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Diagnose model performance before deployment for Amazon Fraud Detector

With the growth in adoption of online applications and the rising number of internet users, digital fraud is on the rise year over year. Amazon Fraud Detector provides a fully managed service to help you better identify potentially fraudulent online activities using advanced machine learning (ML) techniques, and more than 20 years of fraud detection…

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With the growth in adoption of online applications and the rising number of internet users, digital fraud is on the rise year over year. Amazon Fraud Detector provides a fully managed service to help you better identify potentially fraudulent online activities using advanced machine learning (ML) techniques, and more than 20 years of fraud detection expertise from Amazon.

To help you catch fraud faster across multiple use cases, Amazon Fraud Detector offers specific models with tailored algorithms, enrichments, and feature transformations. The model training is fully automated and hassle-free, and you can follow the instructions in the user guide or related blog posts to get started. However, with trained models, you need to decide whether the model is ready for deployment. This requires certain knowledge in ML, statistics, and fraud detection, and it may be helpful to know some typical approaches.

This post will help you to diagnose model performance and pick the right model for deployment. We walk through the metrics provided by Amazon Fraud Detector, help you diagnose potential issues, and provide suggestions to improve model performance. The approaches are applicable to both Online Fraud Insights (OFI) and Transaction Fraud Insights (TFI) model templates.

Solution overview

This post provides an end-to-end process to diagnose your model performance. It first introduces all the model metrics shown on the Amazon Fraud Detector console, including AUC, score distribution, confusion matrix, ROC curve, and model variable importance. Then we present a three-step approach to diagnose model performance using different metrics. Finally, we provide suggestions to improve model performance for typical issues.

Prerequisites

Before diving deep into your Amazon Fraud Detector model, you need to complete the following prerequisites:

  1. Create an AWS account.
  2. Create an event dataset for model training.
  3. Upload your data to Amazon Simple Storage Service (Amazon S3) or ingest your event data into Amazon Fraud Detector.
  4. Build an Amazon Fraud Detector model.

Interpret model metrics

After model training is complete, Amazon Fraud Detector evaluates your model using part of the modeling data that wasn’t used in model training. It returns the evaluation metrics on the Model version page for that model. Those metrics reflect the model performance you can expect on real data after deploying to production.

The following screenshot shows example model performance returned by Amazon Fraud Detector. You can choose different thresholds on score distribution (left), and the confusion matrix (right) is updated accordingly.

You can use the following findings to check performance and decide on strategy rules:

  • AUC (area under the curve) – The overall performance of this model. A model with AUC of 0.50 is no better than a coin flip because it represents random chance, whereas a “perfect” model will have a score of 1.0. The higher AUC, the better your model can distinguish between frauds and legitimates.
  • Score distribution – A histogram of model score distributions assuming an example population of 100,000 events. Amazon Fraud Detector generates model scores between 0–1000, where the lower the score, the lower the fraud risk. Better separation between legitimate (green) and fraud (blue) populations typically indicates a better model. For more details, see Model scores.
  • Confusion matrix – A table that describes model performance for the selected given score threshold, including true positive, true negative, false positive, false negative, true positive rate (TPR), and false positive rate (FPR). The count on the table assumes an example population of 100,0000 events. For more details, see Model performance metrics.
  • ROC (Receiver Operator Characteristic) curve – A plot that illustrates the diagnostic ability of the model, as shown in the following screenshot. It plots the true positive rate as a function of false positive rate over all possible model score thresholds. View this chart by choosing Advanced Metrics. If you have trained multiple versions of one model, you can select different FPR thresholds to check the performance change.
  • Model variable importance – The rank of model variables based on their contribution to the generated model, as shown in the following screenshot. The model variable with the highest value is more important to the model than the other model variables in the dataset for that model version, and is listed at the top by default. For more details, see Model variable importance.

Diagnose model performance

Before deploying your model into production, you should use the metrics Amazon Fraud Detector returned to understand the model performance and diagnose the possible issues. The common problems of ML models can be divided into two main categories: data-related issues and model-related issues. Amazon Fraud Detector has taken care of the model-related issues by carefully using validation and testing sets to evaluate and tune your model on the backend. You can complete the following steps to validate if your model is ready for deployment or has possible data-related issues:

  1. Check overall model performance (AUC and score distribution).
  2. Review business requirements (confusion matrix and table).
  3. Check model variable importance.

Check overall model performance: AUC and score distribution

More accurate prediction of future events is always the primary goal of a predictive model. The AUC returned by Amazon Fraud Detector is calculated on a properly sampled test set not used in training. In general, a model with an AUC greater than 0.9 is considered to be a good model.

If you observe a model with performance less than 0.8, it usually means the model has room for improvement (we discuss common issues for low model performance later in this post). Note that the definition of “good” performance highly depends on your business and the baseline model. You can still follow the steps in this post to improve your Amazon Fraud Detector model even though its AUC is greater than 0.8.

On the other hand, if the AUC is over 0.99, it means the model can almost perfectly separate the fraud and legitimate events on the test set. This is sometimes a “too good to be true” scenario (we discuss common issues for very high model performance later in this post).

Besides the overall AUC, the score distribution can also tell you how well the model is fitted. Ideally, you should see the bulk of legitimate and fraud located on the two ends of the scale, which indicates the model score can accurately rank the events on the test set.

In the following example, the score distribution has an AUC of 0.96.

If the legitimate and fraud distribution overlapped or concentrated in the center, it probably means the model doesn’t perform well on distinguishing fraud events from legitimate events, which might indicate historical data distribution changed or that you need more data or features.

The following is an example of score distribution with an AUC of 0.64.

If you can find a split point that can almost perfectly split fraud and legitimate events, there is a high chance that the model has a label leakage issue or the fraud patterns are too easy to detect, which should catch your attention.

In the following example, the score distribution has an AUC of 1.0.

Review business requirements: Confusion matrix and table

Although AUC is a convenient indicator of model performance, it may not directly translate to your business requirement. Amazon Fraud Detector also provides metrics such as fraud capture rate (true positive rate), percentage of legitimate events that are incorrectly predicted as fraud (false positive rate), and more, which are more commonly used as business requirements. After you train a model with a reasonably good AUC, you need to compare the model with your business requirement with those metrics.

The confusion matrix and table provide you with an interface to review the impact and check if it meets your business needs. Note that the numbers depend on the model threshold, where events with scores larger than then threshold are classified as fraud and events with scores lower than the threshold are classified as legit. You can choose which threshold to use depending on your business requirements.

For example, if your goal is to capture 73% of frauds, then (as shown in the example below) you can choose a threshold such as 855, which allows you to capture 73% of all fraud. However, the model will also mis-classify 3% legitimate events to be fraudulent. If this FPR is acceptable for your business, then the model is good for deployment. Otherwise, you need to improve the model performance.

Another example is if the cost for blocking or challenging a legitimate customer is extremely high, then you want a low FPR and high precision. In that case, you can choose a threshold of 950, as shown in the following example, which will miss-classify 1% of legitimate customers as fraud, and 80% of identified fraud will actually be fraudulent.

In addition, you can choose multiple thresholds and assign different outcomes, such as block, investigate, pass. If you can’t find proper thresholds and rules that satisfy all your business requirements, you should consider training your model with more data and attributes.

