Deploying and Monitoring Image Classification Workflow Using Amazon AWS

Setting Up the Notebook:

Utilized Python 3 (Data Science) kernel on an ml.t3.medium SageMaker notebook instance.
Prepared the environment for working with the CIFAR dataset for Image Classification.

Data Staging:

Extracted the CIFAR-100 dataset from the University of Toronto's hosting service.
Performed ETL (Extract, Transform, Load) operations to prepare the data into a usable format for production.

Drafting Lambdas and Step Function Workflow:

Developed and deployed three Lambda functions to work with a simplified data object.
Organized these Lambda functions using the Step Functions visual editor, chaining them together to create a workflow.
Configured inputs and outputs for Lambda functions within the Step Functions, ensuring proper handling and descriptive naming.

Testing and Evaluation:

Executed multiple Step Function invocations using data from the provided test folder.
Ensured the workflow correctly passed predictions to downstream systems while appropriately handling errors.
Utilized SageMaker Model Monitor to generate recordings of data and inferences for visualization.
Conducted test inputs generation using a function to verify workflow performance.

Conclusion: The workflow successfully underwent deployment and testing, reliably passing predictions and handling errors as expected. Through Step Functions orchestration and SageMaker Model Monitor utilization, the machine learning workflow for Image Classification exhibited consistent and reliable behavior, vital for its successful deployment in production settings.