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Level up your AI game with AWS AI Ready courses

Dataconomy

In a major move to revolutionize AI education, Amazon has launched the AWS AI Ready courses, offering eight free courses in AI and generative AI. This initiative is a direct response to the findings of an AWS study that pointed out a “strong demand” for AI-savvy professionals and the potential for higher salaries in this field.

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Build an end-to-end MLOps pipeline using Amazon SageMaker Pipelines, GitHub, and GitHub Actions

AWS Machine Learning Blog

The built-in project templates provided by Amazon SageMaker include integration with some of third-party tools, such as Jenkins for orchestration and GitHub for source control, and several utilize AWS native CI/CD tools such as AWS CodeCommit , AWS CodePipeline , and AWS CodeBuild. all implemented via CloudFormation.

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professionals

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Philips accelerates development of AI-enabled healthcare solutions with an MLOps platform built on Amazon SageMaker

AWS Machine Learning Blog

This is a joint blog with AWS and Philips. Since 2014, the company has been offering customers its Philips HealthSuite Platform, which orchestrates dozens of AWS services that healthcare and life sciences companies use to improve patient care.

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Unlocking the Power of AI with Implemented Machine Learning Ops Projects

Becoming Human

It covers everything from data preparation and model training to deployment, monitoring, and maintenance. The MLOps process can be broken down into four main stages: Data Preparation: This involves collecting and cleaning data to ensure it is ready for analysis.

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Roadmap to Learn Data Science for Beginners and Freshers in 2023

Becoming Human

Amazon SageMaker is a managed service offered by Amazon Web Services (AWS) that provides a comprehensive platform for building, training, and deploying machine learning models at scale. It includes a range of tools and features for data preparation, model training, and deployment, making it an ideal platform for large-scale ML projects.

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Must-Have Skills for a Machine Learning Engineer

Pickl AI

Familiarity with cloud computing tools supports scalable model deployment. Data Transformation Transforming data prepares it for Machine Learning models. Encoding categorical variables converts non-numeric data into a usable format for ML models, often using techniques like one-hot encoding.

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Predicting the Future of Data Science

Pickl AI

Currently, organisations across sectors are leveraging Data Science to improve customer experiences, streamline operations, and drive strategic initiatives. A key aspect of this evolution is the increased adoption of cloud computing, which allows businesses to store and process vast amounts of data efficiently.