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fix: MLFlow E2E Example Notebook (5513) #5727

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fix: MLFlow E2E Example Notebook (5513) #5727

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29 changes: 14 additions & 15 deletions v3-examples/ml-ops-examples/v3-mlflow-train-inference-e2e-example.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -333,22 +333,24 @@
"from mlflow import MlflowClient\n",
"\n",
"client = MlflowClient()\n",
"registered_model = client.get_registered_model(name=MLFLOW_REGISTERED_MODEL_NAME)\n",
"\n",
"latest_version = registered_model.latest_versions[0]\n",
"# MLflow 3.x removed RegisteredModel.latest_versions - use search_model_versions instead\n",
"latest_version = client.search_model_versions(\n",
" filter_string=f\"name='{MLFLOW_REGISTERED_MODEL_NAME}'\",\n",
" order_by=[\"version_number DESC\"],\n",
" max_results=1\n",
")[0]\n",
"\n",
"model_version = latest_version.version\n",
"model_source = latest_version.source\n",
"\n",
"# Get S3 URL of model files (for info only)\n",
"artifact_uri = client.get_model_version_download_uri(MLFLOW_REGISTERED_MODEL_NAME, model_version)\n",
"\n",
"# MLflow model registry path to use with ModelBuilder\n",
"mlflow_model_path = f\"models:/{MLFLOW_REGISTERED_MODEL_NAME}/{model_version}\"\n",
"\n",
"print(f\"Registered Model: {MLFLOW_REGISTERED_MODEL_NAME}\")\n",
"print(f\"Latest Version: {model_version}\")\n",
"print(f\"Source: {model_source}\")\n",
"print(f\"Model artifacts location: {artifact_uri}\")"
"print(f\"Source (model artifacts location): {model_source}\")\n",
"print(f\"MLflow model path for deployment: {mlflow_model_path}\")"
]
},
{
Expand Down Expand Up @@ -481,19 +483,16 @@
"metadata": {},
"outputs": [],
"source": [
"import boto3\n",
"\n",
"# Test with JSON input\n",
"test_data = [[0.1, 0.2, 0.3, 0.4]]\n",
"\n",
"runtime_client = boto3.client('sagemaker-runtime')\n",
"response = runtime_client.invoke_endpoint(\n",
" EndpointName=core_endpoint.endpoint_name,\n",
" Body=json.dumps(test_data),\n",
" ContentType='application/json'\n",
"result = core_endpoint.invoke(\n",
" body=json.dumps(test_data),\n",
" content_type=\"application/json\"\n",
")\n",
"\n",
"prediction = json.loads(response['Body'].read().decode('utf-8'))\n",
"# Decode and display the result\n",
"prediction = json.loads(result.body.read().decode('utf-8'))\n",
"print(f\"Input: {test_data}\")\n",
"print(f\"Prediction: {prediction}\")"
]
Expand Down
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