AWS Certified Developer - Associate

Once a model has been successfully trained and evaluated, it may need to be deployed for real-world predictions.

Deployment strategies depend on application requirements. Some workloads require real-time predictions with low latency, while others can use batch inference for processing large datasets periodically.

Engineers must consider scalability, availability, latency, resource utilization, and cost when selecting a deployment approach.

A production ML solution should also include appropriate monitoring and error-handling mechanisms.

Machine Learning Monitoring

Monitoring is critical after a machine learning model enters production. A model that performs well during development may behave differently when exposed to real-world data.

Changes in input data distributions can lead to data drift, while changes in relationships between variables and outcomes can affect model performance.

Machine learning engineers need to monitor model behavior, infrastructure performance, application errors, and relevant data characteristics.

Continuous monitoring helps organizations identify issues and determine when models may need retraining or improvement.

MLOps and Automation

MLOps combines machine learning with software engineering and operations practices. It helps organizations automate and manage machine learning workflows throughout the lifecycle.

MLOps can include automated data processing, model training, testing, deployment, monitoring, version management, and retraining.