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pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"},"responsivegrid":{"columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnCount":12,":items":{"quickstart_hero":{"id":"quickstart-hero-0d657e285d","quickstartHeroFirstCertifiedTag":{"tagText":"Quickstart","tagColor":"#29B5E8","tagPath":"/content/cq:tags/snowflake-site/taxonomy/solution-center/certification/quickstart","tagIcon":""},"quickstartHeroTitle":{"lines":["Snowflake and Amazon SageMaker Autopilot Integration: Machine Learning with 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src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/machine-learning-with-aws-autopilot/asset_abstract_9.jpg\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ETaking advantage of ML technology usually requires a lot of infrastructure, a multitude of software packages and a small army of highly-skilled engineers building, configuring, and maintaining the complex environment. But what if you could take advantage of machine learning capabilities using SQL from end to end? Imagine if you or your analysts could build models and score datasets at scale without having to learn a new language (such as scala or python), without having to provision and manage infrastructure on prem or in a public cloud, and without the overhead of maintaining additional software packages (such as \u003Ca href=\"https://scikit-learn.org/\"\u003Escikit-learn\u003C/a\u003E, \u003Ca href=\"https://www.tensorflow.org/\"\u003ETensorFlow\u003C/a\u003E, \u003Ca href=\"https://pytorch.org/\"\u003EPyTorch\u003C/a\u003E, …). How could that impact the bottom line of your business?\u003C/p\u003E\n","\u003Cp\u003EThe Snowflake and Amazon SageMaker Autopilot integration is exactly that. It combines the power of Snowflake to process data at scale with the managed AutoML features in SageMaker Autopilot.\u003C/p\u003E\n","\u003Cp\u003EIn this quickstart we will explore an end-to-end example of building a scalable process from data ingestion to scoring millions of data points in seconds using nothing but SQL. Before we get started, we will perform a onetime setup that is completely script driven and only takes 3 simple steps. If you have already completed the setup and are for the ML part of this guide, just skip over the setup steps and start with section “Snowflake/Autopilot Integration”.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EPrerequisites\u003C/h2\u003E\n","\u003Cp\u003EYou need access to an AWS and a Snowflake account. If you do not already have access, follow the links for a \u003Ca href=\"https://aws.amazon.com/free/\"\u003Efree AWS\u003C/a\u003E and a \u003Ca href=\"https://signup.snowflake.com/?utm_cta=quickstarts_\"\u003Efree Snowflake\u003C/a\u003E account.\u003C/p\u003E\n","\u003Cp\u003ENext, clone the project's github repo. It includes all artifacts needed to create the AWS and Snowflake resources as well as the dataset we are going to analyze.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Ecd ~\nmkdir github\ncd ~/github \ngit clone https://github.com/Snowflake-Labs/sfguide-aws-autopilot-integration.git\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou also need Snowflake's command line interface (CLI). If Snowsql isn't already installed on your machine, please follow these \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowsql.html\"\u003Einstructions\u003C/a\u003E.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EUse Case\u003C/h2\u003E\n","\u003Cp\u003EThe dataset we will explore is the \u003Ca href=\"https://www.kaggle.com/ealaxi/paysim1\"\u003ESynthetic Financial Datasets For Fraud Detection\u003C/a\u003E on \u003Ca href=\"https://www.kaggle.com\"\u003Ekaggle\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EThe dataset represents a synthetic set of credit card transactions. Some of those transactions have been labeled as fraudulent, but most of them are not fraudulent. In fact, if you review the documentation on Kaggle, you will find that 99.9% of the transactions are non-fraudulent.\u003C/p\u003E\n","\u003Cp\u003EThe goal of this exercise is to build an ML model that accurately predicts both transaction types, i.e. fraudulent as well as non-fraudulent transactions. After all, who wants to be sitting in a restaurant after a fantastic dinner and be totally embarrassed by their credit card being declined because the credit card company's transaction model hit a false positive and declined your transaction.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetup\u003C/h2\u003E\n","\u003Cp\u003EBuilding a hands-on environment that will allow you to build and score the Model in your environment is very straightforward. It requires 3 simple, script-driven steps.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ESnowflake configuration: Run script setup.sql from your Snowflake console.\u003C/li\u003E\u003Cli\u003ECredentials configuration: Configure credentials in AWS Secrets Manager from the AWS console.\u003C/li\u003E\u003Cli\u003EIntegration configuration: Run the CouldFormation script from the AWS console.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EPro tip: Alternatively,you could run all steps from CLI commands.\u003C/p\u003E\n","\u003Ch3\u003ESnowflake configuration\u003C/h3\u003E\n","\u003Cp\u003EConnect to your snowflake instance by opening a browser and connecting to \u003Ca href=\"https://app.snowflake.com\"\u003Eapp.snowflake.com\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EProvide your login credentials or click "Sign up" to create a free account.\u003C/p\u003E\n","\u003Cp\u003ENext, click "Worksheet" .\u003C/p\u003E\n","\u003Cp\u003EThis opens up a new Worksheet. All SQL statements in the post can be loaded from the \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-aws-autopilot-integration/tree/main/scripts\"\u003Escripts\u003C/a\u003E directory in the \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-aws-autopilot-integration\"\u003Egithub repo\u003C/a\u003E. Copy and paste the content of \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-aws-autopilot-integration/blob/main/scripts/setup.sql\"\u003Esetup.sql\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EClick the little triangle next to the worksheet name, give it a meaningful name (i called it autopilot_setup), click "Import SQL from File", and find the file scripts/setup.sql in your cloned repo.\u003C/p\u003E\n","\u003Cp\u003EThe  script creates a login, which we will use later to perform all steps executed in Snowflake, and a database and schema which holds all objects, i.e. tables, external functions, and JavaScript functions. Please note, that by creating a new user (and role), we don’t have to use ACCOUNTADMIN to run all subsequent steps in this demo. Be sure to update the password, first name, last name, and email address before you run it.