Download Master Practical MLOps for Data Scientists & DevOps on AWS

Description

Master Practical MLOps for Data Scientists & DevOps on AWS is a practical learning course for data scientists and DevOps engineers with AWS published by Udemy Academy. Brush up on your MLOps knowledge. Increase your AI/ML (Artificial Intelligence and Machine Learning) mastery on AWS with expert guidance.

This comprehensive course is designed for people who want to excel in AI/ML or data science roles and approach them with a production-level mindset. During this course, you’ll enhance your skills in designing, building, deploying, optimizing, training, tuning, and maintaining ML solutions for real-world business challenges, leveraging the power of the AWS Cloud in conjunction with DevOps best practices designed for machine learning. is, you use While you may already have a basic understanding of machine learning, you need to know that employers are looking for more than the basics that can be run on a local notebook.

What you will learn in the Master Practical MLOps for Data Scientists & DevOps on AWS training course:

CI/CD pipeline configuration for machine learning projects
Ability to track source code and tutorial images, configuration files with a Git-based repository – AWS CodeCommit
Ability to run builds using AWS CodeBuild
Ability to deploy the application on the server using AWS CodeDeploy
Coordinate MLOps steps using AWS CodePipeline
Identify the right AWS services to implement ML solutions
Do the load test
Monitor endpoint performance
Model drift monitoring
Ability to follow model training best practices
Ability to follow deployment best practices
Ability to follow best operating practices

Who is this course suitable for:

Anyone preparing for data science, machine learning and deep learning interviews.
Anyone interested in learning how to implement machine learning on large-scale data.
Anyone interested in AWS cloud-based machine learning and data science.
Anyone looking to learn best practices for deploying machine learning models in the Cloud.
Anyone looking to learn the best practices for operationalizing machine learning models.