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    "# Create Amazon SageMaker Autopilot job for audio embedding classification\n",
    "- Use Python 3 Data Scientist kernel in SageMaker Studio\n",
    "- Train and deploy this model after AudioEmbeddingModel\n",
    "- Create a CSV file with audio embedding output from AudioEembeddingModel\n",
    "- Upload CSV file into S3 and run following code to generate an Autopilot job"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import boto3\n",
    "client = boto3.client('sagemaker')\n",
    "response = client.create_auto_ml_job(\n",
    "    AutoMLJobName='Audio_Embedding_Classification_Autopilot',\n",
    "    InputDataConfig=[\n",
    "        {\n",
    "            'DataSource': {\n",
    "                'S3DataSource': {\n",
    "                    'S3DataType': 'S3Prefix',\n",
    "                    'S3Uri': 's3://S3_BUCKET_NAME/PATH/TO/DATA.csv'\n",
    "                }\n",
    "            },\n",
    "            'CompressionType': 'None',\n",
    "            'TargetAttributeName': 'AUDIO_CLASS'\n",
    "        },\n",
    "    ],\n",
    "    OutputDataConfig={\n",
    "        'S3OutputPath': 's3://S3_BUCKET_NAME/AutoML_output/'\n",
    "    },\n",
    "    ProblemType='MulticlassClassification',\n",
    "    AutoMLJobObjective={\n",
    "        'MetricName':'F1'\n",
    "    },\n",
    "    AutoMLJobConfig={\n",
    "        'CompletionCriteria': {\n",
    "            'MaxCandidates': 250,\n",
    "            'MaxRuntimePerTrainingJobInSeconds': 86400,\n",
    "            'MaxAutoMLJobRuntimeInSeconds': 2592000\n",
    "        },\n",
    "    },\n",
    "    RoleArn='arn:aws:iam::ACCOUNT_NUMBER:role/service-role/AmazonSageMaker-ExecutionRole',\n",
    "    ModelDeployConfig={\n",
    "        'AutoGenerateEndpointName': False,\n",
    "        'EndpointName': 'Audio_Embedding_Classification_Endpoint'\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "response = client.describe_auto_ml_job(\n",
    "    AutoMLJobName='Audio_Embedding_Classification_Autopilot'\n",
    ")\n",
    "response"
   ]
  }
 ],
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