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Euse role accountadmin;\ncreate role autopilot_role;\ngrant create integration\n on account\n to role autopilot_role;\n \ncreate database autopilot_db;\ngrant usage on database autopilot_db\n to role autopilot_role;\n \ncreate schema demo;\ngrant ownership\n on schema autopilot_db.demo\n to role autopilot_role;\n \ncreate warehouse autopilot_wh\n with warehouse_size = 'medium';\ngrant modify,monitor,usage,operate \n on warehouse autopilot_wh\n to role autopilot_role; \n \ncreate user autopilot_user\n -- change the value for password in the line below\n password = '<password>'\n login_name = 'autopilot_user'\n display_name = 'autopilot_user' \n -- update the values for first/last name in he lines below\n first_name = '<first name>'\n last_name = '<last name>'\n email = '<your email address>'\n default_role = autopilot_role\n default_warehouse = autopilot_wh\n default_namespace = autopilot_db\n must_change_password = false; \ngrant role autopilot_role\n to user autopilot_user;\n \nselect current_account(),current_region();\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECredentials configuration\u003C/h3\u003E\n","\u003Cp\u003EThe instructions to build all other resources, i.e. API Gateway, S3 bucket, and all Snowflake external and JavaScript functions will be created via an AWS Cloud formation script. Sensitive information like login, password, and fully qualified account ID will be stored using the AWS Secrets Manager.\u003C/p\u003E\n","\u003Cp\u003ETo get started, log into your AWS account and search for Secrets Manager in the Search box in the AWS Console.\u003C/p\u003E\n","\u003Cp\u003EIn the Secrets Manager UI, create the three key/value pairs below. Be sure to configure the fully qualified account ID (including region and cloud).\u003C/p\u003E\n","\u003Cp\u003E<p align="center">![assets/secrets_conf.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/machine-learning-with-aws-autopilot/secrets_conf.png)</p>\u003C/p\u003E\n","\u003Cp\u003EGive your \u003Cstrong\u003Esecret\u003C/strong\u003E configuration a name and save it.\u003C/p\u003E\n","\u003Cp\u003ENext, find your \u003Cstrong\u003Esecrets\u003C/strong\u003E configuration again (the easiest way is to search for it via the Search input field), and copy the Secret ARN. We will need it in the next step when configuring the CloudFormation script.\u003C/p\u003E\n","\u003Ch3\u003EIntegration Configuration (CloudFormation)\u003C/h3\u003E\n","\u003Cp\u003EThe last step is to configure the Snowflake/Autopilot Integration. This used to be a very time-consuming and error-prone process but with AWS CloudFormation it's a piece of cake.\u003C/p\u003E\n","\u003Cp\u003EStart with downloading the \u003Ca href=\"https://github.com/aws-samples/amazon-sagemaker-integration-with-snowflake/blob/main/customer-stack/customer-stack.yml\"\u003ECloudFormation script\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EClick the "Raw" button. This opens a new browser window. "Double click" and click "Save As".\u003C/p\u003E\n","\u003Cp\u003EFor the purpose of this demo we are assuming that you have root access to the AWS console. In case you do not have root access, please ask your AWS admin to run these steps or to create a role based on the permissions listed in policies.zip.\u003C/p\u003E\n","\u003Cp\u003ELog in to your AWS console and select the CloudFormation service.\u003C/p\u003E\n","\u003Cp\u003EThen, click the “Create Stack” button at the top right corner.\u003C/p\u003E\n","\u003Cp\u003E"Template is ready" should be selected by default. Click "Upload a template file" and select the template file you just downloaded.\u003C/p\u003E\n","\u003Cp\u003EThe next screen allows you to enter the stack details for your environment. These are:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EStack name\u003C/li\u003E\u003Cli\u003EapiGatewayName\u003C/li\u003E\u003Cli\u003Es3BucketName\u003C/li\u003E\u003Cli\u003Edatabase name and schema name\u003C/li\u003E\u003Cli\u003Erole to be used for creating the Snowflake objects (external functions and JS function)\u003C/li\u003E\u003Cli\u003ESecrets ARN (from above)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EBe sure to pick consistent names because the AWS resources must be unique in your environment.\u003C/p\u003E\n","\u003Cp\u003E<p align="center">![assets/cloudformation.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/machine-learning-with-aws-autopilot/cloudformation.png)</p>\u003C/p\u003E\n","\u003Cp\u003EGo with the defaults on the next two screens, so click “Next” twice.\u003C/p\u003E\n","\u003Cp\u003EClick the “Acknowledge” checkbox and continue with “Create Stack”.\u003C/p\u003E\n","\u003Cp\u003EYou can follow the “stack creation” by clicking the “Refresh” button. Eventually, you should see “CREATE_COMPLETE”.\u003C/p\u003E\n","\u003Cp\u003ECreating the stack should take about one minute. At this point, the integration has been completely set up and we can head over to Snowflake to start with the data engineering steps.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESnowflake/Autopilot Integration\u003C/h2\u003E\n","\u003Cp\u003ETo follow best practices, we will not use the ACCOUNTADMIN role to run the steps for this demo. Therefore, log in into Snowflake with user autopilot_user. The password should be in your setup scripts.\u003C/p\u003E\n","\u003Cp\u003EThe demo consists of 4 major steps:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EData engineering (import dataset and simple data prep)\u003C/li\u003E\u003Cli\u003EBuild initial model\u003C/li\u003E\u003Cli\u003EScore test dataset and evaluate model\u003C/li\u003E\u003Cli\u003EOptimize model (including hyperparameter tuning)\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EAll SQL statements for this demo are included in \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-aws-autopilot-integration/blob/main/scripts/demo.sql\"\u003Edemo.sql\u003C/a\u003E. Open another Worksheet and copy and paste the content or import the file from your local repo.\u003C/p\u003E\n","\u003Ch3\u003EData Engineering\u003C/h3\u003E\n","\u003Cp\u003ETo make it easier to import the dataset into your Snowflake instance, (the dataset is stored as a zip file on Kaggle), I have included the dataset in the github repo, split into 4 gzipped files. Importing the dataset directly into a Snowflake table is very simple. But before we can load the dataset, we first have to create a table and define a file format to use during the loading process.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Euse role autopilot_role;\nuse database autopilot_db;\nuse schema demo;\nuse warehouse autopilot_wh;\ncreate or replace table cc_dataset (\n step number(38,0),\n type varchar(16777216),\n amount number(38,2),\n nameorig varchar(16777216),\n oldbalanceorg number(38,2),\n newbalanceorig number(38,2),\n namedest varchar(16777216),\n oldbalancedest number(38,2),\n newbalancedest number(38,2),\n isfraud boolean,\n isflaggedfraud boolean\n) ;\ncreate file format cc_file_format type=csv  skip_header=1;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENext, head over to to a terminal session and use snowsql to upload the data files to an internal stage.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Ecd ~/github/sfguide-aws-autopilot-integration/data\nsnowsql -a <accountid> -u autopilot_user -s demo -q "put file://*.gz @~/autopilot/"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThen come back to you Snowflake session and run the copy command to copy the staged data files into the table you had created above.