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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.


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.


GitHub - aws/amazon-sagemaker-examples: Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

#artificialintelligence

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.


Review: AWS AI and Machine Learning stacks up

#artificialintelligence

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.


Hosting VS Code on SageMaker Studio

#artificialintelligence

ML teams need the flexibility to choose notebooks or full-fledge IDE when working on a project. They may even use multiple IDEs in the same project. It makes the climb easier and gives more chances to summit. So far in SageMaker Studio you could pick between JupyterLab and RStudio. In this post, I will show how you can also host VS Code on a Studio environment.