Check model variable importance

The Model variable importance pane displays how each variable contributes to your model. If one variable has a significantly higher importance value than the others, it might indicate label leakage or that the fraud patterns are too easy to detect. Note that the variable importance is aggregated back to your input variables. If you observe slightly higher importance of IP_ADDRESS, CARD_BIN, EMAIL_ADDRESS, PHONE_NUMBER, BILLING_ZIP, or SHIPPING_ZIP, it might because of the power of enrichment.

The following example shows model variable importance with a potential label leakage using investigation_status.

Model variable importance also gives you hints of what additional variables could potentially bring lift to the model. For example, if you observe low AUC and seller-related features show high importance, you might consider collecting more order features such as SELLER_CATEGORY, SELLER_ADDRESS, and SELLER_ACTIVE_YEARS, and add those variables to your model.

Common issues for low model performance

In this section, we discuss common issues you may encounter regarding low model performance.

Historical data distribution changed

Historical data distribution drift happens when you have a big business change or a data collection issue. For example, if you recently launched your product in a new market, the IP_ADDRESS, EMAIL, and ADDRESS related features could be completely different, and the fraud modus operandi could also change. Amazon Fraud Detector uses EVENT_TIMESTAMP to split data and evaluate your model on the appropriate subset of events in your dataset. If your historical data distribution changes significantly, the evaluation set could be very different from the training data, and the reported model performance could be low.

You can check the potential data distribution change issue by exploring your historical data:

  1. Use the Amazon Fraud Detector Data Profiler tool to check if the fraud rate and the missing rate of the label changed over time.
  2. Check if the variable distribution over time changed significantly, especially for features with high variable importance.
  3. Check the variable distribution over time by target variables. If you observe significantly more fraud events from one category in recent data, you might want to check if the change is reasonable using your business judgments.

If you find the missing rate of the label is very high or the fraud rate consistently dropped during the most recent dates, it might be an indicator of labels not fully matured. You should exclude the most recent data or wait longer to collect the accurate labels, and then retrain your model.

If you observe a sharp spike of fraud rate and variables on specific dates, you might want to double-check if it is an outlier or data collection issue. In that case, you should delete those events and retrain the model.

If you find the outdated data can’t represent your current and future business, you should exclude the old period of data from training. If you’re using stored events in Amazon Fraud Detector, you can simply retrain a new version and select the proper date range while configuring the training job. That may also indicate that the fraud modus operandi in your business changes relatively quickly over time. After model deployment, you may need to re-train your model frequently.

Improper variable type mapping

Amazon Fraud Detector enriches and transforms the data based on the variable types. It’s important that you map your variables to the correct type so that Amazon Fraud Detector model can take the maximum value of your data. For example, if you map IP to the CATEGORICAL type instead of IP_ADDRESS, you don’t get the IP-related enrichments in the backend.

In general, Amazon Fraud Detector suggests the following actions:

  1. Map your variables to specific types, such as IP_ADDRESS, EMAIL_ADDRESS, CARD_BIN, and PHONE_NUMBER, so that Amazon Fraud Detector can extract and enrich additional information.
  2. If you can’t find the specific variable type, map it to one of the three generic types: NUMERIC, CATEGORICAL, or FREE_FORM_TEXT.
  3. If a variable is in text form and has high cardinality, such as a customer review or product description, you should map it to the FREE_FORM_TEXT variable type so that Amazon Fraud Detector extracts text features and embeddings on the backend for you. For example, if you map url_string to FREE_FORM_TEXT, it’s able to tokenize the URL and extract information to feed into the downstream model, which will help it learn more hidden patterns from the URL.

If you find any of your variable types are mapped incorrectly in variable configuration, you can change your variable type and then retrain the model.

Insufficient data or features

Amazon Fraud Detector requires at least 10,000 records to train an Online Fraud Insights (OFI) or Transaction Fraud Insights (TFI) model, with at least 400 of those records identified as fraudulent. TFI also requires that both fraudulent records and legitimate records come from at least 100 different entities each to ensure the diversity of the dataset. Additionally, Amazon Fraud Detector requires the modeling data to have at least two variables. Those are the minimum data requirements to build a useful Amazon Fraud Detector model. However, using more records and variables usually helps the ML models better learn the underlying patterns from your data. When you observe a low AUC or can’t find thresholds that meet your business requirement, you should consider retraining your model with more data or add new features to your model. Usually, we find EMAIL_ADDRESS, IP, PAYMENT_TYPE, BILLING_ADDRESS, SHIPPING_ADDRESS, and DEVICE related variables are important in fraud detection.

Another possible cause is that some of your variables contain too many missing values. To see if that is happening, check the model training messages and refer to Troubleshoot training data issues for suggestions.

Common issues for very high model performance

In this section, we discuss common issues related to very high model performance.

Label leakage

Label leakage occurs when the training datasets use information that would not be expected to be available at prediction time. It overestimates the model’s utility when run in a production environment.

High AUC (close to 1), perfectly separated score distribution, and significantly higher variable importance of one variable could be indicators of potential label leakage issues. You can also check the correlation between the features and the label using the Data Profiler. The Feature and label correlation plot shows the correlation between each feature and the label. If one feature has over 0.99 correlation with the label, you should check if the feature is used properly based on business judgments. For example, to build a risk model to approve or decline a loan application, you shouldn’t use the features like AMOUNT_PAID, because the payments happen after the underwriting process. If a variable isn’t available at the time you make prediction, you should remove that variable from model configuration and retrain a new model.

The following example shows the correlation between each variable and label. investigation_status has a high correlation (close to 1) with the label, so you should double-check if there is a label leakage issue.

Simple fraud patterns

When the fraud patterns in your data are simple, you might also observe very high model performance. For example, suppose all the fraud events in the modeling data come through the same Internal Service Provider; it’s straightforward for the model to pick the IP-related variables and return a “perfect” model with high importance of IP.

Simple fraud patterns don’t always indicate a data issue. It could be true that the fraud modus operandi in your business is easy to capture. However, before making a conclusion, you need to make sure the labels used in model training are accurate, and the modeling data covers as many fraud patterns as possible. For example, if you label your fraud events based on rules, such as labeling all applications from a specific BILLING_ZIP plus PRODUCT_CATEGORY as fraud, the model can easily catch those frauds by simulating the rules and achieving a high AUC.

You can check the label distribution across different categories or bins of each feature using the Data Profiler. For example, if you observe that most fraud events come from one or a few product categories, it might be an indicator of simple fraud patterns, and you need to confirm that it’s not a data collection or process mistake. If the feature is like CUSTOMER_ID, you should exclude the feature in model training.

The following example shows label distribution across different categories of product_category. All fraud comes from two product categories.

Improper data sampling

Improper data sampling may happen when you sampled and only sent part of your data to Amazon Fraud Detector. If the data isn’t sampled properly and isn’t representative of the traffic in production, the reported model performance will be inaccurate and the model could be useless for production prediction. For example, if all fraud events in the modeling data are sampled from Asia and all legit events are sampled from the US, the model might learn to separate fraud and legit based on BILLING_COUNTRY. In that case, the model is not generic to be applied to other populations.

Usually, we suggest sending all the latest events without sampling. Based on the data size and fraud rate, Amazon Fraud Detector does sampling before model training for you. If your data is too large (over 100 GB) and you decide to sample and send only a subset, you should randomly sample your data and make sure the sample is representative of the entire population. For TFI, you should sample your data by entity, which means if one entity is sampled, you should include all its history so that the entity level aggregates are calculated correctly. Note that if you only send a subset of data to Amazon Fraud Detector, the real-time aggregates during inference might be inaccurate if the previous events of the entities aren’t sent.

