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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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Recapping the Cloud Amplifier and Snowflake Demo

Towards AI

Recapping the Cloud Amplifier and Snowflake Demo The combined power of Snowflake and Domo’s Cloud Amplifier is the best-kept secret in data management right now — and we’re reaching new heights every day. If you missed our demo, we dive into the technical intricacies of architecting it below.

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

Flipboard

Amazon Redshift is the most popular cloud data warehouse that is used by tens of thousands of customers to analyze exabytes of data every day. Deploy the CloudFormation template Complete the following steps to deploy the CloudFormation template: Save the CloudFormation template sm-redshift-demo-vpc-cfn-v1.yaml

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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 we view Snowflake as a solid data foundation to enable mature data science machine learning practices.

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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 we view Snowflake as a solid data foundation to enable mature data science machine learning practices.

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Agentic AI and AI‑ready data: Transforming consumer‑facing applications

Dataconomy

AI-ready data comes with comprehensive metadata (schema, definitions) to be understandable by humans and AI alike, it maintains a consistent format across historical and real-time streams, and it includes governance/lineage to ensure accuracy and trust. In short, its analytics-grade data prepared for AI. in a query-ready form.

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