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  • AWS Cloud Control API, a Uniform API to Access AWS & Third-Party Services | Amazon Web Services

    AWS News Blog AWS Cloud Control API, a Uniform API to Access AWS & Third-Party Services Today, I am happy to announce the availability of AWS Cloud Control API a set of common application programming interfaces (APIs) that are designed to make it easy for developers to manage their AWS and third-party services. AWS delivers the broadest and deepest portfolio of cloud services. Builders leverage th

      AWS Cloud Control API, a Uniform API to Access AWS & Third-Party Services | Amazon Web Services
    • AWS Lambda standardizes billing for INIT Phase | Amazon Web Services

      AWS Compute Blog AWS Lambda standardizes billing for INIT Phase Effective August 1, 2025, AWS will standardize billing for the initialization (INIT) phase across all AWS Lambda function configurations. This change specifically affects on-demand invocations of Lambda functions packaged as ZIP files that use managed runtimes, for which the INIT phase duration was previously unbilled. This update sta

        AWS Lambda standardizes billing for INIT Phase | Amazon Web Services
      • Amazon Bedrock AgentCoreを一通りさわり倒してみる ~ Memory編 ~ - Generative Agents Tech Blog

        ジェネラティブエージェンツの遠藤です。 Amazon Bedrock AgentCoreは、まさに「これ欲しかったやつ!!」の塊で、テンションが爆上がりしています・・・! そんな勢いで始めた『一通りさわり倒してみる』シリーズ、今回はAgentCore Memory編をお届けします。 前回はAgentCoreがいかに熱いかの感想と実際にAgentCore Runtimeを触ってみたまとめになっているので、ぜひそちらもご覧下さい。 blog.generative-agents.co.jp Memoryに関する第一印象としては「よくぞこの仕組みをマネージドにしてくれた!」という感じですね。 エージェントとのやり取りを短期記憶としてAWSに渡しておくと、それを利用して非同期ジョブで自動的に長期記憶化して保存してくれるのは面白い方向性なんじゃないかと思います。 ただ、現時点だと以下の点がまだ見えない

          Amazon Bedrock AgentCoreを一通りさわり倒してみる ~ Memory編 ~ - Generative Agents Tech Blog
        • Introducing Amazon Bedrock AgentCore Gateway: Transforming enterprise AI agent tool development | Amazon Web Services

          Artificial Intelligence Introducing Amazon Bedrock AgentCore Gateway: Transforming enterprise AI agent tool development To fulfill their tasks, AI Agents need access to various capabilities including tools, data stores, prompt templates, and other agents. As organizations scale their AI initiatives, they face an exponentially growing challenge of connecting each agent to multiple tools, creating a

            Introducing Amazon Bedrock AgentCore Gateway: Transforming enterprise AI agent tool development | Amazon Web Services
          • Better together: AWS SAM CLI and HashiCorp Terraform | Amazon Web Services

            AWS Compute Blog Better together: AWS SAM CLI and HashiCorp Terraform This post is written by Suresh Poopandi, Senior Solutions Architect and Seb Kasprzak, Senior Solutions Architect. Today, AWS is announcing the public preview of AWS Serverless Application Model CLI (AWS SAM CLI) support for local development, testing, and debugging of serverless applications defined using HashiCorp Terraform con

              Better together: AWS SAM CLI and HashiCorp Terraform | Amazon Web Services
            • Amazon Bedrock now provides access to Meta’s Llama 2 Chat 13B model | Amazon Web Services

              AWS News Blog Amazon Bedrock now provides access to Meta’s Llama 2 Chat 13B model Update: November 29, 2023 — Today, we’re adding the Llama 2 70B model in Amazon Bedrock, in addition to the already available Llama 2 13B model. As its name implies, the Llama 2 70B model has been trained on larger datasets than the Llama 2 13B model. If you’re wondering when to use which model, consider using Llama

                Amazon Bedrock now provides access to Meta’s Llama 2 Chat 13B model | Amazon Web Services
              • 【祝GA】 Lambda ExtensionsでLambdaのログをGCPのCloud Loggingに送信してみた | DevelopersIO

                CX事業本部@大阪の岩田です。先日Lambda ExtensionsがGAされました(東京リージョンはまだですが)。Lambda ExtensionsはLambdaのモニタリング、可観測性、セキュリティ、ガバナンスのための運用ツールをLambdaに統合する機能で、Lambda実行環境のライフサイクルと連動して外部のサードパーティ製品に直接ログやメトリクスを送信するといったことが可能です。 パブリックプレビュー自体は半年以上前から利用できる状態だったのですが、これまでちゃんと触れていなかったので自分の理解を深めるために簡単なExtensionを実装してみようと思い立ちました。何を作るか考えたのですが、今回はLambdaのログをGCPのCloud Loggingに送信するExtensionを作ることにしました。なおLambdaのランタイムにはPython3.8を利用しています。 Lambda