Another improper data sampling could be only using a short period of data, like one day’s data, to build the model. The data might be biased, especially if your business or fraud attacks have seasonality. We usually recommend including at least two cycles’ (such as 2 weeks or 2 months) worth of data in the modeling to ensure the diversity of fraud types.

Conclusion

After diagnosing and resolving all the potential issues, you should get a useful Amazon Fraud Detector model and be confident about its performance. For the next step, you can create a detector with the model and your business rules, and be ready to deploy it to production for a shadow mode evaluation.

Appendix

How to exclude variables for model training

After the deep dive, you might identify a variable leak target information, and want to exclude it from model training. You can retrain a model version excluding the variables you don’t want by completing the following steps:

  1. On the Amazon Fraud Detector console, in the navigation pane, choose Models.
  2. On the Models page, choose the model you want to retrain.
  3. On the Actions menu, choose Train new version.
  4. Select the date range you want to use and choose Next.
  5. On the Configure training page, deselect the variable you don’t want to use in model training.
  6. Specify your fraud labels and legitimate labels and how you want Amazon Fraud Detector to use unlabeled events, then choose Next.
  7. Review the model configuration and choose Create and train model.

How to change event variable type

Variables represent data elements used in fraud prevention. In Amazon Fraud Detector, all variables are global and are shared across all events and models, which means one variable could be used in multiple events. For example, IP could be associated with sign-in events, and it could also be associated with transaction events. Naturally, Amazon Fraud Detector locked the variable type and data type once a variable is created. To delete an existing variable, you need to first delete all associated event types and models. You can check the resources associated with the specific variable by navigating to Amazon Fraud Detector, choosing Variables in the navigation pane, and choosing the variable name and Associated resources.

Delete the variable and all associated event types

To delete the variable, complete the following steps:

  1. On the Amazon Fraud Detector console, in the navigation pane, choose Variables.
  2. Choose the variable you want to delete.
  3. Choose Associated resources to view a list of all the event types used this variable.
    You need to delete those associated event types before deleting the variable.
  4. Choose the event types in the list to go to the associated event type page.
  5. Choose Stored events to check if any data is stored under this event type.
  6. If there are events stored in Amazon Fraud Detector, choose Delete stored events to delete the stored events.
    When the delete job is complete, the message “The stored events for this event type were successfully deleted” appears.
  7. Choose Associated resources.
    If detectors and models are associated with this event type, you need to delete those resources first.
  8. If detectors are associated, complete the following steps to delete all associated detectors:
    1. Choose the detector to go to the Detector details page.
    2. In the Model versions pane, choose the detector’s version.
    3. On the detector version page, choose Actions.
    4. If the detector version is active, choose Deactivate, choose Deactivate this detector version without replacing it with a different version, and choose Deactivate detector version.
    5. After the detector version is deactivated, choose Actions and then Delete.
    6. Repeat these steps to delete all detector versions.
    7. On the Detector details page, choose Associated rules.
    8. Choose the rule to delete.
    9. Choose Actions and Delete rule version.
    10. Enter the rule name to confirm and choose Delete version.
    11. Repeat these steps to delete all associated rules.
    12. After all detector versions and associated rules are deleted, go to the Detector details page, choose Actions, and choose Delete detector.
    13. Enter the detector’s name and choose Delete detector.
    14. Repeat these steps to delete the next detector.
  9. If any models are associated with the event type, complete the following steps to delete them:
    1. Choose the name of the model.
    2. In the Model versions pane, choose the version.
    3. If the model status is Active, choose Actions and Undeploy model version.
    4. Enter undeploy to confirm and choose Undeploy model version.
      The status changes to Undeploying. The process takes a few minutes to complete.
    5. After the status becomes Ready to deploy, choose Actions and Delete.
    6. Repeat these steps to delete all model versions.
    7. On the Model details page, choose Actions and Delete model.
    8. Enter the name of the model and choose Delete model.
    9. Repeat these steps to delete the next model.
  10. After all associated detectors and models are deleted, choose Actions and Delete event type on the Event details page.
  11. Enter the name of the event type and choose Delete event type.
  12. In the navigation pane, choose Variables, and choose the variable you want to delete.
  13. Repeat the earlier steps to delete all event types associated with the variable.
  14. On the Variable details page, choose Actions and Delete.
  15. Enter the name of the variable and choose Delete variable.

Create a new variable with the correct variable type

After you have deleted the variable and all associated event types, stored events, models, and detectors from Amazon Fraud Detector, you can create a new variable of the same name and map it to the correct variable type.

  1. On the Amazon Fraud Detector console, in the navigation pane, choose Variables.
  2. Choose Create.
  3. Enter the variable name you want to modify (the one you deleted earlier).
  4. Select the correct variable type you want to change to.
  5. Choose Create variable.

Upload data and retrain the model

After you update the variable type, you can upload the data again and train a new model. For instructions, refer to Detect online transaction fraud with new Amazon Fraud Detector features.

How to add new variables to an existing event type

To add new variables to the existing event type, complete the following steps:

  1. Add the new variables to the previous training CVS file.
  2. Upload the new training data file to an S3 bucket. Note the Amazon S3 location of your training file (for example, s3://bucketname/path/to/some/object.csv) and your role name.
  3. On the Amazon Fraud Detector console, in the navigation pane, choose Events.
  4. On the Event types page, choose the name of the event type you want to add variables.
  5. On the Event type details page, choose Actions, then Add variables.
  6. Under Choose how to define this event’s variables, choose Select variables from a training dataset.
  7. For IAM role, select an existing IAM role or create a new role to access data in Amazon S3.
  8. For Data location, enter the S3 location of the new training file and choose Upload.
    The new variables not present in the existing event type should show up in the list.
  9. Choose Add variables.

Now, the new variables have been added to the existing event type. If you’re using stored events in Amazon Fraud Detector, the new variables of the stored events are still missing. You need to import the training data with the new variables to Amazon Fraud Detector and then retrain a new model version. When uploading the new training data with the same EVENT_ID and EVENT_TIMESTAMP, the new event variables overwrite the previous event variables stored in Amazon Fraud Detector.

About the Authors

Julia Xu is a Research Scientist with Amazon Fraud Detector. She is passionate about solving customer challenges using Machine Learning techniques. In her free time, she enjoys hiking, painting, and exploring new coffee shops.

Hao Zhou is a Research Scientist with Amazon Fraud Detector. He holds a PhD in electrical engineering from Northwestern University, USA. He is passionate about applying machine learning techniques to combat fraud and abuse.

Abhishek Ravi is a Senior Product Manager with Amazon Fraud Detector. He is passionate about leveraging technical capabilities to build products that delight customers.



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Build a GNN-based real-time fraud detection solution using Amazon SageMaker, Amazon Neptune, and the Deep Graph Library

Fraudulent activities severely impact many industries, such as e-commerce, social media, and financial services. Frauds could cause a significant loss for businesses and consumers. American consumers reported losing more than $5.8 billion to frauds in 2021, up more than 70% over 2020. Many techniques have been used to detect fraudsters—rule-based filters, anomaly detection, and machine…

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Fraudulent activities severely impact many industries, such as e-commerce, social media, and financial services. Frauds could cause a significant loss for businesses and consumers. American consumers reported losing more than $5.8 billion to frauds in 2021, up more than 70% over 2020. Many techniques have been used to detect fraudsters—rule-based filters, anomaly detection, and machine learning (ML) models, to name a few.

