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Enhance your Amazon Redshift cloud data warehouse with easier, simpler, and faster machine learning using Amazon SageMaker Canvas

AWS Machine Learning Blog

Conventional ML development cycles take weeks to many months and requires sparse data science understanding and ML development skills. Business analysts’ ideas to use ML models often sit in prolonged backlogs because of data engineering and data science team’s bandwidth and data preparation activities.

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6 AI tools revolutionizing data analysis: Unleashing the best in business

Data Science Dojo

Scikit-learn can be used for a variety of data analysis tasks, including: Classification Regression Clustering Dimensionality reduction Feature selection Leveraging Scikit-learn in data analysis projects Scikit-learn can be used in a variety of data analysis projects. It is open-source, so it is free to use and modify.

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Build ML features at scale with Amazon SageMaker Feature Store using data from Amazon Redshift

Flipboard

Deploy the CloudFormation template Complete the following steps to deploy the CloudFormation template: Save the CloudFormation template sm-redshift-demo-vpc-cfn-v1.yaml For Prepare template , select Template is ready. Enter a stack name, such as Demo-Redshift. yaml locally. On the AWS CloudFormation console, choose Create stack.

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Your guide to generative AI and ML at AWS re:Invent 2024

AWS Machine Learning Blog

As attendees circulate through the GAIZ, subject matter experts and Generative AI Innovation Center strategists will be on-hand to share insights, answer questions, present customer stories from an extensive catalog of reference demos, and provide personalized guidance for moving generative AI applications into production.

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Machine learning with decentralized training data using federated learning on Amazon SageMaker

AWS Machine Learning Blog

Many ML algorithms train over large datasets, generalizing patterns it finds in the data and inferring results from those patterns as new unseen records are processed. Data is split into a training dataset and a testing dataset. Details of the data preparation code are in the following notebook.

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“Fall in love with your data”—Snorkel AI’s Enterprise LLM Summit

Snorkel AI

Data scientists can best improve LLM performance on specific tasks by feeding them the right data prepared in the right way. Representation models encode meaningful features from raw data for use in classification, clustering, or information retrieval tasks. Book a demo today.

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Snowflake Snowpark: cloud SQL and Python ML pipelines

Snorkel AI

And that’s really key for taking data science experiments into production. It won’t be a long demo, it’ll be a very quick demo of what you can do and how you can operationalize stuff in Snowflake. And finally, you’ll see that in action today. I don’t have a lot of time, so we’ll jump into it.

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