Tutorial

Introduction¶ In today’s data-driven world, data scientists are constantly looking for efficient ways to analyze and process large datasets. Amazon SageMaker provides a powerful platform for building, training, and deploying machine learning models. One of the key components of SageMaker is the notebook, which allows data scientists to create and execute interactive code for data …

Guide to Creating Amazon SageMaker Notebook Jobs with API Support Read More »

Introduction¶ In today’s digitally-driven era, downtime can be disastrous for businesses. Even a few minutes of service interruption can lead to severe financial losses and a damaged reputation. To mitigate such risks, it is crucial to have robust disaster recovery strategies in place. Amazon Web Services (AWS) understands this need and has introduced a groundbreaking …

Amazon Route 53 Application Recovery Controller launches zonal autoshift Read More »

Introduction¶ Containerization has become a popular solution in modern software development, enabling developers to package applications and their dependencies into lightweight, portable containers. However, with the rise of container usage, security concerns have also increased. Vulnerabilities in container images can result in devastating consequences, including data breaches, unauthorized access, and service disruptions. To address these …

A Comprehensive Guide to Enhancing Container Image Security with Amazon Inspector Read More »

Introduction¶ Application resilience is a critical aspect of modern software development. With the increasing reliance on cloud infrastructure, it is crucial for organizations to ensure their applications can withstand potential failures and disruptions. To address this need, Amazon Web Services (AWS) has introduced an innovative solution called AWS Fault Injection Service. This service empowers developers …

AWS Fault Injection Service: Unleashing the Power of Scenario Testing for Improved Application Resilience Read More »

Amazon SageMaker Studio is a fully integrated development environment designed specifically for machine learning (ML) tasks. It provides a rich set of tools and features to streamline the ML development process, making it easier for ML teams to collaborate and accelerate the pace of innovation. One of the recent additions to SageMaker Studio is the …

Introduction Read More »

Table of Contents 1. Introduction 2. Amazon SageMaker Studio Overview 3. Pre-configured SageMaker Distribution 4. Launching Fully Managed JupyterLab 5. Generative AI-powered Coding Companions 6. Scaling Compute Resources 7. Persisting Packages with Custom Conda Environments 8. Customizing JupyterLab with Custom-built Images 9. Advanced Features and Functionality 10. Security and Compliance 11. Conclusion 1. Introduction¶ Amazon …

Ultimate Guide to Amazon SageMaker Studio: Faster Fully-Managed Notebooks in JupyterLab Read More »

Introduction¶ Amazon SageMaker is a comprehensive machine learning (ML) platform provided by Amazon Web Services (AWS). It offers a range of tools and services that enable data scientists and developers to build, train, and deploy machine learning models at scale. In this guide, we will delve into the new setup and onboarding experience introduced in …

An In-Depth Guide to Setting Up and Onboarding Organizations and Users on Amazon SageMaker Read More »

Table of Contents¶ Introduction What is an IDE? Benefits of an IDE Extension for AWS Application Composer Getting Started with the IDE Extension Exploring the Features of the IDE Extension Tips and Best Practices for Using the IDE Extension Troubleshooting Common Issues Future Developments and Roadmap for the IDE Extension Conclusion Additional Resources 1. Introduction¶ …

Introducing an Integrated Development Environment (IDE) extension for AWS Application Composer Read More »

Guide Version: 1.0 Table of Contents¶ Introduction Choosing the Right IDE Improved IDEs in SageMaker Studio Code Editor – Powered by Code-OSS Visual Studio Code Faster and Enhanced JupyterLab RStudio Integration Accelerating ML Development Data Exploration and Model Tuning with JupyterLab Deploying and Monitoring Models with Code Editor and Pipelines Full Screen Experience Simplified Training …

New and Improved Amazon SageMaker Studio Read More »

Introduction¶ Amazon SageMaker Distribution is a powerful tool that provides Machine Learning (ML) practitioners with the flexibility to develop their ML models on the Integrated Development Environments (IDEs) of their choice. With the latest update, SageMaker Distribution is now available on Code Editor, which is based on Code-OSS and JupyterLab. This guide will explore the …

Amazon SageMaker Distribution: A Comprehensive Guide Read More »