Quick Links: Helpful Resources

**ML Ops **

• Upscale ML Lifecycle with MLOps: Whitepaper https://learn.microsoft.com/en-us/azure/architecture/ai-ml/guide/mlops-technical-paper

• Azure MLOps Accelerator https://microsoft.github.io/azureml-ops-accelerator/

• ML Ops principles https://ml-ops.org/content/mlops-principles

• Special Intereste Group (SIG) MLOps: https://lists.cd.foundation/g/sig-mlops

AWS MLOps

• Amazon SageMaker MLOps https://aws.amazon.com/sagemaker/mlops/

• What is MLOps https://aws.amazon.com/what-is/mlops/

• Automate MLOps with SageMaker Projects: https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-projects.html

• MLFlow: Manage machine learning experiments using Amazon SageMaker with MLflow https://docs.aws.amazon.com/sagemaker/latest/dg/mlflow.html

• SageMaker Pipelines https://docs.aws.amazon.com/sagemaker/latest/dg/pipelines.html

• ML Lineage Tracking https://docs.aws.amazon.com/sagemaker/latest/dg/lineage-tracking.html

• MLOps Workload Orchestrator on AWS https://aws.amazon.com/solutions/implementations/mlops-workload-orchestrator/

Azure MLOps

• ML Pipelines https://learn.microsoft.com/en-us/azure/machine-learning/concept-ml-pipelines?view=azureml-api-2

• ML Registry for MLOps https://learn.microsoft.com/en-us/azure/machine-learning/concept-machine-learning-registries-mlops?view=azureml-api-2

• CI/CD Pipelines for ML using Azure DevOps https://learn.microsoft.com/en-us/azure/architecture/ai-ml/guide/mlops-python

• Azure DevOps of Machine Learning https://techcommunity.microsoft.com/t5/microsoft-developer-community/introduction-to-azure-devops-for-machine-learning/ba-p/857829

• MLOps for Python Model https://learn.microsoft.com/en-us/azure/architecture/ai-ml/guide/mlops-python

• Machine Learning Operations in Azure https://learn.microsoft.com/en-us/azure/architecture/ai-ml/guide/machine-learning-operations-v2

• Classical MLOps Architecture Diagram on Azure https://learn.microsoft.com/en-us/azure/architecture/ai-ml/guide/_images/classical-ml-architecture.png