In real-world data, entities often involve rich relationships with other entities. Such a graph structure can provide valuable information for anomaly detection. For example, in the following figure, users are connected via shared entities such as Wi-Fi IDs, physical locations, and phone numbers. Due to the large number of unique values of these entities, like phone numbers, it’s difficult to use them in the traditional feature-based models—for example, one-hot encoding all phone numbers wouldn’t be viable. But such relationships could help predict whether a user is a fraudster. If a user has shared several entities with a known fraudster, the user is more likely a fraudster.

Recently, graph neural network (GNN) has become a popular method for fraud detection. GNN models can combine both graph structure and attributes of nodes or edges, such as users or transactions, to learn meaningful representations to distinguish malicious users and events from legitimate ones. This capability is crucial for detecting frauds where fraudsters collude to hide their abnormal features but leave some traces of relations.

Current GNN solutions mainly rely on offline batch training and inference mode, which detect fraudsters after malicious events have happened and losses have occurred. However, catching fraudulent users and activities in real time is crucial for preventing losses. This is particularly true in business cases where there is only one chance to prevent fraudulent activities. For example, in some e-commerce platforms, account registration is wide open. Fraudsters can behave maliciously just once with an account and never use the same account again.

Predicting fraudsters in real time is important. Building such a solution, however, is challenging. Because GNNs are still new to the industry, there are limited online resources on converting GNN models from batch serving to real-time serving. Additionally, it’s challenging to construct a streaming data pipeline that can feed incoming events to a GNN real-time serving API. To the best of the authors’ knowledge, no reference architectures and examples are available for GNN-based real-time inference solutions as of this writing.

To help developers apply GNNs to real-time fraud detection, this post shows how to use Amazon Neptune, Amazon SageMaker, and the Deep Graph Library (DGL), among other AWS services, to construct an end-to-end solution for real-time fraud detection using GNN models.

We focus on four tasks:

  • Processing a tabular transaction dataset into a heterogeneous graph dataset
  • Training a GNN model using SageMaker
  • Deploying the trained GNN models as a SageMaker endpoint
  • Demonstrating real-time inference for incoming transactions

This post extends the previous work in Detecting fraud in heterogeneous networks using Amazon SageMaker and Deep Graph Library, which focuses on the first two tasks. You can refer to that post for more details on heterogeneous graphs, GNNs, and semi-supervised training of GNNs.

Businesses looking for a fully-managed AWS AI service for fraud detection can also use Amazon Fraud Detector, which makes it easy to identify potentially fraudulent online activities, such as the creation of fake accounts or online payment fraud.

Solution overview

This solution contains two major parts.

The first part is a pipeline that processes the data, trains GNN models, and deploys the trained models. It uses AWS Glue to process the transaction data, and saves the processed data to both Amazon Neptune and Amazon Simple Storage Service (Amazon S3). Then, a SageMaker training job is triggered to train a GNN model on the data saved in Amazon S3 to predict whether a transaction is fraudulent. The trained model along with other assets are saved back to Amazon S3 upon the completion of the training job. Finally, the saved model is deployed as a SageMaker endpoint. The pipeline is orchestrated by AWS Step Functions, as shown in the following figure.

The second part of the solution implements real-time fraudulent transaction detection. It starts from a RESTful API that queries the graph database in Neptune to extract the subgraph related to an incoming transaction. It also has a web portal that can simulate business activities, generating online transactions with both fraudulent and legitimate ones. The web portal provides a live visualization of the fraud detection. This part uses Amazon CloudFront, AWS Amplify, AWS AppSync, Amazon API Gateway, Step Functions, and Amazon DocumentDB to rapidly build the web application. The following diagram illustrates the real-time inference process and web portal.

The implementation of this solution, along with an AWS CloudFormation template that can launch the architecture in your AWS account, is publicly available through the following GitHub repo.

Data processing

In this section, we briefly describe how to process an example dataset and convert it from raw tables into a graph with relations identified among different columns.

This solution uses the same dataset, the IEEE-CIS fraud dataset, as the previous post Detecting fraud in heterogeneous networks using Amazon SageMaker and Deep Graph Library. Therefore, the basic principle of the data process is the same. In brief, the fraud dataset includes a transactions table and an identities table, having nearly 500,000 anonymized transaction records along with contextual information (for example, devices used in transactions). Some transactions have a binary label, indicating whether a transaction is fraudulent. Our task is to predict which unlabeled transactions are fraudulent and which are legitimate.

The following figure illustrates the general process of how to convert the IEEE tables into a heterogeneous graph. We first extract two columns from each table. One column is always the transaction ID column, where we set each unique TransactionID as one node. Another column is picked from the categorical columns, such as the ProductCD and id_03 columns, where each unique category was set as a node. If a TransactionID and a unique category appear in the same row, we connect them with one edge. This way, we convert two columns in a table into one bipartite. Then we combine those bipartites along with the TransactionID nodes, where the same TransactionID nodes are merged into one unique node. After this step, we have a heterogeneous graph built from bipartites.

For the rest of the columns that aren’t used to build the graph, we join them together as the feature of the TransactionID nodes. TransactionID values that have the isFraud values are used as the label for model training. Based on this heterogeneous graph, our task becomes a node classification task of the TransactionID nodes. For more details on preparing the graph data for training GNNs, refer to the Feature extraction and Constructing the graph sections of the previous blog post.

The code used in this solution is available in src/scripts/glue-etl.py. You can also experiment with data processing through the Jupyter notebook src/sagemaker/01.FD_SL_Process_IEEE-CIS_Dataset.ipynb.

Instead of manually processing the data, as done in the previous post, this solution uses a fully automatic pipeline orchestrated by Step Functions and AWS Glue that supports processing huge datasets in parallel via Apache Spark. The Step Functions workflow is written in AWS Cloud Development Kit (AWS CDK). The following is a code snippet to create this workflow:

import { LambdaInvoke, GlueStartJobRun } from ‘aws-cdk-lib/aws-stepfunctions-tasks’; const parametersNormalizeTask = new LambdaInvoke(this, ‘Parameters normalize’, { lambdaFunction: parametersNormalizeFn, integrationPattern: IntegrationPattern.REQUEST_RESPONSE, }); … const dataProcessTask = new GlueStartJobRun(this, ‘Data Process’, { integrationPattern: IntegrationPattern.RUN_JOB, glueJobName: etlConstruct.jobName, timeout: Duration.hours(5), resultPath: ‘$.dataProcessOutput’, }); … const definition = parametersNormalizeTask .next(dataIngestTask) .next(dataCatalogCrawlerTask) .next(dataProcessTask) .next(hyperParaTask) .next(trainingJobTask) .next(runLoadGraphDataTask) .next(modelRepackagingTask) .next(createModelTask) .next(createEndpointConfigTask) .next(checkEndpointTask) .next(endpointChoice);

Besides constructing the graph data for GNN model training, this workflow also batch loads the graph data into Neptune to conduct real-time inference later on. This batch data loading process is demonstrated in the following code snippet:

from neptune_python_utils.endpoints import Endpoints from neptune_python_utils.bulkload import BulkLoad … bulkload = BulkLoad( source=targetDataPath, endpoints=endpoints, role=args.neptune_iam_role_arn, region=args.region, update_single_cardinality_properties=True, fail_on_error=True) load_status = bulkload.load_async() status, json = load_status.status(details=True, errors=True) load_status.wait()

GNN model training

After the graph data for model training is saved in Amazon S3, a SageMaker training job, which is only charged when the training job is running, is triggered to start the GNN model training process in the Bring Your Own Container (BYOC) mode. It allows you to pack your model training scripts and dependencies in a Docker image, which it uses to create SageMaker training instances. The BYOC method could save significant effort in setting up the training environment. In src/sagemaker/02.FD_SL_Build_Training_Container_Test_Local.ipynb, you can find details of the GNN model training.

