aws blog Archives

Distributed Data Parallel Training with TensorFlow and Amazon SageMaker Distributed Training Library
Gallery

Distributed Data Parallel Training with TensorFlow and Amazon SageMaker Distributed Training Library

John Patrick Laurel2024-01-22T00:58:08+00:00

Introduction In the realm of machine learning, the ability to train models effectively and efficiently stands as a cornerstone of success. As datasets grow exponentially and models become more complex, traditional single-node training methods increasingly fall short. This is where distributed training enters the picture, offering a scalable solution to this growing challenge. Distributed Training Overview Distributed training is a technique used to train machine learning models on large datasets more efficiently. By splitting the workload across multiple compute nodes, it significantly reduces training time. There are two main strategies in distributed training: data parallelism, where the dataset is partitioned [...]

Distributed Data Parallel Training with TensorFlow and Amazon SageMaker Distributed Training LibraryJohn Patrick Laurel2024-01-22T00:58:08+00:00

Securing Machine Learning Pipelines: Best Practices in Amazon SageMaker
Gallery

Securing Machine Learning Pipelines: Best Practices in Amazon SageMaker

John Patrick Laurel2024-01-17T00:45:41+00:00

Introduction In today's digital era, the importance of security in machine learning (ML) pipelines cannot be overstated. As ML systems increasingly become integral to business operations and decision-making, ensuring the integrity and security of these systems is paramount. A breach or a flaw in an ML pipeline can lead to compromised data, erroneous decision-making, and potentially catastrophic consequences for businesses and individuals alike. This section will delve into why securing ML pipelines is crucial, highlighting the potential risks and impacts of security lapses. Short Introduction to Amazon SageMaker Amazon SageMaker is a fully managed service that provides every developer and [...]

Securing Machine Learning Pipelines: Best Practices in Amazon SageMakerJohn Patrick Laurel2024-01-17T00:45:41+00:00

Building a Deployment Pipeline for a React Application with AWS CodePipeline
Gallery

Building a Deployment Pipeline for a React Application with AWS CodePipeline

Kaye Alvarado2024-01-07T02:48:10+00:00

This is the second part of a series of blogs about the platform management of a React Application infrastructure by adding a continuous deployment component to the earlier infrastructure. In an earlier article, I wrote about how a private react application infrastructure can be deployed with Terraform code. Now, we will explore this further by building a deployment pipeline using AWS CodePipeline. Let's assume that the source code of the React web application is hosted on GitHub. Using the GitHub connections feature of AWS CodePipeline, we can authorize the third-party provider to work with AWS resources to establish integration between [...]

Building a Deployment Pipeline for a React Application with AWS CodePipelineKaye Alvarado2024-01-07T02:48:10+00:00

Train and Deploy a Scikit-Learn Model in Amazon SageMaker
Gallery

Train and Deploy a Scikit-Learn Model in Amazon SageMaker

John Patrick Laurel2024-01-05T10:10:06+00:00

Introduction In the ever-evolving world of machine learning (ML), the ability to efficiently train and deploy models is crucial for turning innovative ideas into real-world applications. This is where Amazon SageMaker, a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly, stands out. Amazon SageMaker streamlines the machine learning workflow, offering a broad set of capabilities that are designed to enable users to focus more on the problem at hand and less on the underlying infrastructure. While SageMaker comes equipped with built-in algorithms and support for various [...]

Train and Deploy a Scikit-Learn Model in Amazon SageMakerJohn Patrick Laurel2024-01-05T10:10:06+00:00

Securing LLMs with Guardrails for Amazon Bedrock
Gallery

Securing LLMs with Guardrails for Amazon Bedrock

Kaye Alvarado2024-01-03T00:32:13+00:00

One of the pillars of the AWS Well-Architected Framework is security. It is a foundational concept when running your workloads in the cloud to think about privacy, access limits, compliance with regulatory requirements, and data protection; and this includes Amazon Bedrock. Along with several AI announcements during the keynote of AWS CEO, Adam Selipsky during AWS re:Invent 2023 was Guardrails for Amazon Bedrock. As AI technology evolves and becomes more mature, it makes sense to also reinvent the way usage is handled by security safeguards. Guardrails for Amazon Bedrock allow security policies to be applied across foundational models, to fulfill [...]

