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3 Ways to Learn Data Science and Get a Job in 2024

Towards AI

All the way back in 2012, Harvard Business Review said that Data Science was the sexiest job of the 21st century and recently followed up with an updated version of their article. I mean, ML engineers often spend most of their time handling and understanding data. So, how is a data scientist different from an ML engineer?

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Promote pipelines in a multi-environment setup using Amazon SageMaker Model Registry, HashiCorp Terraform, GitHub, and Jenkins CI/CD

AWS Machine Learning Blog

Policy 3 – Attach AWSLambda_FullAccess , which is an AWS managed policy that grants full access to Lambda, Lambda console features, and other related AWS services.

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Scale ML workflows with Amazon SageMaker Studio and Amazon SageMaker HyperPod

AWS Machine Learning Blog

This integration addresses these hurdles by providing data scientists and ML engineers with a comprehensive environment that supports the entire ML lifecycle, from development to deployment at scale. In this post, we walk you through the process of scaling your ML workloads using SageMaker Studio and SageMaker HyperPod.

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Use Amazon SageMaker Studio with a custom file system in Amazon EFS

AWS Machine Learning Blog

Amazon SageMaker Studio is the latest web-based experience for running end-to-end machine learning (ML) workflows. SageMaker Studio offers a suite of integrated development environments (IDEs), which includes JupyterLab , Code Editor , as well as RStudio.

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Four approaches to manage Python packages in Amazon SageMaker Studio notebooks

Flipboard

There are also limited options for ad hoc script customization by users, such as data scientists or ML engineers, due to permissions of the user profile execution role. Depending on how many packages are installed and how large they are, the lifecycle script might even timeout.

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Use Amazon SageMaker Model Card sharing to improve model governance

AWS Machine Learning Blog

For the AWS IAM policy configured with the correct credentials, make sure that you have permissions to create, edit, and delete model cards within Amazon SageMaker. For more information, refer to Configure the AWS CLI. aws sagemaker describe-model-card --model-card-name Now you can make changes to this model card from this account.

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Use Amazon SageMaker Model Cards sharing to improve model governance

AWS Machine Learning Blog

For the AWS IAM policy configured with the correct credentials, make sure that you have permissions to create, edit, and delete model cards within Amazon SageMaker. For more information, refer to Configure the AWS CLI. aws sagemaker describe-model-card --model-card-name Now you can make changes to this model card from this account.

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