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Ecopy into cc_dataset from @~/autopilot file_format=cc_file_format;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ELet’s briefly review the dataset.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect * from cc_dataset;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe dataset includes  about 6.3 million credit card transaction and has a variety of different attributes.The attribute we want to predict is called "isfraud" of datatype boolean.\u003C/p\u003E\n","\u003Cp\u003ELet’s review the data distribution of the “isfraud” attribute.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect isfraud, count(*) from cc_dataset group by isfraud;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAs you can see, there is a massive class imbalance in the “isfraud” attribute. In this dataset we have 99.9% non-fraudulent transactions and a very small number of transactions have been classified as fraudulent.\u003C/p\u003E\n","\u003Cp\u003EUsually the next step would be data preparation and feature engineering. For this demo we will skip this step. If you want to learn more about data preparation and feature generation, please refer to the links at the end of this post.\u003C/p\u003E\n","\u003Cp\u003EThe only step left in terms of data engineering is to split the dataset into a training and a test dataset. For this demo we will go with a 50/50 split. In Snowflake SQL, this can be accomplished very easily with these two statements:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Ecreate or replace table cc_dataset_train as\n   select * from cc_dataset sample (50);\ncreate or replace table cc_dataset_test as\n   (select * from cc_dataset ) minus \n   (select * from cc_dataset_train) ;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFor good measure, let’s check the split and that we have a reasonable number of each class value in our test and training tables.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E(select 'cc_dataset_train' name, isfraud, count(*) \n from cc_dataset_train group by isfraud) \nunion\n(select 'cc_dataset_test' name, isfraud, count(*) \n from cc_dataset_test group by isfraud)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAs you can see (your numbers might be slightly different), we have an almost perfect 50/50 split with nearly 50% of the fraud cases in either dataset. Of course, in a real world usecase we would take a much closer look at all attributes to ensure that we haven’t introduced bias unintentionally.\u003C/p\u003E\n","\u003Ch3\u003EBuilding the Model\u003C/h3\u003E\n","\u003Cp\u003EThis is where the “rubber meets the road” and where we would usually switch to a different programming environment like Python or Scala, and use different ML packages, like Scikit-Learn, PyTorch, TensorFlow, MLlib, H20 Sparkling Water, the list goes on and on. However, with the Snowflake integration to AWS Autopilot we can initiate the model building process directly from within your Snowflake session using regular SQL syntax and AWS Autopilot does the rest.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect aws_autopilot_create_model (\n  'cc-fraud-prediction-dev'  -- model name\n  ,'cc_dataset_train'        -- training data location\n  ,'isfraud'                 -- target column\n  ,null                      -- objective metric\n  ,null                      -- problem type\n  ,5                         -- number of candidates to be evaluated\n                             --    via hyperparameter tuning \n  ,15*60*60                  -- training timeout\n  ,'True'                    -- create scoring endpoint yes/no\n  ,1*60*60                   -- endpoint TTL\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ELet’s review the SQL statement above. It calls a function that accepts a few parameters. Without going into too much detail (most parameters are pretty self-explanatory), here are the important ones to note:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EModel Name: This is the name of the model to be created. The name must be unique. There is no programmatic way to delete a model.ng If you want to rebuild a model, append a sequential number to the base name to keep the name unique.\u003C/li\u003E\u003Cli\u003ETraining Data Location: This is the name of the table storing the data used to train the model.\u003C/li\u003E\u003Cli\u003ETarget Column: This is the name of the column in the training table we want to predict.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ETo check the current status of the model build process we call another function in the Snowflake/AWS Autopilot integration package.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect aws_autopilot_describe_model('cc-fraud-prediction-dev');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWhen you call the aws_autopilot_describe_model() function repeatedly you will find that the model build process goes through several state transitions.\u003C/p\u003E\n","\u003Cp\u003EBuilding the model should take about 1 hour and eventually you should see “JobStatus=Completed” when you call the aws_autopilot_describe_model() function.\u003C/p\u003E\n","\u003Cp\u003EAnd that’s it. That’s all we had to do to build a model from an arbitrary dataset. Just pick your dataset to train the model with, the attribute you want to predict, and start the process. Everything else, from building the infrastructure needed to train the model, to picking the right algorithm for training the model, and tuning hyperparameters for optimizing the accuracy, is all done automatically.\u003C/p\u003E\n","\u003Ch3\u003ETesting the Model\u003C/h3\u003E\n","\u003Cp\u003ENow, let’s check how well our model achieves the goal of predicting fraud. For that, we need to score the test dataset. The scoring function takes 2 parameters:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EEndpoint Name: This is the name of the API endpoint. The model training process has a parameter controlling whether or not an endpoint is created and if so, what its TTL (time to live) is. By default the endpoint name is the same name as the model name.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EAttributes: This is an array of all attributes used during the model training process.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E  create or replace table cc_dataset_prediction_result as \n    select isfraud,(parse_json(\n        aws_autopilot_predict_outcome(\n          'cc-fraud-prediction-dev'\n          ,array_construct(\n             step,type,amount,nameorig,oldbalanceorg,newbalanceorig\n             ,namedest,oldbalancedest,newbalancedest,isflaggedfraud))\n      ):"predicted_label")::varchar predicted_label\n    from cc_dataset_train;\n\u003C/code\u003E\u003C/pre\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EIf you get an error message saying “Could not find endpoint” when calling the aws_autopilot_predict_outcome() function, it might mean that even though the endpoint had been created during the model training process, it has expired.\u003C/p\u003E\n","\u003Cp\u003ETo check the endpoint, call aws_autopilot_describe_endpoint(). You will get an error message if the endpoint doesn’t exist.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E\nselect aws_autopilot_describe_endpoint('cc-fraud-prediction-dev');\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETo restart the endpoint call the function aws_autopilot_create_endpoint() which takes 3 parameters.