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AWS: Machine Learning Pipeline & Data Science with SageMaker + Exam Voucher

Cloud Institute's AWS Certified Machine Learning – Specialty Course helps individuals and organizations identify and develop talent with critical skills for implementing cloud initiatives. Earning AWS Certified Machine Learning – Specialty validates expertise in building, training, tuning, and deploying machine learning (ML) models on AWS.

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AWS: Machine Learning Pipeline & Data Science with SageMaker + Exam Voucher

Overview

The AWS Certified Machine Learning – Specialty Course from Cloud Institute is intended for individuals who perform a development or data science role and have more than one year of experience developing, architecting, or running machine learning/deep learning workloads in the AWS Cloud. In addition to helping you earn the AWS Certified Machine Learning – Specialty certification, this course will validate expertise in building, training, tuning, and deploying machine learning (ML) models on AWS. 
 

Audience: 

This course is intended for people who have: 

  • At least two years of hands-on experience developing, architecting, and running ML or deep learning workloads in the AWS Cloud 
  • Ability to express the intuition behind basic ML algorithms 
  • Experience performing basic hyperparameter optimization 
  • Experience with ML and deep learning frameworks 
  • Ability to follow model-training, deployment, and operational best practices 

 

Prerequisites: 

There are no prerequisites required by AWS, but it is recommended that you hold the AWS Certified Cloud Practitioner or a current associate-level certification: AWS Certified Solutions Architect - AssociateAWS Certified Developer - Associate, or AWS Certified SysOps Administrator - Associate.

Outline

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5 Days
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Cloud Institute's AWS Certified Machine Learning – Specialty Course helps individuals and organizations identify and develop talent with critical skills for implementing cloud initiatives. Earning AWS Certified Machine Learning – Specialty validates expertise in building, training, tuning, and deploying machine learning (ML) models on AWS.

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