Docker image

The first part of the Jupyter notebook file is the training Docker image generation (see the following code snippet):

*!* aws ecr get-login-password –region us-east-1 | docker login –username AWS –password-stdin 763104351884.dkr.ecr.us-east-1.amazonaws.com image_name *=* ‘fraud-detection-with-gnn-on-dgl/training’ *!* docker build -t $image_name ./FD_SL_DGL/gnn_fraud_detection_dgl

We used a PyTorch-based image for the model training. The Deep Graph Library (DGL) and other dependencies are installed when building the Docker image. The GNN model code in the src/sagemaker/FD_SL_DGL/gnn_fraud_detection_dgl folder is copied to the image as well.

Because we process the transaction data into a heterogeneous graph, in this solution we choose the Relational Graph Convolutional Network (RGCN) model, which is specifically designed for heterogeneous graphs. Our RGCN model can train learnable embeddings for the nodes in heterogeneous graphs. Then, the learned embeddings are used as inputs of a fully connected layer for predicting the node labels.

Hyperparameters

To train the GNN, we need to define a few hyperparameters before the training process, such as the file names of the graph constructed, the number of layers of GNN models, the training epochs, the optimizer, the optimization parameters, and more. See the following code for a subset of the configurations:

edges *=* “,”*.*join(map(*lambda* x: x*.*split(“/”)[*-*1], [file *for* file *in* processed_files *if* “relation” *in* file])) params *=* {‘nodes’ : ‘features.csv’, ‘edges’: edges, ‘labels’: ‘tags.csv’, ’embedding-size’: 64, ‘n-layers’: 2, ‘n-epochs’: 10, ‘optimizer’: ‘adam’, ‘lr’: 1e-2}

For more information about all the hyperparameters and their default values, see estimator_fns.py in the src/sagemaker/FD_SL_DGL/gnn_fraud_detection_dgl folder.

Model training with SageMaker

After the customized container Docker image is built, we use the preprocessed data to train our GNN model with the hyperparameters we defined. The training job uses the DGL, with PyTorch as the backend deep learning framework, to construct and train the GNN. SageMaker makes it easy to train GNN models with the customized Docker image, which is an input argument of the SageMaker estimator. For more information about training GNNs with the DGL on SageMaker, see Train a Deep Graph Network.

The SageMaker Python SDK uses Estimator to encapsulate training on SageMaker, which runs SageMaker-compatible custom Docker containers, enabling you to run your own ML algorithms by using the SageMaker Python SDK. The following code snippet demonstrates training the model with SageMaker (either in a local environment or cloud instances):

from sagemaker.estimator import Estimator from time import strftime, gmtime from sagemaker.local import LocalSession localSageMakerSession = LocalSession(boto_session=boto3.session.Session(region_name=current_region)) estimator = Estimator(image_uri=image_name, role=sagemaker_exec_role, instance_count=1, instance_type=’local’, hyperparameters=params, output_path=output_path, sagemaker_session=localSageMakerSession) training_job_name = “{}-{}”.format(‘GNN-FD-SL-DGL-Train’, strftime(“%Y-%m-%d-%H-%M-%S”, gmtime())) print(training_job_name) estimator.fit({‘train’: processed_data}, job_name=training_job_name)

After training, the GNN model’s performance on the test set is displayed like the following outputs. The RGCN model normally can achieve around 0.87 AUC and more than 95% accuracy. For a comparison of the RGCN model with other ML models, refer to the Results section of the previous blog post for more details.

Epoch 00099 | Time(s) 7.9413 | Loss 0.1023 | f1 0.3745 Metrics Confusion Matrix: labels positive labels negative predicted positive 4343 576 predicted negative 13494 454019 f1: 0.3817, precision: 0.8829, recall: 0.2435, acc: 0.9702, roc: 0.8704, pr: 0.4782, ap: 0.4782 Finished Model training

Upon the completion of model training, SageMaker packs the trained model along with other assets, including the trained node embeddings, into a ZIP file and then uploads it to a specified S3 location. Next, we discuss the deployment of the trained model for real-time fraudulent detection.

GNN model deployment

SageMaker makes the deployment of trained ML models simple. In this stage, we use the SageMaker PyTorchModel class to deploy the trained model, because our DGL model depends on PyTorch as the backend framework. You can find the deployment code in the src/sagemaker/03.FD_SL_Endpoint_Deployment.ipynb file.

Besides the trained model file and assets, SageMaker requires an entry point file for the deployment of a customized model. The entry point file is run and stored in the memory of an inference endpoint instance to respond to the inference request. In our case, the entry point file is the fd_sl_deployment_entry_point.py file in the src/sagemaker/FD_SL_DGL/code folder, which performs four major functions:

  • Receive requests and parse contents of requests to obtain the to-be-predicted nodes and their associated data
  • Convert the data to a DGL heterogeneous graph as input for the RGCN model
  • Perform the real-time inference via the trained RGCN model
  • Return the prediction results to the requester

Following SageMaker conventions, the first two functions are implemented in the input_fn method. See the following code (for simplicity, we delete some commentary code):

def input_fn(request_body, request_content_type=’application/json’): # ——————— receive request ———————————————— # input_data = json.loads(request_body) subgraph_dict = input_data[‘graph’] n_feats = input_data[‘n_feats’] target_id = input_data[‘target_id’] graph, new_n_feats, new_pred_target_id = recreate_graph_data(subgraph_dict, n_feats, target_id) return (graph, new_n_feats, new_pred_target_id)

The constructed DGL graph and features are then passed to the predict_fn method to fulfill the third function. predict_fn takes two input arguments: the outputs of input_fn and the trained model. See the following code:

def predict_fn(input_data, model): # ——————— Inference ———————————————— # graph, new_n_feats, new_pred_target_id = input_data with th.no_grad(): logits = model(graph, new_n_feats) res = logits[new_pred_target_id].cpu().detach().numpy() return res[1]

The model used in perdict_fn is created by the model_fn method when the endpoint is called the first time. The function model_fn loads the saved model file and associated assets from the model_dir argument and the SageMaker model folder. See the following code:

def model_fn(model_dir): # —————— Loading model ——————- ntype_dict, etypes, in_size, hidden_size, out_size, n_layers, embedding_size = initialize_arguments(os.path.join(BASE_PATH, ‘metadata.pkl’)) rgcn_model = HeteroRGCN(ntype_dict, etypes, in_size, hidden_size, out_size, n_layers, embedding_size) stat_dict = th.load(‘model.pth’) rgcn_model.load_state_dict(stat_dict) return rgcn_model

The output of the predict_fn method is a list of two numbers, indicating the logits for class 0 and class 1, where 0 means legitimate and 1 means fraudulent. SageMaker takes this list and passes it to an inner method called output_fn to complete the final function.