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EEndpoint Name: By default, the function aws_autopilot_create_endpoint() creates an endpoint with the same name as the model name. But you can use any name you like, for instance to create a different endpoint for a different purpose, like development or production.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EEndpoint configuration name: By default, the function aws_autopilot_create_endpoint() creates an endpoint configuration named "model_name"-m5–4xl-2. This name follows a naming convention like "model name"-"instance type"-"number of instances". This means that the default endpoint is made up of two m5.4xlarge EC2 instances.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ETTL: TTL means “time to live”. This is the amount of time the endpoint will be active. For TTL, it does not matter whether or not the endpoint is used. If you know that you no longer need the endpoint, it is cost effective to delete the endpoint by calling aws_autopilot_delete_endpoint(). Remember, if necessary, you can always re-create the endpoint by calling aws_autopilot_create_endpoint().\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E  select aws_autopilot_create_endpoint (\n      'cc-fraud-prediction-dev' \n      ,'cc-fraud-prediction-dev-m5-4xl-2' \n      ,1*60*60);\n\u003C/code\u003E\u003C/pre\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThis is an asynchronous function, meaning it completes immediately but we have to check with function aws_autopilot_descrive_endpoint() until the endpoint is ready.\u003C/p\u003E\n","\u003Cp\u003EAfter having validated that the endpoint is running, and scoring the test dataset with the statement above, we are ready to compute the accuracy of our model. To do so we count all occurrences for each of the 4 combinations between the actual and the predicted value. An easy way to do that is to use an aggregation query grouping by those 2 attributes.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect isfraud, predicted_label, count(*)\nfrom cc_dataset_prediction_result\ngroup by isfraud, predicted_label\norder by isfraud, predicted_label;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETo compute the overall accuracy, we then add up the correctly predicted values and divide by the total number of observations. Your numbers might be slightly different but the overall accuracy will be in the 99% range.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect 'overall' predicted_label\n        ,sum(iff(isfraud = predicted_label,1,0)) correct_predictions\n        ,count(*) total_predictions\n        ,(correct_predictions/total_predictions)*100 accuracy\nfrom cc_dataset_prediction_result;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E<p align="center">![assets/dev_all.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/machine-learning-with-aws-autopilot/dev_all.png)</p>\u003C/p\u003E\n","\u003Cp\u003EPretty good, right? Next, let’s drill down and review the accuracy for each of the predicted classes: not fraudulent (majority class) and fraudulent (minority class).\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect predicted_label\n        ,sum(iff(isfraud = predicted_label,1,0)) correct_predictions\n        ,count(*) total_predictions\n        ,(correct_predictions/total_predictions)*100 accuracy\nfrom cc_dataset_prediction_result\ngroup by predicted_label;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E<p align="center">![assets/dev_detail.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/machine-learning-with-aws-autopilot/dev_detail.png)</p>\u003C/p\u003E\n","\u003Cp\u003EAnd that’s where our model shows some problems. Although the majority class is in the 99.99% range, the minority class has a very high rate of false positives. This means that if our model predicts a fraudulent transaction it will be wrong 4 times out of 5. In a practical application, this model would be useless.\u003C/p\u003E\n","\u003Cp\u003ESo what’s the problem? The main reason for this poor performance is that accurately identifying the minority class in a massively imbalanced class distribution is very difficult for ML algorithms. Though it’s not impossible, it requires hundreds of experiments while tuning different parameters.\u003C/p\u003E\n","\u003Cp\u003ESo what can we do to fix it, you ask? That’s where AutoML systems like Autopilot really shine. Instead of having to manually modify the different parameters, Autopilot will automatically pick reasonable values for each parameter, combine them with parameter sets, compute a model for each parameter set, and evaluate the accuracy. Finally, Autopilot will pick the best model based on accuracy, and will build an endpoint that is ready to use.\u003C/p\u003E\n","\u003Ch3\u003EOptimize the Model\u003C/h3\u003E\n","\u003Cp\u003ETo get a much more accurate model, we can use the defaults for the function aws_autopilot_create_model(). Instead of supplying 9 parameters, we only supply the first three parameters. The Snowflake integration with Autopilot automatically picks default values for all of the other parameters. The main difference is that the default number of candidates is 250 instead of 5 as configured before.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect aws_autopilot_create_model (\n  'cc-fraud-prediction-prd' -- model name\n  ,'cc_dataset_train'         -- training data table name\n  ,'isfraud'                  -- target column\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis process will take considerably longer. To check the status run the aws_autopilot_describe_model.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect aws_autopilot_describe_model('cc-fraud-prediction-prd');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ELike you did before, run the scoring function after the model has been built. Check the status periodically using the function aws_autopilot_describe_model(). Re-create the endpoint if it doesn’t exist using the function aws_autopilot_create_endpoint().\u003C/p\u003E\n","\u003Cp\u003EFinally, score the test dataset using aws_autopilot_predict_outcome() and route the output into a different results table.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Ecreate or replace table cc_dataset_prediction_result_prd as \n  select isfraud,(parse_json(\n      aws_autopilot_predict_outcome(\n        'cc-fraud-prediction-prd'\n        ,array_construct(\n           step,type,amount,nameorig,oldbalanceorg,newbalanceorig\n           ,namedest,oldbalancedest,newbalancedest,isflaggedfraud))\n    ):"predicted_label")::varchar predicted_label\n  from cc_dataset_train;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThen count the observations again by actual and predicted value.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect isfraud, predicted_label, count(*)\nfrom cc_dataset_prediction_result_prd\ngroup by isfraud, predicted_label\norder by isfraud, predicted_label;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe overall accuracy is still in the upper 99% range and that is good.