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Accelerating ML experimentation with enhanced security: AWS PrivateLink support for Amazon SageMaker with MLflow

AWS Machine Learning Blog

It simplifies the often complex and time-consuming tasks involved in setting up and managing an MLflow environment, allowing ML administrators to quickly establish secure and scalable MLflow environments on AWS. AWS CodeArtifact , which provides a private PyPI repository so that SageMaker can use it to download necessary packages.

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Announcing managed MCP servers with Unity Catalog and Mosaic AI Integration

databricks

Events Data + AI Summit Data + AI World Tour Data Intelligence Days Event Calendar Blog and Podcasts Databricks Blog Explore news, product announcements, and more Databricks Mosaic Research Blog Discover the latest in our Gen AI research Data Brew Podcast Let’s talk data!

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Integrate foundation models into your code with Amazon Bedrock

AWS Machine Learning Blog

Prerequisites Before you dive into the integration process, make sure you have the following prerequisites in place: AWS account – You’ll need an AWS account to access and use Amazon Bedrock. You can interact with Amazon Bedrock using AWS SDKs available in Python, Java, Node.js, and more.

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Enhance speech synthesis and video generation models with RLHF using audio and video segmentation in Amazon SageMaker

AWS Machine Learning Blog

We guide you through deploying the necessary infrastructure using AWS CloudFormation , creating an internal labeling workforce, and setting up your first labeling job. This precision helps models learn the fine details that separate natural from artificial-sounding speech. We demonstrate how to use Wavesurfer.js

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How Rocket Companies modernized their data science solution on AWS

AWS Machine Learning Blog

The Hadoop environment was hosted on Amazon Elastic Compute Cloud (Amazon EC2) servers, managed in-house by Rockets technology team, while the data science experience infrastructure was hosted on premises. Communication between the two systems was established through Kerberized Apache Livy (HTTPS) connections over AWS PrivateLink.

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Build a conversational data assistant, Part 2 – Embedding generative business intelligence with Amazon Q in QuickSight

AWS Machine Learning Blog

First, we define Pydantic data models to structure the FM output: class QTopicQuestionPair(BaseModel): """A question related to a Q topic.""" topic_id: str = Field(., First, we define Pydantic data models to structure the FM output: class QTopicQuestionPair(BaseModel): """A question related to a Q topic.""" topic_id: str = Field(.,

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Accelerating Mixtral MoE fine-tuning on Amazon SageMaker with QLoRA

AWS Machine Learning Blog

Using this approach, you can focus on developing and refining the model while using the fully managed training infrastructure provided by SageMaker Training. Implementation details We spin up the cluster by calling the SageMaker control plane through APIs or the AWS Command Line Interface (AWS CLI) or using the SageMaker AWS SDK.