Securing LLMs with Guardrails for Amazon BedrockKaye Alvarado2024-01-03T00:32:13+00:00

Building a Private React Application Infrastructure with Terraform
Gallery

Building a Private React Application Infrastructure with Terraform

Kaye Alvarado2024-01-02T03:12:00+00:00

This is the first part of a series of blogs about the platform management of a React Application infrastructure using Terraform. In the early phases of a software development project, it is mandatory to have the application reviewed by security way before the Beta release to ensure that the app adheres to the standards of a secure web application and is able to protect the sensitive data it handles. Most of the time, DevOps Engineers are tasked to deploy the web application in a private domain to allow the initial load testing, penetration testing, and other security-related testing while keeping [...]

Building a Private React Application Infrastructure with TerraformKaye Alvarado2024-01-02T03:12:00+00:00

Deploying a Serverless Inference Endpoint with Amazon SageMaker
Gallery

Deploying a Serverless Inference Endpoint with Amazon SageMaker

John Patrick Laurel2023-12-27T11:56:32+00:00

Introduction Welcome to our deep dive into the world of serverless machine learning (ML) inference using Amazon SageMaker. In this blog post, we will explore the innovative and efficient approach of deploying ML models without the need for managing servers, a method known as serverless inference. What is Serverless Inference? Serverless inference is a cloud computing execution model where the cloud provider dynamically manages the allocation of machine resources. The key advantage here is that it abstracts the underlying infrastructure, allowing developers and data scientists to focus solely on their application logic. This approach offers several benefits: Cost-Effectiveness: You pay [...]

Deploying a Serverless Inference Endpoint with Amazon SageMakerJohn Patrick Laurel2023-12-27T11:56:32+00:00

Setting Up AWS EventBridge for Cross-Region IAM Event Notifications
Gallery

Setting Up AWS EventBridge for Cross-Region IAM Event Notifications

Bill Junidez Liad2023-12-22T22:11:15+00:00

AWS EventBridge is a serverless event bus service that simplifies event-driven architectures. In this guide, we'll explore how to leverage EventBridge with a cross-region event bus to overcome the regional limitations associated with IAM (Identity and Access Management) events. This setup will enable you to capture and respond to IAM events seamlessly across AWS regions. Use Case: Cross-Region IAM Event Notifications with AWS EventBridge AWS CloudWatch Events are powerful, but they are region-specific, posing challenges when attempting to capture IAM events. It's essential to note that IAM is a global service, and the IAM events are only available in the [...]

Setting Up AWS EventBridge for Cross-Region IAM Event NotificationsBill Junidez Liad2023-12-22T22:11:15+00:00

Mastering AWS CDK Part 2: Leveraging Custom Constructs
Gallery

Mastering AWS CDK Part 2: Leveraging Custom Constructs

Iggy Yuson2023-12-21T09:07:25+00:00

In the previous article, we delved into the fundamentals of AWS CDK, focusing on leveraging AWS's pre-built constructs. We examined a straightforward Serverless REST API architecture, which integrates three primary serverless services from AWS. These include Amazon API Gateway as the REST API's access point, AWS Lambda for computing, and Amazon DynamoDB for data storage. The Fat Lambda In contrast to the basic architecture previously discussed, it's important to note that such a setup is not typically regarded as best practice for production-grade applications. The serverless community often debates the concept of "Fat Lambda," a term used to describe scenarios [...]

Mastering AWS CDK Part 2: Leveraging Custom ConstructsIggy Yuson2023-12-21T09:07:25+00:00

AWS Well-Architected Tool
Gallery

AWS Well-Architected Tool

Nestor Mayagma Jr.2024-03-23T07:02:34+00:00

Bookmarks Key Components Using the Tool Best Practices Benefits Reference AWS Well-Architected Tool Cheat Sheet The AWS Well-Architected Tool is a service that helps you review your workloads and compares them to the latest AWS architectural best practices. The tool provides recommendations for making your workloads more reliable, secure, efficient, and cost-effective. Key Components Workload is a term used to describe a collection of components that collectively contribute to business value. This could range from marketing websites, e-commerce platforms, and backend for mobile applications to analytic platforms. The complexity of a workload [...]

AWS Well-Architected ToolNestor Mayagma Jr.2024-03-23T07:02:34+00:00