These examples show you how to use SageMaker Processing jobs to run data processing workloads. These examples show you how to use SageMaker Pipelines to create, automate and manage end-to-end Machine Learning workflows. These examples show you how to train and host in pre-built deep learning framework containers using the SageMaker Python SDK. These examples show you how to build Machine Learning models with frameworks like Apache Spark or Scikit-learn using SageMaker Python SDK. These examples show how to use Amazon SageMaker for model training, hosting, and inference through Apache Spark using SageMaker Spark.
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.
The R programming language is one of the most commonly used languages in the scientific space, being one of the most commonly used languages for machine learning (probably second following python) and arguably the most popular language amongst mathematicians and statisticians. It is easy to get started with, free to use, with support for many scientific and visualisation libraries. While R can help you analyse your data, the more data you have the more compute power you require and the more impactful your analysis is, the more repeatability and reproducibility is required. Analysts and Data Scientists need to find ways to fulfil such requirements. In this post we briefly describe the main ways of running your R workloads on the cloud, making use of Amazon SageMaker, the end-to-end Machine Learning cloud offering of AWS.
Amazon Web Services (AWS) is happy to announce the general availability of Notebooks within Amazon SageMaker Studio. Amazon SageMaker Studio supports on-the-fly selection of machine learning (ML) instance types, optimized and pre-packaged Amazon SageMaker Images, and sharing of Jupyter notebooks. You can switch a notebook from using a kernel on one instance type to another, for example from ml.t3.medium to ml.p3.2xlarge, without interrupting your work or managing infrastructure. Moving from one instance to another is seamless, and you can continue working while the instance launches. Your notebooks and data are available instantly on the new instance due to the Amazon Elastic File System (Amazon EFS) that is created for your Amazon SageMaker Studio domain.
Amazon Web Services claims to have the broadest and most complete set of machine learning capabilities. I honestly don't know how the company can claim those superlatives with a straight face: Yes, the AWS machine learning offerings are broad and fairly complete and rather impressive, but so are those of Google Cloud and Microsoft Azure. Amazon SageMaker Clarify is the new add-on to the Amazon SageMaker machine learning ecosystem for Responsible AI. SageMaker Clarify integrates with SageMaker at three points: in the new Data Wrangler to detect data biases at import time, such as imbalanced classes in the training set, in the Experiments tab of SageMaker Studio to detect biases in the model after training and to explain the importance of features, and in the SageMaker Model Monitor, to detect bias shifts in a deployed model over time. Historically, AWS has presented its services as cloud-only.