MLOps Engineering on AWS

• Automated ML Pipelines: Use AWS services like SageMaker Pipelines and Step Functions to automate the end-to-end ML workflow.
• Model Deployment: Deploy models using AWS SageMaker endpoints, Lambda functions, or ECS/EKS for scalable and secure inference.
• Monitoring and Logging: Implement monitoring with Amazon CloudWatch and SageMaker Model Monitor to track model performance and operational metrics.
• CI/CD Integration: Integrate machine learning workflows with CI/CD tools like AWS CodePipeline and CodeBuild for continuous integration and deployment.

Before learning MLOps Engineering on AWS, you should have:

1. Cloud Computing Basics: Understanding of AWS services and cloud infrastructure.
2. Machine Learning Fundamentals: Knowledge of machine learning algorithms, model training, and evaluation.
3. Programming Skills: Proficiency in Python or other programming languages used for ML.
4. Data Handling: Experience with data preprocessing, transformation, and storage solutions.

By learning MLOps Engineering on AWS, you gain skills in:

1. Automated ML Workflows: Designing and implementing automated workflows for model training, deployment, and monitoring.
2. Model Deployment: Deploying machine learning models into production environments efficiently and securely.
3. Scalability: Utilizing AWS services to scale ML models and data pipelines based on demand.
4. CI/CD for ML: Applying continuous integration and continuous deployment practices specifically for machine learning projects.