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Governing the ML lifecycle at scale, Part 3: Setting up data governance at scale

AWS Machine Learning

Challenges in data management Traditionally, managing and governing data across multiple systems involved tedious manual processes, custom scripts, and disconnected tools. The diagram shows several accounts and personas as part of the overall infrastructure. The following diagram gives a high-level illustration of the use case.

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

AWS Machine Learning

SageMaker Feature Store now makes it effortless to share, discover, and access feature groups across AWS accounts. With this launch, account owners can grant access to select feature groups by other accounts using AWS Resource Access Manager (AWS RAM).

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An Interview with Kate Nasser: Better #PeopleSkills

Customers That Stick

Kate Nasser, The People Skills Coach™ , is a smart, energizing, experienced speaker, coach, and workshop leader. The most challenging people skill to learn and use seems to be replacing defensive reactions with simple accountability. To engage Kate Nasser’s keynotes, workshops, and coaching, visit her blog.

Coaching 152
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Secure a generative AI assistant with OWASP Top 10 mitigation

AWS Machine Learning

In the preceding architecture diagram, AWS WAF is integrated with Amazon API Gateway to filter incoming traffic, blocking unintended requests and protecting applications from threats like SQL injection, cross-site scripting (XSS), and DoS attacks. Learn more about building generative AI applications with AWS Workshops for Bedrock.

APIs 98
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Create an end-to-end serverless digital assistant for semantic search with Amazon Bedrock

AWS Machine Learning

Prerequisites To follow along and set up this solution, you must have the following: An AWS account A device with access to your AWS account with the following: Python 3.12 Create an S3 bucket in your account. You can also complete these steps by running the script cognito-create-testuser.sh installed Node.js

APIs 126
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Accelerate pre-training of Mistral’s Mathstral model with highly resilient clusters on Amazon SageMaker HyperPod

AWS Machine Learning

Reusable scaling scripts for rapid experimentation – HyperPod offers a set of scalable and reusable scripts that simplify the process of launching multiple training runs. In your account, you will have a VPC provisioned with a public and private subnet, and an S3 bucket synced to your FSxL file system via a data repository link.

Scripts 104
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KT’s journey to reduce training time for a vision transformers model using Amazon SageMaker

AWS Machine Learning

During a 1-day workshop, we were able to set up a distributed training configuration based on SageMaker within KT’s AWS account, accelerate KT’s training scripts using the SageMaker Distributed Data Parallel (DDP) library, and even test a training job using two ml.p4d.24xlarge 24xlarge instances. region_name}.amazonaws.com/pytorch-training:2.0.0-gpu-py310-cu118-ubuntu20.04-sagemaker'

Scripts 109