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

2024-01-24T01:07:44+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 Actions2024-01-24T01:07:44+00:00

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

2024-01-22T00:58:08+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 Library2024-01-22T00:58:08+00:00

Securing Machine Learning Pipelines: Best Practices in Amazon SageMaker

2024-01-17T00:45:41+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 SageMaker2024-01-17T00:45:41+00:00

Building a Deployment Pipeline for a React Application with AWS CodePipeline

2024-01-07T02:48:10+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 CodePipeline2024-01-07T02:48:10+00:00

Securing LLMs with Guardrails for Amazon Bedrock

2024-01-03T00:32:13+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 Bedrock2024-01-03T00:32:13+00:00

Mastering AWS CDK Part 2: Leveraging Custom Constructs

2023-12-21T09:07:25+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 Constructs2023-12-21T09:07:25+00:00

Amazon Kendra

2024-03-23T06:15:27+00:00

Bookmarks Features Benefits Use Cases Document Types and Formats Supported Data Sources Querying Data Use of Tags Pricing Amazon Kendra Cheat Sheet Amazon Kendra is a highly scalable, intelligent enterprise search service. It utilizes machine learning to search unstructured data and improve accuracy in search results. It’s tightly integrated with other AWS services, such as Amazon S3 and Amazon Lex. It offers enterprise-grade security. Features Natural Language Processing (NLP): Amazon Kendra uses NLP to get highly accurate answers without the need for machine learning (ML) expertise. Fine-tuning Search Results: [...]

Amazon Kendra2024-03-23T06:15:27+00:00

AWS Device Farm

2024-04-12T13:48:53+00:00

Bookmarks Key Features Terminology Availability Device Selection Test Types Test Reports Pricing Security Reference AWS Device Farm Cheat Sheet AWS Device Farm allows you to examine and interact with your Android, iOS, and web applications on actual, physical devices maintained by Amazon Web Services (AWS). Key Features Automated App Testing - Device Farm provides the functionality to either upload your personalized tests or utilize the pre-existing, script-free compatibility tests. The testing process is executed concurrently, which enables tests on various devices to commence within minutes. Upon the completion [...]

AWS Device Farm2024-04-12T13:48:53+00:00

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