AWS Archives

Building a Simple Video Hosting Service using Amazon CloudFront, Amazon S3, and AWS Amplify
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Building a Simple Video Hosting Service using Amazon CloudFront, Amazon S3, and AWS Amplify

Lesmon Andres Lenin Saluta2024-02-01T11:00:28+00:00

Application Use Case The primary purpose of this application is to share a video as a link and have it accessed by viewers with low latency. This feature is great for those who make quick videos, for example, a short demonstration on how to use an application or a group of academics sharing their explanations to mentor their peers. Overview of the Tech Stack Different video hosting websites cater to various features that are subjective to users. Some users prefer highly secure video hosting websites for private content, integrating one (or more) authentication layers to upload [...]

Building a Simple Video Hosting Service using Amazon CloudFront, Amazon S3, and AWS AmplifyLesmon Andres Lenin Saluta2024-02-01T11:00:28+00:00

Release with a Pipeline: Continuous Delivery to AWS with GitHub Actions
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Release with a Pipeline: Continuous Delivery to AWS with GitHub Actions

Kaye Alvarado2024-01-24T01:07:44+00:00

This is the final part of a three-part article about a Web Application Project from building a private infrastructure to building a deployment pipeline using AWS’ cloud-native continuous delivery service AWS CodePipeline, and now finalizing the infrastructure to be accessible in a public domain and building a pipeline for continuous deployment using a third-party CD tool – GitHub Actions. From the private infrastructure previously built, we will update the S3 policy to add a statement for an allowed action for the CloudFront resource. As best practice, this statement will be added to the Terraform script of the infrastructure to make it [...]

Release with a Pipeline: Continuous Delivery to AWS with GitHub ActionsKaye Alvarado2024-01-24T01:07:44+00:00

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

HTTP Flood Attack Notification using AWS Lambda, Amazon EventBridge and CloudWatch Logs Insights
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HTTP Flood Attack Notification using AWS Lambda, Amazon EventBridge and CloudWatch Logs Insights

Neil Rico2024-01-25T05:04:59+00:00

We can almost do everything now on the website. Selling clothes, ordering food, video posting, doing business meetings, online classes, and others, you name it. Running a website is very awesome and at the same time hard, especially when bad actors want to mess with it. One sneaky way is an "HTTP Flood Attack," where your website gets bombarded with too many requests. This can slow down or even break your site. Detecting and responding to such attacks promptly is crucial for maintaining the availability and performance of your applications. In this blog post, we'll explore how to implement a simple [...]

HTTP Flood Attack Notification using AWS Lambda, Amazon EventBridge and CloudWatch Logs InsightsNeil Rico2024-01-25T05:04:59+00:00

Training an Image Classification Model with TensorFlow in Amazon SageMaker
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Training an Image Classification Model with TensorFlow in Amazon SageMaker

John Patrick Laurel2024-01-15T12:39:38+00:00

Introduction In today's digital age, where data is as vital as currency, the power of Machine Learning (ML) in transforming industries is undeniable. From self-driving cars to personalized medicine, ML models are at the heart of many technological breakthroughs. Among the various tools and frameworks available for ML, TensorFlow has emerged as a leader, renowned for its versatility and scalability. This article aims to demystify the process of training an Image Classification model using TensorFlow, specifically within the Amazon SageMaker environment. For beginners and intermediate enthusiasts alike, navigating through TensorFlow's complexities and harnessing the power of SageMaker can seem daunting. [...]

Training an Image Classification Model with TensorFlow in Amazon SageMakerJohn Patrick Laurel2024-01-15T12:39:38+00:00

NEW Product Release: All-in-Access: Courses + PlayCloud Sandbox (AWS) & Play Sandbox (AWS)
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NEW Product Release: All-in-Access: Courses + PlayCloud Sandbox (AWS) & Play Sandbox (AWS)

Nikee Tomas2024-03-26T01:17:06+00:00

Brace yourselves for a journey into the cutting-edge realms of tech and education! We're thrilled to announce not one but two incredible products designed to supercharge your learning experience and bring your skills to new heights! 🚀 Get ready to dive into innovation with our super-focused PlayCloud Sandbox (AWS) and All-in-Access: Courses + PlayCloud Sandbox (AWS). Let's explore why these releases are set to revolutionize your learning journey. PlayCloud Sandbox (AWS) The PlayCloud Sandbox is a secure, isolated AWS environment where you can confidently experiment and run applications at your own pace. With this Playcloud Sandbox product subscription, you get two (2) [...]

NEW Product Release: All-in-Access: Courses + PlayCloud Sandbox (AWS) & Play Sandbox (AWS)Nikee Tomas2024-03-26T01:17:06+00:00

Building a Deployment Pipeline for a React Application with AWS CodePipeline
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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
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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
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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