In this post, we will demonstrate how to securely launch notebook instances in a private subnet of an Amazon Virtual Private Cloud (Amazon VPC), with internet access disabled, and to securely connect to Amazon Simple Storage Service (Amazon S3) using VPC endpoints. This post is for network and security architects that support decentralized data science teams on AWS. SageMaker notebook instances can be deployed in a private subnet and we recommend deploying them without internet access. Securing your notebook instances within a private subnet helps prevent unauthorized internet access to your notebook instances, which may contain sensitive information. The examples in this post will use Notebook instance Lifecycle Configurations (LCCs) to connect to an S3 VPC endpoint and download idle-usage detection and termination scripts onto the notebook instance.

Amazon SageMaker notebook instances now support Amazon Linux 2, so you can now create a new Amazon SageMaker notebook instance to start developing your machine learning (ML) models with the latest updates. An obvious question is: what do I need to do to migrate my work from an existing notebook instance that runs on Amazon Linux to a new notebook instance with Amazon Linux 2? In this post, we describe an approach to migrate your work from an existing notebook instance to a new notebook instance. The following diagram shows an overview of the components in a SageMaker notebook instance and how the migration takes place. Note that this solution isn't limited to a particular version of an Amazon Linux image in the source and destination instance. Therefore, we denote the notebook instance that has existing work and data as an existing or source instance, and to refer the notebook instance that we migrate existing work and data to as a new or destination instance.

Amazon SageMaker is a fully managed service that allows you to build, train, deploy, and monitor machine learning (ML) models. Its modular design allows you to pick and choose the features that suit your use cases at different stages of the ML lifecycle. Amazon SageMaker offers capabilities that abstract the heavy lifting of infrastructure management and provides the agility and scalability you desire for large-scale ML activities with different features and a pay-as-you-use pricing model. In this post, we outline the pricing model for Amazon SageMaker and offer some best practices on how you can optimize your cost of using Amazon SageMaker resources to effectively and efficiently build, train, and deploy your ML models. In addition, the post offers programmatic approaches for automatically stopping or detecting idle resources that are incurring costs, allowing you to avoid unnecessary charges. Machine Learning is an iterative process with different computational needs for prototyping the code and exploring the dataset, processing, training, and hosting the model for real-time and offline predictions. In a traditional paradigm, estimating the right amount of computational resources to support different workloads is difficult, and often leads to over-provisioning resources. The modular design of Amazon SageMaker offers flexibility to optimize the scalability, performance, and costs for your ML workloads depending on each stage of the ML lifecycle. The following diagram is a simplified illustration of the modular design for each stage of the ML lifecycle.

Amazon SageMaker is a fully managed service that allows you to build, train, and deploy machine learning (ML) models quickly. Amazon SageMaker removes the heavy lifting from each step of the ML process to make it easier to develop high-quality models. In August 2019, Amazon SageMaker announced the availability of the pre-installed R kernel in all Regions. This capability is available out-of-the-box and comes with the reticulate library pre-installed. This library offers an R interface for the Amazon SageMaker Python SDK, which enables you to invoke Python modules from within an R script.