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Announcing RStudio on Amazon SageMaker

#artificialintelligence

As more organizations migrate their data science work to the cloud, they naturally want to bring along their favorite data science tools, including RStudio, R, and Python. While RStudio provides many different ways to support an organization's cloud strategyOpens a new window, we've heard from many customers who also use Amazon SageMaker. They wanted an easier way to combine RStudio's professional products with SageMaker's rich machine learning and deep learning capabilities, and to incorporate RStudio into their data science infrastructure on SageMaker. Based on this feedback, we are excited to announce RStudio on Amazon SageMaker, developed in collaboration with the SageMaker team. Amazon SageMakerOpens a new window helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning models quickly by bringing together a broad set of capabilities purpose-built for machine learning.


Machine Learning at the Edge with AWS Outposts and Amazon SageMaker

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As customers continue to come up with new use-cases for machine learning, data gravity is as important as ever. Where latency and network connectivity is not an issue, generating data in one location (such as a manufacturing facility) and sending it to the cloud for inference is acceptable for some use-cases. With other critical use-cases, such as fraud detection for financial transactions, product quality in manufacturing, or analyzing video surveillance in real-time, customers are faced with the challenges that come with having to move that data to the cloud first. One of the challenges customers are facing with performing inference in the cloud is the lack of real-time inference and/or security requirements preventing user data to be sent or stored in the cloud. Tens of thousands of customers use Amazon SageMaker to accelerate their Machine Learning (ML) journey by helping data scientists and developers to prepare, build, train, and deploy machine learning models quickly.


Amazon SageMaker price reductions: Up to 18% lower prices on ml.p3 and ml.p2 instances

#artificialintelligence

Effective October 1st, 2020, we're reducing the prices for ml.p3 and ml.p2 instances in Amazon SageMaker by up to 18% so you can maximize your machine learning (ML) budgets and innovate with deep learning using these accelerated compute instances. The new price reductions apply to ml.p3 and ml.p2 instances of all sizes for Amazon SageMaker Studio notebooks, on-demand notebooks, processing, training, real-time inference, and batch transform. Customers including Intuit, Thomson Reuters, Cerner, and Zalando are already reducing their total cost of ownership (TCO) by at least 50% using Amazon SageMaker. Amazon SageMaker removes the heavy lifting from each step of the ML process and makes it easy to apply advanced deep learning techniques at scale. Amazon SageMaker provides lower TCO because it's a fully managed service, so you don't need to build, manage, or maintain any infrastructure and tooling for your ML workloads.


Announcing RStudio on Amazon SageMaker

#artificialintelligence

As more organizations migrate their data science work to the cloud, they naturally want to bring along their favorite data science tools, including RStudio, R, and Python. While RStudio provides many different ways to support an organization's cloud strategyOpens a new window, we've heard from many customers who also use Amazon SageMaker. They wanted an easier way to combine RStudio's professional products with SageMaker's rich machine learning and deep learning capabilities, and to incorporate RStudio into their data science infrastructure on SageMaker. Based on this feedback, we are excited to announce RStudio on Amazon SageMaker, developed in collaboration with the SageMaker team. Amazon SageMakerOpens a new window helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning models quickly by bringing together a broad set of capabilities purpose-built for machine learning.


AWS releases SageMaker to make it easier to build and deploy machine learning models

#artificialintelligence

Cloud services are designed to take away a lot of the complexity associated with managing a particular process, whether that's software or infrastructure. Today, machine learning is quickly gaining traction with developers, and AWS wants to help remove some of the obstacles associated with building and deploying machine learning models.