MLOps Engineering on AWS Course Overview

Target Audience for MLOps Engineering on AWS

The MLOps Engineering on AWS course equips learners with the skills to integrate ML workflows with DevOps practices on AWS.

• Data Scientists seeking to streamline ML workflows
• DevOps Engineers transitioning into MLOps roles
• Machine Learning Engineers interested in operationalizing ML models
• IT Professionals aiming for expertise in deploying and monitoring ML models on AWS
• Cloud Engineers looking to specialize in ML infrastructure on AWS
• Software Engineers wanting to understand the MLOps lifecycle
• AI/ML Product Managers overseeing the end-to-end ML model lifecycle
• Technical Project Managers looking to manage MLOps projects
• AWS Certified professionals aiming to deepen their MLOps knowledge
• System Administrators interested in ML model deployment and management

Learning Objectives - What you will Learn in this MLOps Engineering on AWS?

Introduction to the Course's Learning Outcomes:

This MLOps Engineering on AWS course equips students with the skills to automate and streamline ML workflows, ensuring efficient model operations and deployment on AWS.

Learning Objectives and Outcomes:

• Understand the concept of Machine Learning Operations (MLOps) and its goals in automating ML workflows.
• Learn the transition from traditional DevOps to MLOps and the unique considerations in ML workflows.
• Gain hands-on experience with AWS services to build, train, and evaluate machine learning models within MLOps pipelines.
• Acquire knowledge on integrating security best practices into MLOps processes.
• Familiarize with Apache Airflow and Kubernetes for orchestrating and scaling ML workflows.
• Master the use of Amazon SageMaker's suite of tools to streamline the MLOps lifecycle, including model training, tuning, and deployment.
• Develop skills to package models, manage inference operations, and deploy models to production with robustness and scalability.
• Conduct A/B testing and deploy models to edge devices, understanding various deployment patterns.
• Implement monitoring solutions for ML models using Amazon SageMaker Model Monitor and learn the importance of monitoring by design.
• Create an MLOps Action Plan and troubleshoot common issues in MLOps pipelines, ensuring continuous improvement and operational excellence.