Category: Management Tools

In today’s digital landscape, the protection of personally identifiable information (PII) is not just a regulatory requirement, but a cornerstone of consumer trust and business integrity. Organizations use advanced natural language detection services like HAQM Lex for building conversational interfaces and HAQM CloudWatch for monitoring and analyzing operational data. One risk many organizations face is […]

In this post, we share the story of how The Weather Company (TWCo) enhanced its MLOps platform using services such as HAQM SageMaker, AWS CloudFormation, and HAQM CloudWatch. TWCo data scientists and ML engineers took advantage of automation, detailed experiment tracking, integrated training, and deployment pipelines to help scale MLOps effectively. TWCo reduced infrastructure management time by 90% while also reducing model deployment time by 20%.

HAQM SageMaker is a machine learning (ML) platform designed to simplify the process of building, training, deploying, and managing ML models at scale. With a comprehensive suite of tools and services, SageMaker offers developers and data scientists the resources they need to accelerate the development and deployment of ML solutions. In today’s fast-paced technological landscape, […]

In this blog post, we will share some of capabilities to help you get quick and easy visibility into HAQM Bedrock workloads in context of your broader application. We will use the contextual conversational assistant example in the HAQM Bedrock GitHub repository to provide examples of how you can customize these views to further enhance visibility, tailored to your use case. Specifically, we will describe how you can use the new automatic dashboard in HAQM CloudWatch to get a single pane of glass visibility into the usage and performance of HAQM Bedrock models and gain end-to-end visibility by customizing dashboards with widgets that provide visibility and insights into components and operations such as Retrieval Augmented Generation in your application.

This post is co-written with HyeKyung Yang, Jieun Lim, and SeungBum Shim from LotteON. LotteON is transforming itself into an online shopping platform that provides customers with an unprecedented shopping experience based on its in-store and online shopping expertise. Rather than simply selling the product, they create and let customers experience the product through their […]

As you navigate the complexities of cloud migration, the need for a structured, secure, and compliant environment is paramount. AWS Landing Zone addresses this need by offering a standardized approach to deploying AWS resources. This makes sure your cloud foundation is built according to AWS best practices from the start. With AWS Landing Zone, you eliminate the guesswork in security configurations, resource provisioning, and account management. It’s particularly beneficial for organizations looking to scale without compromising on governance or control, providing a clear path to a robust and efficient cloud setup. In this post, we show you how to generate customized, compliant IaC scripts for AWS Landing Zone using HAQM Bedrock.

This post walks you through the Open Source Observability pattern for AWS Inferentia, which shows you how to monitor the performance of ML chips, used in an HAQM Elastic Kubernetes Service (HAQM EKS) cluster, with data plane nodes based on HAQM Elastic Compute Cloud (HAQM EC2) instances of type Inf1 and Inf2.

HAQM Lex is a fully managed artificial intelligence (AI) service with advanced natural language models to design, build, test, and deploy conversational interfaces in applications. It employs advanced deep learning technologies to understand user input, enabling developers to create chatbots, virtual assistants, and other applications that can interact with users in natural language. Managing your […]

Large Language Models (LLMs) have revolutionized the field of natural language processing (NLP), improving tasks such as language translation, text summarization, and sentiment analysis. However, as these models continue to grow in size and complexity, monitoring their performance and behavior has become increasingly challenging. Monitoring the performance and behavior of LLMs is a critical task […]

In this post, we show you how to build an internal SaaS layer to access foundation models with HAQM Bedrock in a multi-tenant (team) architecture. We specifically focus on usage and cost tracking per tenant and also controls such as usage throttling per tenant. We describe how the solution and HAQM Bedrock consumption plans map to the general SaaS journey framework. The code for the solution and an AWS Cloud Development Kit (AWS CDK) template is available in the GitHub repository.