Category: Amazon SageMaker

July 2023: You can also use the newly launched JumpStart APIs, an extension of the SageMaker Python SDK. These APIs allow you to programmatically deploy and fine-tune a vast selection of JumpStart-supported pre-trained models on your own datasets. Please refer to Amazon SageMaker JumpStart models and algorithms now available via API for more details on how […]

Bias detection in data and model outcomes is a fundamental requirement for building responsible artificial intelligence (AI) and machine learning (ML) models. Unfortunately, detecting bias isn’t an easy task for the vast majority of practitioners due to the large number of ways in which it can be measured and different factors that can contribute to […]

On August 9, 2022, we announced the general availability of cross-account sharing of Amazon SageMaker Pipelines entities. You can now use cross-account support for Amazon SageMaker Pipelines to share pipeline entities across AWS accounts and access shared pipelines directly through Amazon SageMaker API calls. Customers are increasingly adopting multi-account architectures for deploying and managing machine […]

Data preparation is the process of collecting, cleaning, and transforming raw data to make it suitable for insight extraction through machine learning (ML) and analytics. Data preparation is crucial for ML and analytics pipelines. Your model and insights will only be as reliable as the data you use for training them. Flawed data will produce […]

In December 2020, AWS announced the general availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that helps you quickly and easily get started with machine learning (ML). JumpStart provides one-click fine-tuning and deployment of a wide variety of pre-trained models across popular ML tasks, as well as a selection of end-to-end solutions that […]

Machine learning (ML) model deployments can have very demanding performance and latency requirements for businesses today. Use cases such as fraud detection and ad placement are examples where milliseconds matter and are critical to business success. Strict service level agreements (SLAs) need to be met, and a typical request may require multiple steps such as […]

Today, a lot of customers are using TensorFlow to train deep learning models for their clickthrough rate in advertising and personalization recommendations in ecommerce. As the behavior of their clients change, they can accumulate large amounts of new data every day. Model iteration is one of a data scientist’s daily jobs, but they face the […]

So much data, so little time. Machine learning (ML) experts, data scientists, engineers and enthusiasts have encountered this problem the world over. From natural language processing to computer vision, tabular to time series, and everything in-between, the age-old problem of optimizing for speed when running data against as many GPUs as you can get has […]

Amazon SageMaker JumpStart solutions are a feature within Amazon SageMaker Studio that allow a simple-click experience to set up your own machine learning (ML) workflows. When you launch a solution, various of AWS resources are set up in your account to demonstrate how the business problem can be solved using the pre-built architecture. The solutions […]

Fraudulent activities severely impact many industries, such as e-commerce, social media, and financial services. Frauds could cause a significant loss for businesses and consumers. American consumers reported losing more than $5.8 billion to frauds in 2021, up more than 70% over 2020. Many techniques have been used to detect fraudsters—rule-based filters, anomaly detection, and machine […]