                  【祝GA】 Lambda ExtensionsでLambdaのログをGCPのCloud Loggingに送信してみた | DevelopersIO
                • Agents for Amazon Bedrock now support memory retention and code interpretation (preview) | Amazon Web Services

                  AWS News Blog Agents for Amazon Bedrock now support memory retention and code interpretation (preview) With Agents for Amazon Bedrock, generative artificial intelligence (AI) applications can run multistep tasks across different systems and data sources. A couple of months back, we simplified the creation and configuration of agents. Today, we are introducing in preview two new fully managed capab

                    Agents for Amazon Bedrock now support memory retention and code interpretation (preview) | Amazon Web Services
                  • Best Practices for Writing Step Functions Terraform Projects | Amazon Web Services

                    AWS DevOps & Developer Productivity Blog Best Practices for Writing Step Functions Terraform Projects Terraform by HashiCorp is one of the most popular infrastructure-as-code (IaC) platforms. AWS Step Functions is a visual workflow service that helps developers use AWS services to build distributed applications, automate processes, orchestrate microservices, and create data and machine learning (M

                      Best Practices for Writing Step Functions Terraform Projects | Amazon Web Services
                    • Behind the Scenes Lambda

                      statesunsetinwritingdate6/12/2020🌇 Sunset The sun is setting on these articles, they are still useful, but they are not the future. They are the past, and likely outdated. Read with caution. Writing code and deploying it to AWS Lambda is as easy as baking a cake (depending on the type of cake). Lambda performs the heavy lifting for you, from provisioning to scaling. But where is the magic happeni

                      • Using AWS CodePipeline for deploying container images to AWS Lambda Functions | Amazon Web Services

                        AWS DevOps & Developer Productivity Blog Using AWS CodePipeline for deploying container images to AWS Lambda Functions AWS Lambda launched support for packaging and deploying functions as container images at re:Invent 2020. In the post working with Lambda layers and extensions in container images, we demonstrated packaging Lambda Functions with layers while using container images. This post will t

                          Using AWS CodePipeline for deploying container images to AWS Lambda Functions | Amazon Web Services
                        • Build secure multi-account multi-VPC connectivity for your applications with Amazon VPC Lattice | Amazon Web Services

                          Networking & Content Delivery Build secure multi-account multi-VPC connectivity for your applications with Amazon VPC Lattice Introduction In this blog post, we will discuss how you can use Amazon VPC Lattice to connect your services securely, and monitor communication flows, in a simple and consistent way across instances, containers, and serverless, in a multi-account and multi-Virtual Private C

                            Build secure multi-account multi-VPC connectivity for your applications with Amazon VPC Lattice | Amazon Web Services
                          • AWS Step Functionsレシピ集 - TECHSCORE BLOG

                            はじめに AWSで定期的なデータ連携バッチを書く方法の一つにAWS Step Functions(以下Step Functions)があります。Step FunctionsはAWS Lambda(以下Lambda)を始めとするAWSの様々なサービスを組み合わせたワークフローを記述できるサービスです。LambdaおよびStep Functionsはバッチ用のサーバを構築・管理する必要がなく安価にサービスを運用できるため、私のチームでも活用しています。 LambdaおよびStep Functionsにはそれぞれ制約があり工夫が必要となる場合もあります。例えばパイプラインを定義するためにASL(Amazon State Language)というDSLを使いますが、ASLで何ができるかを理解するには時間が掛かります。細かい書き方などはすぐ忘れてしまって、以前書いたのを探すのに苦労したりもします。こ

                              AWS Step Functionsレシピ集 - TECHSCORE BLOG
                            • Data processing options for AI/ML | Amazon Web Services

                              Artificial Intelligence Data processing options for AI/ML This blog post was reviewed and updated June, 2022 to include new features that have been added to the Data processing such as Amazon SageMaker Studio and EMR integration. Training an accurate machine learning (ML) model requires many different steps, but none are potentially more important than data processing. Examples of processing steps

                                Data processing options for AI/ML | Amazon Web Services
                              • How LotteON built a personalized recommendation system using Amazon SageMaker and MLOps | Amazon Web Services

                                Artificial Intelligence How LotteON built a personalized recommendation system using Amazon SageMaker and MLOps This post is co-written with HyeKyung Yang, Jieun Lim, and SeungBum Shim from LotteON. LotteON aims to be a platform that not only sells products, but also provides a personalized recommendation experience tailored to your preferred lifestyle. LotteON operates various specialty stores, i

                                  How LotteON built a personalized recommendation system using Amazon SageMaker and MLOps | Amazon Web Services
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