Machine Learning (ML) and Generative AI workspace that has allowed customers all over the world to more easily adopt ML technologies to solve old and new challenges thanks to its visual, no-code interface. It does so by covering the ML workflow end-to-end: whether you’re looking for powerful data […]
Empower your business users to extract insights from company documents using Amazon SageMaker Canvas and Generative AI
Enterprises seek to harness the potential of Machine Learning (ML) to solve complex problems and improve outcomes. Until recently, building and deploying ML models required deep levels of technical and coding skills, including tuning ML models and maintaining operational pipelines. Since its introduction in 2021, Amazon SageMaker Canvas has enabled business analysts to build, deploy, […]
Optimize deployment cost of Amazon SageMaker JumpStart foundation models with Amazon SageMaker asynchronous endpoints
In this post, we target these situations and solve the problem of risking high costs by deploying large foundation models to Amazon SageMaker asynchronous endpoints from Amazon SageMaker JumpStart. This can help cut costs of the architecture, allowing the endpoint to run only when requests are in the queue and for a short time-to-live, while scaling down to zero when no requests are waiting to be serviced. This sounds great for a lot of use cases; however, an endpoint that has scaled down to zero will introduce a cold start time before being able to serve inferences.
Scale training and inference of thousands of ML models with Amazon SageMaker
Training and serving thousands of models requires a robust and scalable infrastructure, which is where Amazon SageMaker can help. SageMaker is a fully managed platform that enables developers and data scientists to build, train, and deploy ML models quickly, while also offering the cost-saving benefits of using the AWS Cloud infrastructure. In this post, we explore how you can use SageMaker features, including Amazon SageMaker Processing, SageMaker training jobs, and SageMaker multi-model endpoints (MMEs), to train and serve thousands of models in a cost-effective way. To get started with the described solution, you can refer to the accompanying notebook on GitHub.
Train a time series forecasting model faster with Amazon SageMaker Canvas Quick build
Today, Amazon SageMaker Canvas introduces the ability to use the Quick build feature with time series forecasting use cases. This allows you to train models and generate the associated explainability scores in under 20 minutes, at which point you can generate predictions on new, unseen data. Quick build training enables faster experimentation to understand how […]
Provision and manage ML environments with Amazon SageMaker Canvas using AWS CloudFormation, AWS CDK and AWS Service Catalog
June 2024: This blog post has been updated to reflect the updates in the architecture described. Additionally, support for CloudFormation templates has been added. The proliferation of machine learning (ML) across a wide range of use cases is becoming prevalent in every industry. However, this outpaces the increase in the number of ML practitioners who […]
Use a pre-signed URL to provide your business analysts with secure access to Amazon SageMaker Canvas
Agility and security have historically been two aspects of IT of paramount importance for any company. With the simplification of access to advanced IT technologies thanks to low-code and no-code (LCNC) tools, an even bigger number of people must be enabled to access resources, without impacting security. For many companies, the solution has been to […]