🐾How to implement MLOps practices with AWS Services🐾

Data:
🔹 S3 for data storage
🔹 Glue as a data transformation tool
🔹 EMR for processing big data
🔹 Kinesis for streaming data

Training:
🔹 SageMaker Studio Notebooks to train models
🔹 SageMaker Feature store to version your data
🔹 SageMaker Model registry to version models

Deployment:
🔹 SageMaker Endpoint for deploying the model
🔹 CloudFormation to deploy your infrastructure as a code
🔹 CodePipeline for CI/CD for model deployment
🔹 SageMaker Pipelines for orchestration of data preparation, model training, and deployment

Monitoring:
🔹 SageMaker Model Monitor to keep track of model drift, data drift, and model errors for SageMaker Endpoint inference
🔹 CloudWatch for monitoring of custom inference endpoints
🔹 SNS for notifications and alerting

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