Building a Simple Video Hosting Service using Amazon CloudFront, Amazon S3, and AWS Amplify
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 [...]
Machine Learning Mathematics: Regression Basics
In recent years, of the ever-evolving landscape of technology, a few of the most prominent buzzwords that continue to capture the imagination of innovators and entrepreneurs alike are Machine Learning (ML) and Artificial Intelligence (AI). With the boom of OpenAI’s ChatGPT last November 2023, as well as the many that followed suit, including major tech companies like Meta with their LLaMa model and Google’s PaLM and newly announced Gemini, ML and AI will most [...]
Release with a Pipeline: Continuous Delivery to AWS with GitHub Actions
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 [...]
Distributed Data Parallel Training with TensorFlow and Amazon SageMaker Distributed Training Library
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 [...]
Securing Machine Learning Pipelines: Best Practices in Amazon SageMaker
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, [...]
HTTP Flood Attack Notification using AWS Lambda, Amazon EventBridge and CloudWatch Logs Insights
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 [...]
Training an Image Classification Model with TensorFlow in Amazon SageMaker
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 [...]
NEW Product Release: All-in-Access: Courses + PlayCloud Sandbox (AWS) & Play Sandbox (AWS)
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 [...]
Building a Deployment Pipeline for a React Application with AWS CodePipeline
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 [...]
Train and Deploy a Scikit-Learn Model in Amazon SageMaker
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 [...]
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