This MLOps Engineering on AWS training builds upon and extends the DevOps practice prevalent in software development to build, train, and deploy machine learning (ML) models.
MLOps Engineering, AWS, DevOps, machine learning models, data, model, code, ML deployments, tools, automation, processes, teamwork, data engineers, data scientists, software developers, operations, model prediction, key performance indicators, machine learning operations, DevOps and MLOps differences, machine learning workflow, communications in MLOps, ML workflows automation, Amazon SageMaker, MLOps automation, build, train, test, deploy models, model retraining, deployment process, model package, selecting models for deployment, ML frameworks, built-in algorithms, bring-your-own-models, scaling in machine learning, inference, deployment strategies, edge devices, monitoring importance, MLOps goals, MLOps cases, MLOps development, MLOps security, Apache Airflow, Kubernetes integration, MLOps pipeline, AWS CodeBuild, MLOps Action Plan Workbook, deployment operations, model packaging, SageMaker production variants, A/B testing, Troubleshoot your pipeline, monitoring by design, Human-in-the-loop, Amazon SageMaker Model Monitor, Amazon SageMaker Pipelines, model registry, Feature Store, course review, wrap-up.