To deploy our GNN model, we first wrap the GNN model into a SageMaker PyTorchModel class with the entry point file and other parameters (the path of the saved ZIP file, the PyTorch framework version, the Python version, and so on). Then we call its deploy method with instance settings. See the following code:

env = { ‘SAGEMAKER_MODEL_SERVER_WORKERS’: ‘1’ } print(f’Use model {repackged_model_path}’) sagemakerSession = sm.session.Session(boto3.session.Session(region_name=current_region)) fd_sl_model = PyTorchModel(model_data=repackged_model_path, role=sagemaker_exec_role, entry_point=’./FD_SL_DGL/code/fd_sl_deployment_entry_point.py’, framework_version=’1.6.0′, py_version=’py3′, predictor_cls=JSONPredictor, env=env, sagemaker_session=sagemakerSession) fd_sl_predictor *=* fd_sl_model*.*deploy(instance_type*=*’ml.c5.4xlarge’, initial_instance_count*=*1,)

The preceding procedures and code snippets demonstrate how to deploy your GNN model as an online inference endpoint from a Jupyter notebook. However, for production, we recommend using the previously mentioned MLOps pipeline orchestrated by Step Functions for the entire workflow, including processing data, training the model, and deploying an inference endpoint. The entire pipeline is implemented by an AWS CDK application, which can be easily replicated in different Regions and accounts.

Real-time inference

When a new transaction arrives, to perform real-time prediction, we need to complete four steps:

  1. Node and edge insertion – Extract the transaction’s information such as the TransactionID and ProductCD as nodes and edges, and insert the new nodes into the existing graph data stored at the Neptune database.
  2. Subgraph extraction – Set the to-be-predicted transaction node as the center node, and extract a n-hop subgraph according to the GNN model’s input requirements.
  3. Feature extraction – For the nodes and edges in the subgraph, extract their associated features.
  4. Call the inference endpoint – Pack the subgraph and features into the contents of a request, then send the request to the inference endpoint.

In this solution, we implement a RESTful API to achieve real-time fraudulent predication described in the preceding steps. See the following pseudo-code for real-time predictions. The full implementation is in the complete source code file.

For prediction in real time, the first three steps require lower latency. Therefore, a graph database is an optimal choice for these tasks, particularly for the subgraph extraction, which could be achieved efficiently with graph database queries. The underline functions that support the pseudo-code are based on Neptune’s gremlin queries.

def handler(event, context): graph_input = GraphModelClient(endpoints) # Step 1: node and edge insertion trans_dict, identity_dict, target_id, transaction_value_cols, union_li_cols = load_data_from_event(event, transactions_id_cols, transactions_cat_cols, dummied_col) graph_input.insert_new_transaction_vertex_and_edge(trans_dict, identity_dict , target_id, vertex_type = ‘Transaction’) # Setp 2: subgraph extraction subgraph_dict, transaction_embed_value_dict = graph_input.query_target_subgraph(target_id, trans_dict, transaction_value_cols, union_li_cols, dummied_col) # Step 3 & 4: feature extraction & call the inference endpoint transaction_id = int(target_id[(target_id.find(‘-‘)+1):]) pred_prob = invoke_endpoint_with_idx(endpointname = ENDPOINT_NAME, target_id = transaction_id, subgraph_dict = subgraph_dict, n_feats = transaction_embed_value_dict) function_res = { ‘id’: event[‘transaction_data’][0][‘TransactionID’], ‘flag’: pred_prob > MODEL_BTW, ‘pred_prob’: pred_prob } return function_res

One caveat about real-time fraud detection using GNNs is the GNN inference mode. To fulfill real-time inference, we need to convert the GNN model inference from transductive mode to inductive mode. GNN models in transductive inference mode can’t make predictions for newly appeared nodes and edges, whereas in inductive mode, GNN models can handle new nodes and edges. A demonstration of the difference between transductive and inductive mode is shown in the following figure.

In transductive mode, predicted nodes and edges coexist with labeled nodes and edges during training. Models identify them before inference, and they could be inferred in training. Models in inductive mode are trained on the training graph but need to predict unseen nodes (those in red dotted circles on the right) with their associated neighbors, which might be new nodes, like the gray triangle node on the right.

Our RGCN model is trained and tested in transductive mode. It has access to all nodes in training, and also trained an embedding for each featureless node, such as IP address and card types. In the testing stage, the RGCN model uses these embeddings as node features to predict nodes in the test set. When we do real-time inference, however, some of the newly added featureless nodes have no such embeddings because they’re not in the training graph. One way to tackle this issue is to assign the mean of all embeddings in the same node type to the new nodes. In this solution, we adopt this method.

In addition, this solution provides a web portal (as seen in the following screenshot) to demonstrate real-time fraudulent predictions from business operators’ perspectives. It can generate the simulated online transactions, and provide a live visualization of detected fraudulent transaction information.

Clean up

When you’re finished exploring the solution, you can clean the resources to avoid incurring charges.

Conclusion

In this post, we showed how to build a GNN-based real-time fraud detection solution using SageMaker, Neptune, and the DGL. This solution has three major advantages:

  • It has good performance in terms of prediction accuracy and AUC metrics
  • It can perform real-time inference via a streaming MLOps pipeline and SageMaker endpoints
  • It automates the total deployment process with the provided CloudFormation template so that interested developers can easily test this solution with custom data in their account

For more details about the solution, see the GitHub repo.

After you deploy this solution, we recommend customizing the data processing code to fit your own data format and modify the real-time inference mechanism while keeping the GNN model unchanged. Note that we split the real-time inference into four steps without further optimization of the latency. These four steps take a few seconds to get a prediction on the demo dataset. We believe that optimizing the Neptune graph data schema design and queries for subgraph and feature extraction can significantly reduce the inference latency.

About the authors

Jian Zhang is an applied scientist who has been using machine learning techniques to help customers solve various problems, such as fraud detection, decoration image generation, and more. He has successfully developed graph-based machine learning, particularly graph neural network, solutions for customers in China, USA, and Singapore. As an enlightener of AWS’s graph capabilities, Zhang has given many public presentations about the GNN, the Deep Graph Library (DGL), Amazon Neptune, and other AWS services.

Mengxin Zhu is a manager of Solutions Architects at AWS, with a focus on designing and developing reusable AWS solutions. He has been engaged in software development for many years and has been responsible for several startup teams of various sizes. He also is an advocate of open-source software and was an Eclipse Committer.

Haozhu Wang is a research scientist at Amazon ML Solutions Lab, where he co-leads the Reinforcement Learning Vertical. He helps customers build advanced machine learning solutions with the latest research on graph learning, natural language processing, reinforcement learning, and AutoML. Haozhu received his PhD in Electrical and Computer Engineering from the University of Michigan.



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New – AWS Private 5G – Build Your Own Private Mobile Network

Back in the mid-1990’s, I had a young family and 5 or 6 PCs in the basement. One day my son Stephen and I bought a single box that contained a bunch of 3COM network cards, a hub, some drivers, and some cables, and spent a pleasant weekend setting up our first home LAN. Introducing…

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Back in the mid-1990’s, I had a young family and 5 or 6 PCs in the basement. One day my son Stephen and I bought a single box that contained a bunch of 3COM network cards, a hub, some drivers, and some cables, and spent a pleasant weekend setting up our first home LAN.

