learn amazon web services Archives

VPC Interface Endpoint vs. Gateway Endpoint in AWS
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VPC Interface Endpoint vs. Gateway Endpoint in AWS

Irene Bonso2024-02-16T03:44:21+00:00

What is a VPC Endpoint? With a VPC endpoint, you can establish a private connection to specific AWS services and VPC endpoint services through AWS PrivateLink. It eliminates the need for public IP addresses for communication between these services and your Amazon VPC instances. Furthermore, a secure connection is maintained since no information leaves the Amazon network while traveling between your Amazon VPC and the service. VPC endpoints are virtual devices that enable communication between instances in an Amazon VPC and various services. These endpoints enhance network traffic without compromising availability or restricting bandwidth. They are designed to scale horizontally, [...]

VPC Interface Endpoint vs. Gateway Endpoint in AWSIrene Bonso2024-02-16T03:44:21+00:00

Securing Application Logs with Amazon Comprehend
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Securing Application Logs with Amazon Comprehend

Lesmon Andres Lenin Saluta2024-02-07T03:19:12+00:00

Security is one of the more overlooked aspects that many fall victim to when designing the architecture of applications. Partnering this lack of security priority with the increasing value of personal user data, security breaches become one of the certain ways for companies to lose user trust, face legal charges, and, in the long run, fail. Various governments developed data compliance laws to set minimum guidelines for security in data handling by data stakeholders. These laws govern the collecting, processing, storing, and sharing of personal and sensitive information to protect individuals' privacy and data security. Although regulations may vary depending [...]

Securing Application Logs with Amazon ComprehendLesmon Andres Lenin Saluta2024-02-07T03:19:12+00:00

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

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

Deploying a Serverless Inference Endpoint with Amazon SageMaker
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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