IT Skills     Cloud Computing and Virtualization     Amazon     AWS Certified Machine Learning

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Building successful machine learning (ML) applications require the transformation of raw data, such that it meets the requirements of individual ML algorithms. Explore how to prepare data using Amazon SageMaker and S3 and create security services for this data through this course.
Start by delving deeper into summary statistics and visualization​ before moving on to security best practices for Amazon SageMaker. You'll also examine Amazon CloudWatch and Amazon CloudTrail in greater detail.
After taking this course, you'll have a solid grasp of various data formats, data security practices, and monitoring and alerting services used in SageMaker. You'll also have the knowledge to prepare data for machine learning and take a step further in your preparation for the AWS Certified Machine Learning – Specialty certification exam.

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Objectives

AWS Certified Machine Learning: Data Preparation & SageMaker Security discover the key concepts covered in this course use summary statistics to perform data preparation for an Amazon Reviews dataset perform visualization for data preparation of an Amazon Reviews dataset recognize the difference between various SageMaker data formats convert a data frame to a sparse matrix outline how to create S3 buckets for data storage work with Amazon Ground Truth for data labeling jobs describe the at rest and in-transit encryption approaches used for security in SageMaker work with SageMaker data accessing approaches, including VPCs, IAM, logs, and monitoring describe how to monitor an AWS system using CloudWatch outline how to record API activity using CloudTrail summarize the key concepts covered in this course