anomaly_detection_using_databricks

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anomaly_detection_using_databricks

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This demo uses the [kaggle CC dataset](https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud) to show it can be applied for anomaly detection. The specific components used are

-> The DBR ML runtime 
    -> DBR managed MLFlow
    -> Databricks ML Serving
    -> Optimized Pyspark Pandas UDFs for batch scoring.

There are a couple of examples here in this repo (as of June 1 2022).

If you are just getting started with anomaly detection, start from `anomaly_detection_using_iforest_dlt` folder. In there you will find the following assets. If you are already familiar with anomaly detection and you want to implement a custom anomaly detection model, head over to the next section of this document.

1. `iforest_train_register.py` - quickly trains an isolation forest model and registers it to the model registry. Tweak it as you see fit for your use.
2. `json_record_generator.py` - shows a way to generate dummy json files should you choose to use the [demo data from kaggle](https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud).
3. `dlt_task_0_iforest_dlt_udf.py` - contains code that wrap the model into a pandas udf for scoring inside DLT
4. `dlt_task_1_anomaly_detector.sql` - shows a simplified end to end DLT workflow that reads the json files created in step 2 and scores that with the trained anomaly detection model registered as a udf on step 3.

To read more about DLT and help getting started with DLT - visit the following [link](https://docs.microsoft.com/en-us/azure/databricks/data-engineering/delta-live-tables/delta-live-tables-quickstart)

### Scoring custom models for anomaly detection using DLT

1. `anomaly_detection_deepsvdd_dbr.py` - shows how to implement a custom MLFlow model on databricks for anomaly detection.
2. `json_record_generator.py` - shows a way to generate dummy json files should you choose to use the [demo data from kaggle](https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud).
3. `anomaly_detector_dlt.sql` - shows a part of the working dlt pipeline that must be used with the code from the step below
4. `Define_and_register_inference_udf.py`- registers and uses the model inside the dlt pipeline

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