Category: Amazon SageMaker

Today Amazon SageMaker announced the support of SageMaker training instance fallbacks for Amazon SageMaker Automatic Model Tuning (AMT) that allow users to specify alternative compute resource configurations. SageMaker automatic model tuning finds the best version of a model by running many training jobs on your dataset using the ranges of hyperparameters that you specify for your […]

In November 2021, in collaboration with RStudio PBC, we announced the general availability of RStudio on Amazon SageMaker, the industry’s first fully managed RStudio Workbench IDE in the cloud. You can now bring your current RStudio license to easily migrate your self-managed RStudio environments to Amazon SageMaker in just a few simple steps. RStudio is […]

October 2023: Starting in April 26th, 2024, you can no longer access Amazon SageMaker Edge Manager. For more information about continuing to deploy your models to edge devices, see SageMaker Edge Manager end of life. Internet of Things (IoT) has enabled customers in multiple industries, such as manufacturing, automotive, and energy, to monitor and control […]

This is a guest post by Viktor Enrico Jeney, Senior Machine Learning Engineer at Adspert. Adspert is a Berlin-based ISV that developed a bid management tool designed to automatically optimize performance marketing and advertising campaigns. The company’s core principle is to automate maximization of profit of ecommerce advertising with the help of artificial intelligence. The […]

Amazon SageMaker Feature Store helps data scientists and machine learning (ML) engineers securely store, discover, and share curated data used in training and prediction workflows. Feature Store is a centralized store for features and associated metadata, allowing features to be easily discovered and reused by data scientist teams working on different projects or ML models. […]

After data scientists carefully come up with a satisfying machine learning (ML) model, the model must be deployed to be easily accessible for inference by other members of the organization. However, deploying models at scale with optimized cost and compute efficiencies can be a daunting and cumbersome task. Amazon SageMaker endpoints provide an easily scalable […]

Feature engineering is one of the most challenging aspects of the machine learning (ML) lifecycle and a phase where the most amount of time is spent—data scientists and ML engineers spend 60–70% of their time on feature engineering. AWS introduced Amazon SageMaker Feature Store during AWS re:Invent 2020, which is a purpose-built, fully managed, centralized […]

There have been many recent advancements in the NLP domain. Pre-trained models and fully managed NLP services have democratised access and adoption of NLP. Amazon Comprehend is a fully managed service that can perform NLP tasks like custom entity recognition, topic modelling, sentiment analysis and more to extract insights from data without the need of any prior […]

Amazon SageMaker Studio is a web-based integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models. Each onboarded user in Studio has their own dedicated set of resources, such as compute instances, a home directory on an Amazon Elastic File System (Amazon EFS) volume, and […]

This is the second part of a series that showcases the machine learning (ML) lifecycle with a data mesh design pattern for a large enterprise with multiple lines of business (LOBs) and a Center of Excellence (CoE) for analytics and ML. In part 1, we addressed the data steward persona and showcased a data mesh […]