Fraud detection workflow example

source/conf.py

@@ -49,7 +49,7 @@
         "rapids_conda_channels": "-c rapidsai-nightly -c conda-forge",
         "rapids_conda_packages": f"rapids={nightly_version} python=3.13 'cuda-version>=12.0,<=12.9'",
         "rapids_pip_index": "https://pypi.anaconda.org/rapidsai-wheels-nightly/simple",
-        "rapids_pip_version": f"{nightly_version}.*,>=0.0.0a0",
+        "rapids_pip_version": f"~={nightly_version}.0a0",
         # SageMaker Notebook Instance examples need to stay pinned to an older RAPIDS until this is resolved:
         # https://github.com/rapidsai/deployment/issues/520
         "rapids_sagemaker_conda_packages": f"rapids={nightly_version} python=3.12 cuda-version=13",

source/examples/fraud-detection-mlops-pipeline/.env.example

@@ -0,0 +1,22 @@
+# Required: NGC API key for pulling the training container
+NGC_API_KEY=
+
+# MLflow tracking server URL
+MLFLOW_TRACKING_URI=http://localhost:5050
+
+# Prefect API URL
+PREFECT_API_URL=http://localhost:4200/api
+
+# Triton server URL
+TRITON_HTTP_URL=localhost:8000
+TRITON_GRPC_URL=localhost:8001
+
+# Pipeline data paths (adjust for your environment)
+DATA_ROOT=./data/TabFormer
+MODEL_OUTPUT_DIR=./data/trained_models
+TRITON_MODEL_REPO=./data/models
+
+# Docker-compose volume paths
+MLFLOW_DATA_DIR=./mlflow-data
+PREFECT_DATA_DIR=./prefect-data
+TRITON_IMAGE=triton-fraud:latest

source/examples/fraud-detection-mlops-pipeline/docker-compose.yml

@@ -0,0 +1,51 @@
+services:
+  mlflow:
+    image: ghcr.io/mlflow/mlflow:v3.12.0
+    ports:
+      - "5050:5050"
+    volumes:
+      - ${MLFLOW_DATA_DIR:-./mlflow-data}:/mlflow
+    command: >
+      mlflow server
+      --host 0.0.0.0
+      --port 5050
+      --backend-store-uri sqlite:///mlflow/mlflow.db
+      --default-artifact-root mlflow-artifacts:/
+      --serve-artifacts
+      --artifacts-destination /mlflow/artifacts
+
+  prefect:
+    image: prefecthq/prefect:3-latest
+    ports:
+      - "4200:4200"
+    volumes:
+      - ${PREFECT_DATA_DIR:-./prefect-data}:/root/.prefect
+    command: prefect server start --host 0.0.0.0
+
+  triton:
+    image: ${TRITON_IMAGE:-triton-fraud:latest}
+    environment:
+      PYTHONDONTWRITEBYTECODE: "1"
+    ports:
+      - "8000:8000"
+      - "8001:8001"
+      - "8002:8002"
+    volumes:
+      - ${TRITON_MODEL_REPO}:/models
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities: [gpu]
+    command: >
+      tritonserver
+      --model-repository=/models
+      --model-control-mode=explicit
+      --exit-timeout-secs=6000
+      --http-port=8000
+      --grpc-port=8001
+      --metrics-port=8002
+    profiles:
+      - gpu

source/examples/fraud-detection-mlops-pipeline/environment.yml

@@ -0,0 +1,19 @@
+name: fraud-mlops
+channels:
+  - rapidsai
+  - conda-forge
+  - nvidia
+dependencies:
+  - python=3.13
+  - cuda-version>=13.0,<=13.1
+  - rapids=26.04
+  - prefect
+  - mlflow=3.12.0
+  - tritonclient
+  - aiohttp
+  - geventhttpclient
+  - python-dotenv
+  - category_encoders
+  - scipy
+  - networkx
+  - matplotlib