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect 'overall' predicted_label\n        ,sum(iff(isfraud = predicted_label,1,0)) correct_predictions\n        ,count(*) total_predictions\n        ,(correct_predictions/total_predictions)*100 accuracy\nfrom cc_dataset_prediction_result_prd;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E<p align="center">![assets/prd_all.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/machine-learning-with-aws-autopilot/prd_all.png)</p>\u003C/p\u003E\n","\u003Cp\u003EHowever, Autopilot shows its real power when we review the per class accuracy. It has improved from 13.3% to a whopping 88.88%.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect predicted_label\n        ,sum(iff(isfraud = predicted_label,1,0)) correct_predictions\n        ,count(*) total_predictions\n        ,(correct_predictions/total_predictions)*100 accuracy\nfrom cc_dataset_prediction_result_prd\ngroup by predicted_label;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E<p align="center">![assets/prd_detail.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/machine-learning-with-aws-autopilot/prd_detail.png)</p>\u003C/p\u003E\n","\u003Cp\u003EWith these results, there is just one obvious question left to answer. Why wouldn’t we always let the Snowflake integration pick all parameters? The main reason is the time it takes to create the model with default parameters. In this particular example it takes 9 hours to produce an optimal model. So if you just want to test the end-to-end process, you may want to ask Autopilot to evaluate only a handful of candidate models. However, when you want to get a model with the best accuracy, go with the defaults. Autopilot will then evaluate 250 candidates.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion\u003C/h2\u003E\n","\u003Cp\u003EHaving managed ML capabilities directly in the Snowflake Data Cloud opens up the world of machine learning for data engineers, data analysts, and data scientists who are primarily working in a more SQL-centric environment. Not only can you take advantage of all the benefits of the Snowflake Data Cloud but now you can add full ML capabilities (model building and scoring) from the same interface. As you have seen in this article, AutoML makes ML via SQL extremely powerful and easy to use.\u003C/p\u003E"],"title":"Snowflake and Amazon SageMaker Autopilot Integration: Machine Learning with SQL",":items":{},":itemsOrder":[],"isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\r\n\u003C!-- ------------------------ --\u003E\r\n## Overview \r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/machine-learning-with-aws-autopilot/asset_abstract_9.jpg)\r\n\r\nTaking advantage of ML technology usually requires a lot of infrastructure, a multitude of software packages and a small army of highly-skilled engineers building, configuring, and maintaining the complex environment. But what if you could take advantage of machine learning capabilities using SQL from end to end? Imagine if you or your analysts could build models and score datasets at scale without having to learn a new language (such as scala or python), without having to provision and manage infrastructure on prem or in a public cloud, and without the overhead of maintaining additional software packages (such as [scikit-learn](https://scikit-learn.org/), [TensorFlow](https://www.tensorflow.org/), [PyTorch](https://pytorch.org/), …). How could that impact the bottom line of your business? \r\n\r\nThe Snowflake and Amazon SageMaker Autopilot integration is exactly that. It combines the power of Snowflake to process data at scale with the managed AutoML features in SageMaker Autopilot.\r\n\r\nIn this quickstart we will explore an end-to-end example of building a scalable process from data ingestion to scoring millions of data points in seconds using nothing but SQL. Before we get started, we will perform a onetime setup that is completely script driven and only takes 3 simple steps. If you have already completed the setup and are for the ML part of this guide, just skip over the setup steps and start with section “Snowflake/Autopilot Integration”.\r\n\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Prerequisites \r\n\r\nYou need access to an AWS and a Snowflake account. If you do not already have access, follow the links for a [free AWS](https://aws.amazon.com/free/) and a [free Snowflake](https://signup.snowflake.com/?utm_cta=quickstarts_) account.\r\n\r\nNext, clone the project's github repo. It includes all artifacts needed to create the AWS and Snowflake resources as well as the dataset we are going to analyze.\r\n\r\n```\r\ncd ~\r\nmkdir github\r\ncd ~/github \r\ngit clone https://github.com/Snowflake-Labs/sfguide-aws-autopilot-integration.git\r\n\r\n```\r\n\r\nYou also need Snowflake's command line interface (CLI). If Snowsql isn't already installed on your machine, please follow these [instructions](https://docs.snowflake.com/en/user-guide/snowsql.html).\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Use Case\r\n\r\nThe dataset we will explore is the [Synthetic Financial Datasets For Fraud Detection](https://www.kaggle.com/ealaxi/paysim1) on [kaggle](https://www.kaggle.com).\r\n\r\nThe dataset represents a synthetic set of credit card transactions. Some of those transactions have been labeled as fraudulent, but most of them are not fraudulent. In fact, if you review the documentation on Kaggle, you will find that 99.9% of the transactions are non-fraudulent.\r\n\r\nThe goal of this exercise is to build an ML model that accurately predicts both transaction types, i.e. fraudulent as well as non-fraudulent transactions. After all, who wants to be sitting in a restaurant after a fantastic dinner and be totally embarrassed by their credit card being declined because the credit card company's transaction model hit a false positive and declined your transaction.\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Setup\r\n\r\nBuilding a hands-on environment that will allow you to build and score the Model in your environment is very straightforward. It requires 3 simple, script-driven steps.\r\n\r\n1. Snowflake configuration: Run script setup.sql from your Snowflake console.\r\n1. Credentials configuration: Configure credentials in AWS Secrets Manager from the AWS console.\r\n1. Integration configuration: Run the CouldFormation script from the AWS console.\r\n\r\nPro tip: Alternatively,you could run all steps from CLI commands.\r\n\r\n### Snowflake configuration\r\n\r\nConnect to your snowflake instance by opening a browser and connecting to [app.snowflake.com](https://app.snowflake.com).\r\n\r\nProvide your login credentials or click \"Sign up\" to create a free account. \r\n\r\nNext, click \"Worksheet\" .\r\n\r\nThis opens up a new Worksheet. All SQL statements in the post can be loaded from the [scripts](https://github.com/Snowflake-Labs/sfguide-aws-autopilot-integration/tree/main/scripts) directory in the [github repo](https://github.com/Snowflake-Labs/sfguide-aws-autopilot-integration). Copy and paste the content of [setup.sql](https://github.com/Snowflake-Labs/sfguide-aws-autopilot-integration/blob/main/scripts/setup.sql).\r\n\r\nClick the little triangle next to the worksheet name, give it a meaningful name (i called it autopilot_setup), click \"Import SQL from File\", and find the file scripts/setup.sql in your cloned repo.