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Optimize pet profiles for Purina’s Petfinder application using Amazon Rekognition Custom Labels and AWS Step Functions

AWS Machine Learning Blog

The solution focuses on the fundamental principles of developing an AI/ML application workflow of data preparation, model training, model evaluation, and model monitoring. Tayo Olajide is a seasoned Cloud Data Engineering generalist with over a decade of experience in architecting and implementing data solutions in cloud environments.

AWS 97
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Accelerating AI/ML development at BMW Group with Amazon SageMaker Studio

Flipboard

JuMa is tightly integrated with a range of BMW Central IT services, including identity and access management, roles and rights management, BMW Cloud Data Hub (BMW’s data lake on AWS) and on-premises databases. Furthermore, the notebooks can be integrated into the corporate Git repositories to collaborate using version control.

ML 95
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How Does Snowpark Work?

phData

The Snowflake Data Cloud is a leading cloud data platform that provides various features and services for data storage, processing, and analysis. A new feature that Snowflake offers is called Snowpark, which provides an intuitive library for querying and processing data at scale in Snowflake.

Python 52
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Getting Started With Snowflake: Best Practices For Launching

phData

However, if there’s one thing we’ve learned from years of successful cloud data implementations here at phData, it’s the importance of: Defining and implementing processes Building automation, and Performing configuration …even before you create the first user account. Use with caution, and test before committing to using them.

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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. AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, ML, and application development.

ML 93
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Exploring the AI and data capabilities of watsonx

IBM Journey to AI blog

Visual modeling: Delivers easy-to-use workflows for data scientists to build data preparation and predictive machine learning pipelines that include text analytics, visualizations and a variety of modeling methods. The post Exploring the AI and data capabilities of watsonx appeared first on IBM Blog.

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Snowflake Snowpark: cloud SQL and Python ML pipelines

Snorkel AI

What’s really important in the before part is having production-grade machine learning data pipelines that can feed your model training and inference processes. And that’s really key for taking data science experiments into production. And so that’s where we got started as a cloud data warehouse.

SQL 52