AmazonSageMakerCourse and certified-aws-machine-learning-specialty

Maintenance 0/25
Adoption 10/25
Maturity 16/25
Community 25/25
Maintenance 0/25
Adoption 5/25
Maturity 8/25
Community 15/25
Stars: 238
Forks: 408
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License:
Stars: 13
Forks: 5
Downloads:
Commits (30d): 0
Language:
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 certified-aws-machine-learning-specialty

Ernyoke/certified-aws-machine-learning-specialty

AWS Certified Machine Learning - Specialty Exam Preparation Notes

This is a comprehensive study guide designed to help individuals prepare for the AWS Certified Machine Learning - Specialty exam. It provides detailed notes and explanations across key domains like data engineering, exploratory data analysis, modeling, and MLOps using AWS services. Aspiring machine learning engineers, data scientists, and cloud practitioners looking to validate their AWS ML skills will find this resource invaluable.

AWS certification machine learning engineering cloud computing data science professional development

Scores updated daily from GitHub, PyPI, and npm data. How scores work