技術や情報、そして人々が集まる"港"
Previously, we mastered the art of crafting an app on AWS Amplify—from creating intricate UI components on Figma to seamlessly synchronizing them with AWS Amplify. With each step, we imported these components into our local React code, ensuring smooth rendering. In this blog, we embark on the next leg of building our app, exploring new features, and advancing our project. So, without further ado, let's begin.
記事を見る
Previously, we mastered the art of crafting an app on AWS Amplify—from creating intricate UI components on Figma to seamlessly synchronizing them with AWS Amplify. With each step, we imported these components into our local React code, ensuring smooth rendering. In this blog, we embark on the next leg of building our app, exploring new features, and advancing our project. So, without further ado, let's begin.
記事を見る
Welcome back to Part 2 of our journey into the world of Machine Learning using Amazon SageMaker! In Part 1 of this Series, we dipped our toes into understanding the basics of Machine Learning, the types, and our goal with the Cats vs Dogs dataset. Now, it's time to delve deeper into the features of SageMaker. Excited? Let's jump in!
記事を見る
AWS Amplify is a powerful suite of tools designed to simplify the development of full-stack web and mobile applications on AWS. As cloud services have evolved, developers have gained access to increasingly sophisticated solutions that reduce infrastructure overhead and accelerate application delivery. Amplify continues this trend by providing a seamless way to build, connect, and host modern applications while leveraging the broader AWS ecosystem as needed. In this article, we will introduce the key components of AWS Amplify and explore how they work together to support efficient full-stack development, especially for React-based applications.
記事を見る
Terraform is a powerful Infrastructure-as-Code tool, but as projects grow, managing them becomes increasingly complex. What begins as a small set of configuration files can quickly expand into a large codebase that must support multiple environments, teams, and deployment processes. Without clear structure and consistent practices, Terraform projects can become difficult to maintain and risky to change. In this article, we will explore best practices for building large-scale, production-ready Terraform projects. The focus will be on keeping code organized, reusable, and safe to manage in collaborative environments. We will also cover key topics such as state management, remote backends, locking, versioning, Git-based workflows, and CI/CD pipelines to help teams improve reliability and reduce errors.
記事を見る
Starting a Terraform project can be challenging, especially when you are trying to decide how to organize the code from the beginning. A well-planned structure is important because it affects how easily the project can grow, how reusable the code will be, and how simple it is for a team to maintain over time. Without clear conventions, even a small Terraform setup can become difficult to manage as more environments, modules, and resources are added. In this article, we will look at the best way to get started with Terraform projects. The focus will be on practical guidance for structuring your code, defining key components, and following basic rules that help keep the project clean, scalable, and easy to work with.
記事を見る
Along with multi language support, reliable documentation and steady upgrades, AWS CDK has its own testing constructs as well. Tests in CDK can be categorised into two types: Snapshot and Fine-grained assertions. Snapshot tests tend to be more simplified in terms of implementation as well as maintenance. In this article we will take a deeper look at implementing a Snapshot test in AWS CDK.
記事を見る
Machine Learning is one of the most exciting areas in modern technology, but it can also feel overwhelming when you are just getting started. Terms like AI, ML, and deep learning are everywhere, yet many people are still unsure about what happens behind the scenes. This series is designed to make that first step easier by introducing machine learning in a simple and practical way. In this article, we will explore the basic ideas behind machine learning and the main learning types, including supervised, unsupervised, reinforcement, and semi-supervised learning. We will also introduce the dataset and model used in this series, and explain why Amazon SageMaker is a great starting point for beginners who want to build and train ML models in the cloud.
記事を見る
The introduction of Infrastructure as a Code (IaC) has had a profound impact on the world of Cloud and DevOps. With IaC, the tedious process of manual infrastructure deployment and maintenance is replaced by an automated operation to deploy, destroy, monitor, and replicate your infrastructure as and when required. Infrastructure as a Code platforms have been on the rise since automated provisioning of infrastructure became in-demand. Today, we have cloud specific IaC tools like CDK and tools with multi-cloud support like Terraform and Pulumi. As reliability on IaC increases, it is important to ensure that quality coding standards persist. One of the key factors in improving code quality and development efficiency is Testing. Especially for IaC, Testing is an important practice to confirm and check all resources and their configuration before proceeding with deployment. This article provides a brief overview of different types of tests carried out in AWS CDK.
記事を見る
みなさんはAPM(Application performance monitoring)を使っていますでしょうか。昨今のアプリケーションはContainerやCI/CDを活かした高頻度なデプロイが可能になっています。しかしながら、アプリケーションのデプロイによるパフォーマンスの変化を評価せず、インフラ監視のみに留まっているサービスも多いのではないでしょうか。本記事ではAPMツール選定におけるポイント5つをご紹介します。
記事を見るContact
Copyright © JTP Co., Ltd. All Rights Reserved.
