IT Skills     Cloud Computing and Virtualization     Amazon     AWS Certified Machine Learning

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Training a machine learning (ML) model is the first step of many when developing ML applications that enable businesses to discover new trends within broad and diverse data sets. Use this course to learn more about SageMaker's built-in algorithm and perform model training, evaluation, monitoring, tuning, and deployment using Amazon Elastic Compute Cloud (EC2) instances.
Begin by examining factorization machines and the selection of EC2 instances. Next, you'll discover how to perform model training, evaluation, and deployment. You'll wrap up the course by exploring the steps involved in tuning and testing ML models.
After you're done with this course, you'll have the skills and knowledge to successfully train and evaluate a model, further preparing you for the AWS Certified Machine Learning – Specialty certification exam.

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Objectives

AWS Certified Machine Learning: Model Training & Evaluation discover the key concepts covered in this course describe how factorization machines work and specify why they are powerful tools for recommender systems list and describe EC2 instances that can be used with SageMaker work with training recommender system on Amazon Reviews dataset using Python and SageMaker demonstrate how to reduce cost while training machine learning algorithms using Spot instances evaluate a trained machine learning algorithm deploy a machine learning model using API endpoints monitor API usage in real-time work with feature engineering and machine learning experimentations using Python and SageMaker demonstrate how to run hyperparameter tuning jobs with SageMaker using Python and Amazon Reviews dataset conduct A/B testing for models trained on Amazon Reviews dataset using production variants summarize the key concepts covered in this course