Introducing AWS Private 5G
Today I would like to introduce you to AWS Private 5G, the modern, corporate version of that very powerful box of hardware and software. This cool new service lets you design and deploy your own private mobile network in a matter of days. It is easy to install, operate, and scale, and does not require any specialized expertise. You can use the network to communicate with the sensors & actuators in your smart factory, or to provide better connectivity for handheld devices, scanners, and tablets for process automation.

The private mobile network makes use of CBRS spectrum. It supports 4G LTE (Long Term Evolution) today, and will support 5G in the future, both of which give you a consistent, predictable level of throughput with ultra low latency. You get long range coverage, indoors and out, and fine-grained access control.

AWS Private 5G runs on AWS-managed infrastructure. It is self-service and API-driven, and can scale with respect to geographic coverage, device count, and overall throughput. It also works nicely with other parts of AWS, and lets you use AWS Identity and Access Management (IAM) to control access to both devices and applications.

Getting Started with AWS Private 5G
To get started, I visit the AWS Private 5G Console and click Create network:

I assign a name to my network (JeffCell) and to my site (JeffSite) and click Create network:

The network and the site are created right away. Now I click Create order:

I fill in the shipping address, agree to the pricing (more on that later), and click Create order:

Then I await delivery, and click Acknowledge order to proceed:

The package includes a radio unit and ten SIM cards. The radio unit requires AC power and wired access to the public Internet, along with basic networking (IPv4 and DHCP).

When the order arrives, I click Acknowledge order and confirm that I have received the desired radio unit and SIMs. Then I engage a Certified Professional Installer (CPI) to set it up. As part of the installation process, the installer will enter the latitude, longitude, and elevation of my site.

Things to Know
Here are a couple of important things to know about AWS Private 5G:

Partners – Planning and deploying a private wireless network can be complex and not every enterprise will have the tools to do this work on their own. In addition, CBRS spectrum in the United States requires Certified Professional Installation (CPI) of radios. To address these needs, we are building an ecosystem of partners that can provide customers with radio planning, installation, CPI certification, and implementation of customer use cases. You can access these partners from the AWS Private 5G Console and work with them through the AWS Marketplace.

Deployment Options – In the demo above, I showed you the cloud–based deployment option, which is designed for testing and evaluation purposes, for time-limited deployments, and for deployments that do not use the network in latency-sensitive ways. With this option, the AWS Private 5G Mobile Core runs within a specific AWS Region. We are also working to enable on-premises hosting of the Mobile Core on a Private 5G compute appliance.

CLI and API Access – I can also use the create-network, create-network-site, and acknowledge-order-receipt commands to set up my AWS Private 5G network from the command line. I still need to use the console to place my equipment order.

Scaling and Expansion – Each network supports one radio unit that can provide up to 150 Mbps of throughput spread across up to 100 SIMs. We are working to add support for multiple radio units and greater number of SIM cards per network.

Regions and Locations – We are launching AWS Private 5G in the US East (Ohio), US East (N. Virginia), and US West (Oregon) Regions, and are working to make the service available outside of the United States in the near future.

Pricing – Each radio unit is billed at $10 per hour, with a 60 day minimum.

To learn more, read about AWS Private 5G.

Jeff;



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Build an air quality anomaly detector using Amazon Lookout for Metrics

Today, air pollution is a familiar environmental issue that creates severe respiratory and heart conditions, which pose serious health threats. Acid rain, depletion of the ozone layer, and global warming are also adverse consequences of air pollution. There is a need for intelligent monitoring and automation in order to prevent severe health issues and in…

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Today, air pollution is a familiar environmental issue that creates severe respiratory and heart conditions, which pose serious health threats. Acid rain, depletion of the ozone layer, and global warming are also adverse consequences of air pollution. There is a need for intelligent monitoring and automation in order to prevent severe health issues and in extreme cases life-threatening situations. Air quality is measured using the concentration of pollutants in the air. Identifying symptoms early and controlling the pollutant level before it’s dangerous is crucial. The process of identifying the air quality and the anomaly in the weight of pollutants, and quickly diagnosing the root cause, is difficult, costly, and error-prone.

The process of applying AI and machine learning (ML)-based solutions to find data anomalies involves a lot of complexity in ingesting, curating, and preparing data in the right format and then optimizing and maintaining the effectiveness of these ML models over long periods of time. This has been one of the barriers to quickly implementing and scaling the adoption of ML capabilities.

This post shows you how to use an integrated solution with Amazon Lookout for Metrics and Amazon Kinesis Data Firehose to break these barriers by quickly and easily ingesting streaming data, and subsequently detecting anomalies in the key performance indicators of your interest.

Lookout for Metrics automatically detects and diagnoses anomalies (outliers from the norm) in business and operational data. It’s a fully managed ML service that uses specialized ML models to detect anomalies based on the characteristics of your data. For example, trends and seasonality are two characteristics of time series metrics in which threshold-based anomaly detection doesn’t work. Trends are continuous variations (increases or decreases) in a metric’s value. On the other hand, seasonality is periodic patterns that occur in a system, usually rising above a baseline and then decreasing again. You don’t need ML experience to use Lookout for Metrics.

We demonstrate a common air quality monitoring scenario, in which we detect anomalies in the pollutant concentration in the air. By the end of this post, you’ll learn how to use these managed services from AWS to help prevent health issues and global warming. You can apply this solution to other use cases for better environment management, such as detecting anomalies in water quality, land quality, and power consumption patterns, to name a few.

Solution overview

The architecture consists of three functional blocks:

  • Wireless sensors placed at strategic locations to sense the concentration level of carbon monoxide (CO), sulfur dioxide (SO2), and nitrogen dioxide(NO2) in the air
  • Streaming data ingestion and storage
  • Anomaly detection and notification

The solution provides a fully automated data path from the sensors all the way to a notification being raised to the user. You can also interact with the solution using the Lookout for Metrics UI in order to analyze the identified anomalies.

The following diagram illustrates our solution architecture.

Prerequisites

You need the following prerequisites before you can proceed with solution. For this post, we use the us-east-1 Region.

  1. Download the Python script (publish.py) and data file from the GitHub repo.
  2. Open the live_data.csv file in your preferred editor and replace the dates to be today’s and tomorrow’s date. For example, if today’s date is July 8, 2022, then replace 2022-03-25 with 2022-07-08. Keep the format the same. This is required to simulate sensor data for the current date using the IoT simulator script.
  3. Create an Amazon Simple Storage Service (Amazon S3) bucket and a folder named air-quality. Create a subfolder inside air-quality named historical. For instructions, see Creating a folder.
  4. Upload the live_data.csv file in the root S3 bucket and historical_data.json in the historical folder.
  5. Create an AWS Cloud9 development environment, which we use to run the Python simulator program to create sensor data for this solution.

Ingest and transform data using AWS IoT Core and Kinesis Data Firehose

We use a Kinesis Data Firehose delivery stream to ingest the streaming data from AWS IoT Core and deliver it to Amazon S3. Complete the following steps:

  1. On the Kinesis Data Firehose console, choose Create delivery stream.
  2. For Source, choose Direct PUT.
  3. For Destination, choose Amazon S3.
  4. For Delivery stream name, enter a name for your delivery stream.
  5. For S3 bucket, enter the bucket you created as a prerequisite.
  6. Enter values for S3 bucket prefix and S3 bucket error output prefix.One of the key points to note is the configuration of the custom prefix that is configured for the Amazon S3 destination. This prefix pattern makes sure that the data is created in the S3 bucket as per the prefix hierarchy expected by Lookout for Metrics. (More on this later in this post.) For more information about custom prefixes, see Custom Prefixes for Amazon S3 Objects.
  7. For Buffer interval, enter 60.
  8. Choose Create or update IAM role.
  9. Choose Create delivery stream.