\r\n\r\nThe  script creates a login, which we will use later to perform all steps executed in Snowflake, and a database and schema which holds all objects, i.e. tables, external functions, and JavaScript functions. Please note, that by creating a new user (and role), we don’t have to use ACCOUNTADMIN to run all subsequent steps in this demo. Be sure to update the password, first name, last name, and email address before you run it.\r\n\r\n```\r\nuse role accountadmin;\r\ncreate role autopilot_role;\r\ngrant create integration\r\n on account\r\n to role autopilot_role;\r\n \r\ncreate database autopilot_db;\r\ngrant usage on database autopilot_db\r\n to role autopilot_role;\r\n \r\ncreate schema demo;\r\ngrant ownership\r\n on schema autopilot_db.demo\r\n to role autopilot_role;\r\n \r\ncreate warehouse autopilot_wh\r\n with warehouse_size = 'medium';\r\ngrant modify,monitor,usage,operate \r\n on warehouse autopilot_wh\r\n to role autopilot_role; \r\n \r\ncreate user autopilot_user\r\n -- change the value for password in the line below\r\n password = '\u003Cpassword\u003E'\r\n login_name = 'autopilot_user'\r\n display_name = 'autopilot_user' \r\n -- update the values for first/last name in he lines below\r\n first_name = '\u003Cfirst name\u003E'\r\n last_name = '\u003Clast name\u003E'\r\n email = '\u003Cyour email address\u003E'\r\n default_role = autopilot_role\r\n default_warehouse = autopilot_wh\r\n default_namespace = autopilot_db\r\n must_change_password = false; \r\ngrant role autopilot_role\r\n to user autopilot_user;\r\n \r\nselect current_account(),current_region();\r\n```\r\n\r\n### Credentials configuration\r\n\r\nThe instructions to build all other resources, i.e. API Gateway, S3 bucket, and all Snowflake external and JavaScript functions will be created via an AWS Cloud formation script. Sensitive information like login, password, and fully qualified account ID will be stored using the AWS Secrets Manager.\r\n\r\nTo get started, log into your AWS account and search for Secrets Manager in the Search box in the AWS Console.\r\n\r\nIn the Secrets Manager UI, create the three key/value pairs below. Be sure to configure the fully qualified account ID (including region and cloud).\r\n\r\n\u003Cp align=\"center\"\u003E![assets/secrets_conf.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/machine-learning-with-aws-autopilot/secrets_conf.png)\u003C/p\u003E\r\n\r\nGive your **secret** configuration a name and save it.\r\n\r\nNext, find your **secrets** configuration again (the easiest way is to search for it via the Search input field), and copy the Secret ARN. We will need it in the next step when configuring the CloudFormation script.\r\n\r\n### Integration Configuration (CloudFormation)\r\n\r\nThe last step is to configure the Snowflake/Autopilot Integration. This used to be a very time-consuming and error-prone process but with AWS CloudFormation it's a piece of cake.\r\n\r\nStart with downloading the [CloudFormation script](https://github.com/aws-samples/amazon-sagemaker-integration-with-snowflake/blob/main/customer-stack/customer-stack.yml).\r\n\r\nClick the \"Raw\" button. This opens a new browser window. \"Double click\" and click \"Save As\".\r\n\r\nFor the purpose of this demo we are assuming that you have root access to the AWS console. In case you do not have root access, please ask your AWS admin to run these steps or to create a role based on the permissions listed in policies.zip.\r\n\r\nLog in to your AWS console and select the CloudFormation service.\r\n\r\nThen, click the “Create Stack” button at the top right corner.\r\n\r\n\"Template is ready\" should be selected by default. Click \"Upload a template file\" and select the template file you just downloaded.\r\n\r\nThe next screen allows you to enter the stack details for your environment. These are:\r\n\r\n- Stack name\r\n- apiGatewayName\r\n- s3BucketName\r\n- database name and schema name\r\n- role to be used for creating the Snowflake objects (external functions and JS function)\r\n- Secrets ARN (from above)\r\n\r\nBe sure to pick consistent names because the AWS resources must be unique in your environment.\r\n\r\n\u003Cp align=\"center\"\u003E![assets/cloudformation.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/machine-learning-with-aws-autopilot/cloudformation.png)\u003C/p\u003E\r\n\r\nGo with the defaults on the next two screens, so click “Next” twice.\r\n\r\nClick the “Acknowledge” checkbox and continue with “Create Stack”.\r\n\r\nYou can follow the “stack creation” by clicking the “Refresh” button. Eventually, you should see “CREATE_COMPLETE”.\r\n\r\nCreating the stack should take about one minute. At this point, the integration has been completely set up and we can head over to Snowflake to start with the data engineering steps.\r\n\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Snowflake/Autopilot Integration\r\n\r\nTo follow best practices, we will not use the ACCOUNTADMIN role to run the steps for this demo. Therefore, log in into Snowflake with user autopilot_user. The password should be in your setup scripts.\r\n\r\n\r\nThe demo consists of 4 major steps:\r\n\r\n1. Data engineering (import dataset and simple data prep)\r\n2. Build initial model\r\n3. Score test dataset and evaluate model\r\n4. Optimize model (including hyperparameter tuning)\r\n\r\nAll SQL statements for this demo are included in [demo.sql](https://github.com/Snowflake-Labs/sfguide-aws-autopilot-integration/blob/main/scripts/demo.sql). Open another Worksheet and copy and paste the content or import the file from your local repo.\r\n\r\n### Data Engineering\r\n\r\nTo make it easier to import the dataset into your Snowflake instance, (the dataset is stored as a zip file on Kaggle), I have included the dataset in the github repo, split into 4 gzipped files. Importing the dataset directly into a Snowflake table is very simple. But before we can load the dataset, we first have to create a table and define a file format to use during the loading process.\r\n\r\n```\r\nuse role autopilot_role;\r\nuse database autopilot_db;\r\nuse schema demo;\r\nuse warehouse autopilot_wh;\r\ncreate or replace table cc_dataset (\r\n step number(38,0),\r\n type varchar(16777216),\r\n amount number(38,2),\r\n nameorig varchar(16777216),\r\n oldbalanceorg number(38,2),\r\n newbalanceorig number(38,2),\r\n namedest varchar(16777216),\r\n oldbalancedest number(38,2),\r\n newbalancedest number(38,2),\r\n isfraud boolean,\r\n isflaggedfraud boolean\r\n) ;\r\ncreate file format cc_file_format type=csv  skip_header=1;\r\n```\r\n\r\nNext, head over to to a terminal session and use snowsql to upload the data files to an internal stage.\r\n\r\n```\r\ncd ~/github/sfguide-aws-autopilot-integration/data\r\nsnowsql -a \u003Caccountid\u003E -u autopilot_user -s demo -q \"put file://*.gz @~/autopilot/\"\r\n```\r\n\r\nThen come back to you Snowflake session and run the copy command to copy the staged data files into the table you had created above.\r\n\r\n```\r\ncopy into cc_dataset from @~/autopilot file_format=cc_file_format;\r\n```\r\n\r\nLet’s briefly review the dataset.