Pearson - AWS Certified Machine Learning - Specialty

Getting the AWS Certified Machine Learning - certification highlights your versatility as an ML engineer. Usually, ML engineers focus on handling data and building models, so if you know can use cloud tools, it makes you an even more valuable as an MLOps engineer. You’ll be able to ingest your own data, get through the feature engineering process, train and evaluate models, and deploy them to where they will be consumed. This certification shows that you know how to do full-stack ML development.

In this series of videos, author Milecia McGregor shares a mix of slides and demonstrations in AWS, along with some examples in Visual Studio with Python. It’s just what you need to learn to pass the exam. It includes an overview of concepts with hands-on work using AWS tools like Kinesis and EMR.

About the Instructor

Milecia McGregor is a software generalist who has worked in numerous areas of tech over the past decade. With a master’s degree in mechanical and aerospace engineering, Milecia has accomplished many groundbreaking projects over the years, including Machine Learning (ML) work for human-computer interfaces on autonomous vehicles; front-end and back-end; data science; robotics; DevOps; cybersecurity; VR; and more. Milecia is also an international speaker in the tech community, with talks covering a variety of topics across multiple programming languages.

Skill Level

Intermediate

Learn How To

Learn effective tips and techniques for passing the AWS Certified Machine Learning - Specialty exam
Identify and implement data ingestion solutions with Kinesis
Evaluate ML models
Deploy ML models with AWS tools

Course requirement

Prerequisites: Knowledge of how to use various AWS tools to deploy ML models into different environments
Knowledge of.data engineering principles and model training and evaluatio

Who Should Take This Course

Job titles: ML engineer, DevOps engineer, Aspiring ML engineer

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