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mwaa-ml-classifier-deployment/README.md

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+# Localstack Demo: Training and deploying ML classifier with MWAA
+
+App that creates a DAG inside MWAA that takes a dataset, and builds a classifier model based on the feature columns and targetting column. A classifier is trained and the one with the best accuracy out of a bunch of three algorithms is picked up: SVM, Logistic Regression, and Decision Tree. Finally, the model is deployed as a Lambda function.
+
+To keep it simple, no external dependencies (custom Docker images) were added, and the training happens locally in Airflow. Following that, the model gets deployed as a Lambda function. While not ideal, as usually all workloads are supposed to be off-loaded (i.e. with SageMaker, or EC2 / AWS Batch jobs), but easily trained models can still technically be run with the local executor.
+
+The only input the dag has is a `airflow/variables/dataset_spec` secret in `SecretsManager` service, like the following one:
+
+```json
+{
+    "url": "https://gist.githubusercontent.com/netj/8836201/raw/6f9306ad21398ea43cba4f7d537619d0e07d5ae3/iris.csv",
+    "name": "iris.data",
+    "feature_columns": ["sepal.length", "sepal.width", "petal.length", "petal.width"],
+    "target_column": "variety"
+}
+```
+
+## Prerequisites
+
+* LocalStack
+* Docker
+* Python 3.8+ / Python Pip
+* `make`
+* `jq`
+* `curl`
+* `awslocal`
+
+## Installing
+
+To install the dependencies:
+
+```shell
+make install
+```
+
+## Starting LocalStack
+
+Make sure that LocalStack is started:
+
+```shell
+LOCALSTACK_AUTH_TOKEN=... make start
+```
+
+## Running
+
+Run the sample demo script:
+
+```shell
+make run
+```
+
+## License
+
+This code is available under the Apache 2.0 license.
+