\r\n\r\n```\r\nselect * from cc_dataset;\r\n```\r\n\r\nThe dataset includes  about 6.3 million credit card transaction and has a variety of different attributes.The attribute we want to predict is called \"isfraud\" of datatype boolean. \r\n\r\nLet’s review the data distribution of the “isfraud” attribute.\r\n\r\n```\r\nselect isfraud, count(*) from cc_dataset group by isfraud;\r\n```\r\n\r\nAs you can see, there is a massive class imbalance in the “isfraud” attribute. In this dataset we have 99.9% non-fraudulent transactions and a very small number of transactions have been classified as fraudulent.\r\n\r\nUsually the next step would be data preparation and feature engineering. For this demo we will skip this step. If you want to learn more about data preparation and feature generation, please refer to the links at the end of this post.\r\n\r\nThe only step left in terms of data engineering is to split the dataset into a training and a test dataset. For this demo we will go with a 50/50 split. In Snowflake SQL, this can be accomplished very easily with these two statements:\r\n\r\n```\r\ncreate or replace table cc_dataset_train as\r\n   select * from cc_dataset sample (50);\r\ncreate or replace table cc_dataset_test as\r\n   (select * from cc_dataset ) minus \r\n   (select * from cc_dataset_train) ;\r\n```\r\n\r\nFor good measure, let’s check the split and that we have a reasonable number of each class value in our test and training tables.\r\n\r\n```\r\n(select 'cc_dataset_train' name, isfraud, count(*) \r\n from cc_dataset_train group by isfraud) \r\nunion\r\n(select 'cc_dataset_test' name, isfraud, count(*) \r\n from cc_dataset_test group by isfraud)\r\n```\r\n\r\nAs you can see (your numbers might be slightly different), we have an almost perfect 50/50 split with nearly 50% of the fraud cases in either dataset. Of course, in a real world usecase we would take a much closer look at all attributes to ensure that we haven’t introduced bias unintentionally.\r\n\r\n### Building the Model\r\n\r\nThis is where the “rubber meets the road” and where we would usually switch to a different programming environment like Python or Scala, and use different ML packages, like Scikit-Learn, PyTorch, TensorFlow, MLlib, H20 Sparkling Water, the list goes on and on. However, with the Snowflake integration to AWS Autopilot we can initiate the model building process directly from within your Snowflake session using regular SQL syntax and AWS Autopilot does the rest.\r\n\r\n```\r\nselect aws_autopilot_create_model (\r\n  'cc-fraud-prediction-dev'  -- model name\r\n  ,'cc_dataset_train'        -- training data location\r\n  ,'isfraud'                 -- target column\r\n  ,null                      -- objective metric\r\n  ,null                      -- problem type\r\n  ,5                         -- number of candidates to be evaluated\r\n                             --    via hyperparameter tuning \r\n  ,15*60*60                  -- training timeout\r\n  ,'True'                    -- create scoring endpoint yes/no\r\n  ,1*60*60                   -- endpoint TTL\r\n);\r\n```\r\n\r\nLet’s review the SQL statement above. It calls a function that accepts a few parameters. Without going into too much detail (most parameters are pretty self-explanatory), here are the important ones to note:\r\n\r\n- Model Name: This is the name of the model to be created. The name must be unique. There is no programmatic way to delete a model.ng If you want to rebuild a model, append a sequential number to the base name to keep the name unique.\r\n- Training Data Location: This is the name of the table storing the data used to train the model.\r\n- Target Column: This is the name of the column in the training table we want to predict.\r\n\r\n\r\nTo check the current status of the model build process we call another function in the Snowflake/AWS Autopilot integration package.\r\n\r\n```\r\nselect aws_autopilot_describe_model('cc-fraud-prediction-dev');\r\n```\r\n\r\nWhen you call the aws_autopilot_describe_model() function repeatedly you will find that the model build process goes through several state transitions.\r\n\r\nBuilding the model should take about 1 hour and eventually you should see “JobStatus=Completed” when you call the aws_autopilot_describe_model() function.\r\n\r\nAnd that’s it. That’s all we had to do to build a model from an arbitrary dataset. Just pick your dataset to train the model with, the attribute you want to predict, and start the process. Everything else, from building the infrastructure needed to train the model, to picking the right algorithm for training the model, and tuning hyperparameters for optimizing the accuracy, is all done automatically.\r\n\r\n### Testing the Model\r\n\r\nNow, let’s check how well our model achieves the goal of predicting fraud. For that, we need to score the test dataset. The scoring function takes 2 parameters:\r\n\r\n- Endpoint Name: This is the name of the API endpoint. The model training process has a parameter controlling whether or not an endpoint is created and if so, what its TTL (time to live) is. By default the endpoint name is the same name as the model name.\r\n- Attributes: This is an array of all attributes used during the model training process. \r\n\r\n        create or replace table cc_dataset_prediction_result as \r\n          select isfraud,(parse_json(\r\n              aws_autopilot_predict_outcome(\r\n                'cc-fraud-prediction-dev'\r\n                ,array_construct(\r\n                   step,type,amount,nameorig,oldbalanceorg,newbalanceorig\r\n                   ,namedest,oldbalancedest,newbalancedest,isflaggedfraud))\r\n            ):\"predicted_label\")::varchar predicted_label\r\n          from cc_dataset_train;\r\n\r\n \r\n\r\n\r\nIf you get an error message saying “Could not find endpoint” when calling the aws_autopilot_predict_outcome() function, it might mean that even though the endpoint had been created during the model training process, it has expired.\r\n\r\nTo check the endpoint, call aws_autopilot_describe_endpoint(). You will get an error message if the endpoint doesn’t exist.\r\n\r\n```\r\n\r\nselect aws_autopilot_describe_endpoint('cc-fraud-prediction-dev');\r\n\r\n```\r\n\r\nTo restart the endpoint call the function aws_autopilot_create_endpoint() which takes 3 parameters.\r\n\r\n- Endpoint Name: By default, the function aws_autopilot_create_endpoint() creates an endpoint with the same name as the model name. But you can use any name you like, for instance to create a different endpoint for a different purpose, like development or production.\r\n- Endpoint configuration name: By default, the function aws_autopilot_create_endpoint() creates an endpoint configuration named \"model_name\"-m5–4xl-2. This name follows a naming convention like \"model name\"-\"instance type\"-\"number of instances\". This means that the default endpoint is made up of two m5.4xlarge EC2 instances.\r\n- TTL: TTL means “time to live”. This is the amount of time the endpoint will be active. For TTL, it does not matter whether or not the endpoint is used. If you know that you no longer need the endpoint, it is cost effective to delete the endpoint by calling aws_autopilot_delete_endpoint(). Remember, if necessary, you can always re-create the endpoint by calling aws_autopilot_create_endpoint().