MLOps Engineering on AWS Course Overview
The 'MLOps Engineering on AWS Course Overview' equips data scientists, machine learning engineers, and IT professionals with essential skills to streamline machine learning workflows. Emphasizing the integration of DevOps principles in ML operations, this course is vital for optimizing model deployment, monitoring, and management, ultimately enhancing collaboration and efficiency in delivering AI solutions.
Course outline & what you'll learn
- Definition and importance of MLOps
Overview of the MLOps lifecycle
- Introduction to AWS Machine Learning services
Overview of AWS infrastructure for MLOps
- Data collection, cleaning, and preprocessing
- Data storage solutions in AWS
- Developing machine learning models using SageMaker
- Experimentation and hyperparameter tuning
- Strategies for deploying ML models
- Using Amazon SageMaker for model deployment
- Setting up monitoring for deployed models
- Model performance tracking and feedback loops
- Implementing CI/CD pipelines for ML workflows
- Tools and best practices for automation
- Ensuring data privacy and security in AWS
- Compliance considerations for ML applications
- Analyzing successful MLOps implementations
- Lessons learned from industry use cases
- Hands-on project applying MLOps concepts on AWS
- Presentation and evaluation of projects
Why train with Traincrest
This AWS course is delivered by Traincrest's certified instructors, live online or in the classroom, with hands-on labs and a 98% exam success rate. Trusted by 500+ companies and 50,000+ students worldwide.