    Now we configure AWS IoT Core and run the air quality simulator program.
  10. On the AWS IoT Core console, create an AWS IoT policy called admin.
  11. In the navigation pane under Message Routing, choose Rules.
  12. Choose Create rule.
  13. Create a rule with the Kinesis Data Firehose(firehose) action.
    This sends data from an MQTT message to a Kinesis Data Firehose delivery stream.
  14. Choose Create.
  15. Create an AWS IoT thing with name Test-Thing and attach the policy you created.
  16. Download the certificate, public key, private key, device certificate, and root CA for AWS IoT Core.
  17. Save each of the downloaded files to the certificates subdirectory that you created earlier.
  18. Upload publish.py to the iot-test-publish folder.
  19. On the AWS IoT Core console, in the navigation pane, choose Settings.
  20. Under Custom endpoint, copy the endpoint.
    This AWS IoT Core custom endpoint URL is personal to your AWS account and Region.
  21. Replace customEndpointUrl with your AWS IoT Core custom endpoint URL, certificates with the name of certificate, and Your_S3_Bucket_Name with your S3 bucket name.
    Next, you install pip and the AWS IoT SDK for Python.
  22. Log in to AWS Cloud9 and create a working directory in your development environment. For example: aq-iot-publish.
  23. Create a subdirectory for certificates in your new working directory. For example: certificates.
  24. Install the AWS IoT SDK for Python v2 by running the following from the command line.
  25. To test the data pipeline, run the following command:

You can see the payload in the following screenshot.

Finally, the data is delivered to the specified S3 bucket in the prefix structure.

The data of the files is as follows:

  • {“TIMESTAMP”:”2022-03-20 00:00″,”LOCATION_ID”:”B-101″,”CO”:2.6,”SO2″:62,”NO2″:57}
  • {“TIMESTAMP”:”2022-03-20 00:05″,”LOCATION_ID”:”B-101″,”CO”:3.9,”SO2″:60,”NO2″:73}

The timestamps show that each file contains data for 5-minute intervals.

With minimal code, we have now ingested the sensor data, created an input stream from the ingested data, and stored the data in an S3 bucket based on the requirements for Lookout for Metrics.

In the following sections, we take a deeper look at the constructs within Lookout for Metrics, and how easy it is to configure these concepts using the Lookout for Metrics console.

Create a detector

A detector is a Lookout for Metrics resource that monitors a dataset and identifies anomalies at a predefined frequency. Detectors use ML to find patterns in data and distinguish between expected variations in data and legitimate anomalies. To improve its performance, a detector learns more about your data over time.

In our use case, the detector analyzes data from the sensor every 5 minutes.

To create the detector, navigate to the Lookout for Metrics console and choose Create detector. Provide the name and description (optional) for the detector, along with the interval of 5 minutes.

Your data is encrypted by default with a key that AWS owns and manages for you. You can also configure if you want to use a different encryption key from the one that is used by default.

Now let’s point this detector to the data that you want it to run anomaly detection on.

Create a dataset

A dataset tells the detector where to find your data and which metrics to analyze for anomalies. To create a dataset, complete the following steps:

  1. On the Amazon Lookout for Metrics console, navigate to your detector.
  2. Choose Add a dataset.
  3. For Name, enter a name (for example, air-quality-dataset).
  4. For Datasource, choose your data source (for this post, Amazon S3).
  5. For Detector mode, select your mode (for this post, Continuous).

With Amazon S3, you can create a detector in two modes:

    • Backtest – This mode is used to find anomalies in historical data. It needs all records to be consolidated in a single file.
    • Continuous – This mode is used to detect anomalies in live data. We use this mode with our use case because we want to detect anomalies as we receive air pollutant data from the air monitoring sensor.
  1. Enter the S3 path for the live S3 folder and path pattern.
  2. For Datasource interval, choose 5 minute intervals.If you have historical data from which the detector can learn patterns, you can provide it during this configuration. The data is expected to be in the same format that you use to perform a backtest. Providing historical data speeds up the ML model training process. If this isn’t available, the continuous detector waits for sufficient data to be available before making inferences.
  3. For this post, we already have historical data, so select Use historical data.
  4. Enter the S3 path of historical_data.json.
  5. For File format, select JSON lines.

At this point, Lookout for Metrics accesses the data source and validates whether it can parse the data. If the parsing is successful, it gives you a “Validation successful” message and takes you to the next page, where you configure measures, dimensions, and timestamps.

Configure measures, dimensions, and timestamps

Measures define KPIs that you want to track anomalies for. You can add up to five measures per detector. The fields that are used to create KPIs from your source data must be of numeric format. The KPIs can be currently defined by aggregating records within the time interval by doing a SUM or AVERAGE.

Dimensions give you the ability to slice and dice your data by defining categories or segments. This allows you to track anomalies for a subset of the whole set of data for which a particular measure is applicable.

In our use case, we add three measures, which calculate the AVG of the objects seen in the 5-minute interval, and have only one dimension, for which pollutants concentration is measured.

Every record in the dataset must have a timestamp. The following configuration allows you to choose the field that represents the timestamp value and also the format of the timestamp.

The next page allows you to review all the details you added and then save and activate the detector.

The detector then begins learning the data streaming into the data source. At this stage, the status of the detector changes to Initializing.

It’s important to note the minimum amount of data that is required before Lookout for Metrics can start detecting anomalies. For more information about requirements and limits, see Lookout for Metrics quotas.

With minimal configuration, you have created your detector, pointed it at a dataset, and defined the metrics that you want Lookout for Metrics to find anomalies in.

Visualize anomalies

Lookout for Metrics provides a rich UI experience for users who want to use the AWS Management Console to analyze the anomalies being detected. It also provides the capability to query the anomalies via APIs.

Let’s look at an example anomaly detected from our air quality data use case. The following screenshot shows an anomaly detected in CO concentration in the air at the designated time and date with a severity score of 93. It also shows the percentage contribution of the dimension towards the anomaly. In this case, 100% contribution comes from the location ID B-101 dimension.

Create alerts

Lookout for Metrics allows you to send alerts using a variety of channels. You can configure the anomaly severity score threshold at which the alerts must be triggered.

In our use case, we configure alerts to be sent to an Amazon Simple Notification Service (Amazon SNS) channel, which in turn sends an SMS. The following screenshots show the configuration details.

You can also use an alert to trigger automations using AWS Lambda functions in order to drive API-driven operations on AWS IoT Core.

Conclusion

In this post, we showed you how easy to use Lookout for Metrics and Kinesis Data Firehose to remove the undifferentiated heavy lifting involved in managing the end-to-end lifecycle of building ML-powered anomaly detection applications. This solution can help you accelerate your ability to find anomalies in key business metrics and allow you focus your efforts on growing and improving your business.

We encourage you to learn more by visiting the Amazon Lookout for Metrics Developer Guide and try out the end-to-end solution enabled by these services with a dataset relevant to your business KPIs.

About the author

Dhiraj Thakur is a Solutions Architect with Amazon Web Services. He works with AWS customers and partners to provide guidance on enterprise cloud adoption, migration, and strategy. He is passionate about technology and enjoys building and experimenting in the analytics and AI/ML space.



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