AmazonSageMakerCourse and ml-aws-specialty-lab

AmazonSageMakerCourse
51
Established
ml-aws-specialty-lab
41
Emerging
Maintenance 0/25
Adoption 10/25
Maturity 16/25
Community 25/25
Maintenance 0/25
Adoption 10/25
Maturity 8/25
Community 23/25
Stars: 238
Forks: 408
Downloads: —
Commits (30d): 0
Language: Jupyter Notebook
License: —
Stars: 143
Forks: 76
Downloads: —
Commits (30d): 0
Language: Jupyter Notebook
License: —
Stale 6m No Package No Dependents
No License Stale 6m No Package No Dependents

About AmazonSageMakerCourse

ChandraLingam/AmazonSageMakerCourse

In this AWS Machine Learning Specialty Course, You will gain first-hand experience on how to train, optimize, deploy, and integrate ML in AWS cloud. Learn how to use AWS Built-in SageMaker algorithms and AI, How to Bring Your Own Algorithm, Zero Downtime Model Deployment Options, How to Integrate and Invoke ML from your Application, Automated Hyperparameter Tuning

This course teaches you how to build, refine, and deploy machine learning models on Amazon's cloud platform, AWS SageMaker. You'll learn to take raw data and transform it into working AI solutions ready for integration into your applications. This is designed for IT professionals, data scientists, and machine learning engineers who need to manage AI workflows in a cloud environment.

cloud-machine-learning ML-operations data-science AWS-cloud model-deployment

About ml-aws-specialty-lab

FabG/ml-aws-specialty-lab

Repo with resources to pass the AWS ML Specialty exam

This collection of notes, Jupyter notebooks, and white papers helps prepare you for the AWS Machine Learning Specialty certification exam. It condenses essential information from various courses and documents, providing a streamlined study path. This is for machine learning practitioners and data scientists looking to validate their ability to design, implement, deploy, and maintain ML solutions on AWS.

AWS certification Machine Learning Engineering Data Science Operations Cloud Machine Learning ML Solution Architecture

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