\r\n\r\n\r\n\r\n        select aws_autopilot_create_endpoint (\r\n            'cc-fraud-prediction-dev' \r\n            ,'cc-fraud-prediction-dev-m5-4xl-2' \r\n            ,1*60*60);\r\n\r\n\r\nThis is an asynchronous function, meaning it completes immediately but we have to check with function aws_autopilot_descrive_endpoint() until the endpoint is ready.\r\n\r\nAfter having validated that the endpoint is running, and scoring the test dataset with the statement above, we are ready to compute the accuracy of our model. To do so we count all occurrences for each of the 4 combinations between the actual and the predicted value. An easy way to do that is to use an aggregation query grouping by those 2 attributes.\r\n\r\n```\r\nselect isfraud, predicted_label, count(*)\r\nfrom cc_dataset_prediction_result\r\ngroup by isfraud, predicted_label\r\norder by isfraud, predicted_label;\r\n```\r\n\r\nTo compute the overall accuracy, we then add up the correctly predicted values and divide by the total number of observations. Your numbers might be slightly different but the overall accuracy will be in the 99% range.\r\n\r\n```\r\nselect 'overall' predicted_label\r\n        ,sum(iff(isfraud = predicted_label,1,0)) correct_predictions\r\n        ,count(*) total_predictions\r\n        ,(correct_predictions/total_predictions)*100 accuracy\r\nfrom cc_dataset_prediction_result;\r\n```\r\n\r\n\u003Cp align=\"center\"\u003E![assets/dev_all.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/machine-learning-with-aws-autopilot/dev_all.png)\u003C/p\u003E\r\n\r\nPretty good, right? Next, let’s drill down and review the accuracy for each of the predicted classes: not fraudulent (majority class) and fraudulent (minority class).\r\n\r\n```\r\nselect predicted_label\r\n        ,sum(iff(isfraud = predicted_label,1,0)) correct_predictions\r\n        ,count(*) total_predictions\r\n        ,(correct_predictions/total_predictions)*100 accuracy\r\nfrom cc_dataset_prediction_result\r\ngroup by predicted_label;\r\n```\r\n\r\n\u003Cp align=\"center\"\u003E![assets/dev_detail.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/machine-learning-with-aws-autopilot/dev_detail.png)\u003C/p\u003E\r\n\r\nAnd that’s where our model shows some problems. Although the majority class is in the 99.99% range, the minority class has a very high rate of false positives. This means that if our model predicts a fraudulent transaction it will be wrong 4 times out of 5. In a practical application, this model would be useless.\r\n\r\nSo what’s the problem? The main reason for this poor performance is that accurately identifying the minority class in a massively imbalanced class distribution is very difficult for ML algorithms. Though it’s not impossible, it requires hundreds of experiments while tuning different parameters.\r\n\r\nSo what can we do to fix it, you ask? That’s where AutoML systems like Autopilot really shine. Instead of having to manually modify the different parameters, Autopilot will automatically pick reasonable values for each parameter, combine them with parameter sets, compute a model for each parameter set, and evaluate the accuracy. Finally, Autopilot will pick the best model based on accuracy, and will build an endpoint that is ready to use.\r\n\r\n### Optimize the Model\r\n\r\nTo get a much more accurate model, we can use the defaults for the function aws_autopilot_create_model(). Instead of supplying 9 parameters, we only supply the first three parameters. The Snowflake integration with Autopilot automatically picks default values for all of the other parameters. The main difference is that the default number of candidates is 250 instead of 5 as configured before.\r\n\r\n```\r\nselect aws_autopilot_create_model (\r\n  'cc-fraud-prediction-prd' -- model name\r\n  ,'cc_dataset_train'         -- training data table name\r\n  ,'isfraud'                  -- target column\r\n);\r\n```\r\n\r\nThis process will take considerably longer. To check the status run the aws_autopilot_describe_model.\r\n\r\n```\r\nselect aws_autopilot_describe_model('cc-fraud-prediction-prd');\r\n```\r\n\r\nLike you did before, run the scoring function after the model has been built. Check the status periodically using the function aws_autopilot_describe_model(). Re-create the endpoint if it doesn’t exist using the function aws_autopilot_create_endpoint().\r\n\r\nFinally, score the test dataset using aws_autopilot_predict_outcome() and route the output into a different results table.\r\n\r\n```\r\ncreate or replace table cc_dataset_prediction_result_prd as \r\n  select isfraud,(parse_json(\r\n      aws_autopilot_predict_outcome(\r\n        'cc-fraud-prediction-prd'\r\n        ,array_construct(\r\n           step,type,amount,nameorig,oldbalanceorg,newbalanceorig\r\n           ,namedest,oldbalancedest,newbalancedest,isflaggedfraud))\r\n    ):\"predicted_label\")::varchar predicted_label\r\n  from cc_dataset_train;\r\n```\r\n\r\nThen count the observations again by actual and predicted value.\r\n\r\n```\r\nselect isfraud, predicted_label, count(*)\r\nfrom cc_dataset_prediction_result_prd\r\ngroup by isfraud, predicted_label\r\norder by isfraud, predicted_label;\r\n```\r\n\r\nThe overall accuracy is still in the upper 99% range and that is good.\r\n\r\n```\r\nselect 'overall' predicted_label\r\n        ,sum(iff(isfraud = predicted_label,1,0)) correct_predictions\r\n        ,count(*) total_predictions\r\n        ,(correct_predictions/total_predictions)*100 accuracy\r\nfrom cc_dataset_prediction_result_prd;\r\n```\r\n\r\n\u003Cp align=\"center\"\u003E![assets/prd_all.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/machine-learning-with-aws-autopilot/prd_all.png)\u003C/p\u003E\r\n\r\nHowever, Autopilot shows its real power when we review the per class accuracy. It has improved from 13.3% to a whopping 88.88%.\r\n\r\n```\r\nselect predicted_label\r\n        ,sum(iff(isfraud = predicted_label,1,0)) correct_predictions\r\n        ,count(*) total_predictions\r\n        ,(correct_predictions/total_predictions)*100 accuracy\r\nfrom cc_dataset_prediction_result_prd\r\ngroup by predicted_label;\r\n```\r\n\r\n\u003Cp align=\"center\"\u003E![assets/prd_detail.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/machine-learning-with-aws-autopilot/prd_detail.png)\u003C/p\u003E\r\n\r\nWith these results, there is just one obvious question left to answer. Why wouldn’t we always let the Snowflake integration pick all parameters? The main reason is the time it takes to create the model with default parameters. In this particular example it takes 9 hours to produce an optimal model. So if you just want to test the end-to-end process, you may want to ask Autopilot to evaluate only a handful of candidate models. However, when you want to get a model with the best accuracy, go with the defaults. Autopilot will then evaluate 250 candidates.\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Conclusion\r\n\r\nHaving managed ML capabilities directly in the Snowflake Data Cloud opens up the world of machine learning for data engineers, data analysts, and data scientists who are primarily working in a more SQL-centric environment. Not only can you take advantage of all the benefits of the Snowflake Data Cloud but now you can add full ML capabilities (model building and scoring) from the same interface. 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