add basic handler for now

model_data/simulation_system/Dockerfiles/Dockerfile.prediction.lambda

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+FROM public.ecr.aws/lambda/python:3.10
+
+# Install python packages
+COPY ../requirements/predictions/predictions.txt requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the project code to user home directory
+COPY ./core ${LAMBDA_TASK_ROOT}/simulation_system/core
+COPY ./MLModel ${LAMBDA_TASK_ROOT}/simulation_system/MLModel
+COPY ./predictions.py ${LAMBDA_TASK_ROOT}/simulation_system/predictions.py
+COPY ./handlers/predictions_app.py ${LAMBDA_TASK_ROOT}/simulation_system/predictions_app.py
+
+# Run off a lambda trigger
+CMD [ "prediction_app.handler" ]

model_data/simulation_system/Dockerfiles/Dockerfile.training.lambda

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+FROM public.ecr.aws/lambda/python:3.10
+
+# Install python packages
+COPY ../requirements/training/training.txt requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the project code to user home directory
+COPY ./core ${LAMBDA_TASK_ROOT}/simulation_system/core
+COPY ./MLModel ${LAMBDA_TASK_ROOT}/simulation_system/MLModel
+COPY ./training.py ${LAMBDA_TASK_ROOT}/simulation_system/training.py
+COPY ./handlers/training_app.py ${LAMBDA_TASK_ROOT}/simulation_system/training_app.py
+
+# Run off a lambda trigger
+CMD [ "training_app.handler" ]

model_data/simulation_system/handlers/predictions_app.py

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+import os
+import urllib.parse
+from predictions import prediction
+
+
+def handler(event, context):
+    """
+    Take in event and trigger the prediction pipeline
+    """
+
+    # Assuming a file in a bucket landing for now?
+    # Assuming we have a model to use
+
+    bucket = event["Records"][0]["s3"]["bucket"]["name"]
+    key = urllib.parse.unquote_plus(
+        event["Records"][0]["s3"]["bucket"]["key"], encoding="utf-8"
+    )
+
+    prediction_file = bucket + "/" + key
+
+    # TODO: put a model into s3, both locally and in aws
+    model_path = os.environ.get("MODEL_PATH", "http://minio:9000/data/model_directory/")
+
+    try:
+        prediction(model_path=model_path, data_path=prediction_file)
+    except (Exception, KeyError, ValueError):
+        print("Prediction failed")