AWS Machine Learning Blog

Access an Amazon SageMaker Studio notebook from a corporate network

Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning. It provides a single, web-based visual interface where you can perform all ML development steps required to build, train, and deploy models. You can quickly upload data, create new notebooks, train and tune models, move back and forth between steps to […]

Build conversation flows with multi-valued slots in Amazon Lex

Multiple pieces of information are often required to complete a task or to process a query. For example, when talking to an insurance agent, a caller might ask, “Can you provide me quotes for home, auto, and boat?” The agent recognizes this as a list of policy types before continuing with the conversation. Automation of […]

Amazon SageMaker notebook instances now support Amazon Linux 2

February 8th, 2022: Updated with AWS CloudFormation support to create an Amazon Linux 2 based SageMaker notebook instance. Today, we’re excited to announce that Amazon SageMaker notebook instances support Amazon Linux 2. You can now choose Amazon Linux 2 for your new SageMaker notebook instance to take advantage of the latest update and support provided […]

Secure multi-account model deployment with Amazon SageMaker: Part 2

In Part 1 of this series of posts, we offered step-by-step guidance for using Amazon SageMaker, SageMaker projects and Amazon SageMaker Pipelines, and AWS services such as Amazon Virtual Private Cloud (Amazon VPC), AWS CloudFormation, AWS Key Management Service (AWS KMS), and AWS Identity and Access Management (IAM) to implement secure architectures for multi-account enterprise […]

Secure multi-account model deployment with Amazon SageMaker: Part 1

Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models. Although Studio provides all the tools you need to take your models from experimentation to production, you need a robust and secure model deployment process. This process must fulfill […]

Optimize personalized recommendations for a business metric of your choice with Amazon Personalize

Amazon Personalize now enables you to optimize personalized recommendations for a business metric of your choice, in addition to improving relevance of recommendations for your users. You can define a business metric such as revenue, profit margin, video watch time, or any other numerical attribute of your item catalog to optimize your recommendations. Amazon Personalize […]

Create Amazon SageMaker projects using third-party source control and Jenkins

Launched at AWS re:Invent 2020, Amazon SageMaker Pipelines is the first purpose-built, easy-to-use continuous integration and continuous delivery (CI/CD) service for machine learning (ML). With Pipelines, you can create, automate, and manage end-to-end ML workflows at scale. You can integrate Pipelines with existing CI/CD tooling. This includes integration with existing source control systems such as […]

Use Block Kit when integrating Amazon Lex bots with Slack

If you’re integrating your Amazon Lex chatbots with Slack, chances are you’ll come across Block Kit. Block Kit is a UI framework for Slack apps. Like response cards, Block Kit can help simplify interactions with your users. It offers flexibility to format your bot messages with blocks, buttons, check boxes, date pickers, time pickers, select […]

Patterns for multi-account, hub-and-spoke Amazon SageMaker model registry

Data science workflows have to pass multiple stages as they progress from the experimentation to production pipeline. A common approach involves separate accounts dedicated to different phases of the AI/ML workflow (experimentation, development, and production). In addition, issues related to data access control may also mandate that workflows for different AI/ML applications be hosted on […]