article thumbnail

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?

article thumbnail

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.

professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

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.

ML 101
article thumbnail

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.

Python 123
article thumbnail

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.

ML 125
article thumbnail

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.

ML 52
article thumbnail

The 11 Top AI Influencers to Watch in 2024 (Guide)

Viso.ai

Each of these individuals serves as an inspiration for aspiring AI and ML engineers breaking into the field. Solidifying this influence, Koller received the Time’s 100 Most Influential People recognition in 2012. Furthermore, Hinton played a pivotal role in the development of AlexNet for the 2012 ImageNet challenge.