Remove Accountability Remove APIs Remove Examples Remove Scripts
article thumbnail

Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

AWS Machine Learning

For example, in an application that recommends a music playlist, features could include song ratings, listening duration, and listener demographics. SageMaker Feature Store now makes it effortless to share, discover, and access feature groups across AWS accounts. Features are inputs to ML models used during training and inference.

article thumbnail

Build a cross-account MLOps workflow using the Amazon SageMaker model registry

AWS Machine Learning

When designing production CI/CD pipelines, AWS recommends leveraging multiple accounts to isolate resources, contain security threats and simplify billing-and data science pipelines are no different. Some things to note in the preceding architecture: Accounts follow a principle of least privilege to follow security best practices.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

How to Create an Interactive Scavenger Hunt with Nexmo’s SMS and Voice API

Nexmo

And thus I thought it’d be fun to design and build something with Nexmo’s Voice and SMS APIs to do just that. To work through this tutorial, you will need a Nexmo account. You can sign up now for free if you don’t already have an account. Here’s an example of one of my clues: [link]. Prerequisites.

APIs 63
article thumbnail

Optimize AWS Inferentia utilization with FastAPI and PyTorch models on Amazon EC2 Inf1 & Inf2 instances

AWS Machine Learning

If the model changes on the server side, the client has to know and change its API call to the new endpoint accordingly. For example, NEURON_RT_NUM_CORES=2 myapp.py For this example, we’re going with us-east-2 as the region and json as the default output. As an example, we will choose Inf2 as the guide.

Scripts 73
article thumbnail

Get smarter search results with the Amazon Kendra Intelligent Ranking and OpenSearch plugin

AWS Machine Learning

In this post, we show you how to get started with Amazon Kendra Intelligent Ranking for self-managed OpenSearch, and we provide a few examples that demonstrate the power and value of this feature. For this tutorial, you’ll need a bash terminal on Linux , Mac , or Windows Subsystem for Linux , and an AWS account. Prerequisites.

Scripts 79
article thumbnail

Configure and use defaults for Amazon SageMaker resources with the SageMaker Python SDK

AWS Machine Learning

Once configured, the Python SDK automatically inherits these values and propagates them to the underlying SageMaker API calls such as CreateProcessingJob() , CreateTrainingJob() , and CreateEndpointConfig() , with no additional actions needed. The steps are as follows: Launch the CloudFormation stack in your account.

APIs 78
article thumbnail

Use the Amazon SageMaker and Salesforce Data Cloud integration to power your Salesforce apps with AI/ML

AWS Machine Learning

The following steps give an overview of how to use the new capabilities launched in SageMaker for Salesforce to enable the overall integration: Set up the Amazon SageMaker Studio domain and OAuth between Salesforce and the AWS account s. frameworks to restrict client access to your APIs. Select Enable OAuth Settings.

APIs 78