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Introducing a Public Registry for AWS CloudFormation

AWS CloudFormation and the AWS Cloud Development Kit (CDK) provide scalable and consistent provisioning of AWS resources (for example, compute infrastructure, monitoring tools, databases, and more). We’ve heard from many customers that they’d like to benefit from the same consistency and scalability when provisioning resources from AWS Partner Network (APN) members, third-party vendors, and open-source…

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AWS CloudFormation and the AWS Cloud Development Kit (CDK) provide scalable and consistent provisioning of AWS resources (for example, compute infrastructure, monitoring tools, databases, and more). We’ve heard from many customers that they’d like to benefit from the same consistency and scalability when provisioning resources from AWS Partner Network (APN) members, third-party vendors, and open-source technologies, regardless of whether they are using CloudFormation templates or have adopted the CDK to define their cloud infrastructure.

I’m pleased to announce a new public registry for CloudFormation, providing a searchable collection of extensions – resource types or modules – published by AWS, APN partners, third parties, and the developer community. The registry makes it easy to discover and provision these extensions in your CloudFormation templates and CDK applications in the same manner you use AWS-provided resources. Using extensions, you no longer need to create and maintain custom provisioning logic for resource types from third-party vendors. And, you are able to use a single infrastructure as code tool, CloudFormation, to provision and manage AWS and third-party resources, further simplifying the infrastructure provisioning process (the CDK uses CloudFormation under the hood).

Launch Partners
We’re excited to be joined by over a dozen APN Partners for the launch of the registry, with more than 35 extensions available for you to use today. Blog posts and announcements from the APN Partners who collaborated on this launch, along with AWS Quick Starts, can be found below (some will be added in the next few days).

Registries and Resource Types
In 2019, CloudFormation launched support for private registries. These enabled registration and use of resource providers (Lambda functions) in your account, including providers from AWS and third-party vendors. After you registered a provider you could use resource types, comprised of custom provisioning logic, from the provider in your CloudFormation templates. Resource types were uploaded by providers to an Amazon Simple Storage Service (Amazon S3) bucket, and you used the types by referencing the relevant S3 URL. The public registry provides consistency in the sourcing of resource types and modules, and you no longer need to use a collection of Amazon Simple Storage Service (Amazon S3) buckets.

Third-party resource types in the public registry also integrate with drift detection. After creating a resource from a third-party resource type, CloudFormation will detect changes to the resource from its template configuration, known as configuration drift, just as it would with AWS resources. You can also use AWS Config to manage compliance for third-party resources consumed from the registry. The resource types are automatically tracked as Configuration items when you have configured AWS Config to record them, and used CloudFormation to create, update, and delete them. Whether the resource types you use are third-party or AWS resources, you can view configuration history for them, in addition to being able to write AWS Config rules to verify configuration best practices.

The public registry also supports Type Configuration, enabling you to configure third-party resource types with API keys and OAuth tokens per account and region. Once set, the configuration is stored securely and can be updated. This also provides a centralized way to configure third-party resource types.

Publishing Extensions to the Public Registry
Extension publishers must be verified as AWS Marketplace sellers, or as GitHub or BitBucket users, and extensions are validated against best practices. To publish extensions (resource types or modules) to the registry, you must first register in an AWS Region, using one of the mentioned account types.

After you’ve registered, you next publish your extension to a private registry in the same Region. Then, you need to test that the extension meets publishing requirements. For a resource type extension, this means it must pass all the contract tests defined for the type. Modules are subject to different requirements, and you can find more details in the documentation. With testing complete, you can publish your extension to the public registry for your Region. See the user guide for detailed information on publishing extensions.

Using Extensions in the Public Registry
I decided to try a couple of extensions related to Kubernetes, contributed by AWS Quick Starts, to make configuration changes to a cluster. Personally, I don’t have a great deal of experience with Kubernetes and its API so this was a great chance to examine how extensions could save me significant time and effort. During the process of writing this post I learned from others that using the Kubernetes API (the usual way to achieve the changes I had in mind) would normally involve effort even for those with more experience.

For this example I needed a Kubernetes cluster, so I followed this tutorial to set one up in Amazon Elastic Kubernetes Service (EKS), using the Managed nodes – Linux node type. With my cluster ready, I want to make two configuration changes.

First, I want to add a new namespace to the cluster. A namespace is a partitioning construct that lets me deploy the same set of resources to different namespaces in the same cluster without conflict thanks to the isolation namespaces provide. Second, I want to set up and use Helm, a package manager for Kubernetes. I’ll use Helm to install the kube-state-metrics package from the Prometheus helm-charts repository for gathering cluster metrics. While I can use CloudFormation to provision clusters and compute resources, previously, to perform these two configuration tasks, I’d have had to switch to the API or various bespoke tool chains. With the registry, and these two extensions, I can now do everything using CloudFormation (and of course, as I mentioned earlier, I could also use the extensions with the CDK, which I’ll show later).

Before using an extension, it needs to be activated in my account. While activation is easy to do for single accounts using the console, as we’ll see in a moment, if I were using AWS Organizations and wanted to activate various third-party extensions across my entire organization, or for a specific organization unit (OU), I could achieve this using Service-Managed StackSets in CloudFormation. Using the resource type AWS::CloudFormation::TypeActivation in a template submitted to a Service-Managed StackSet, I can target an entire Organization, or a particular OU, passing the Amazon Resource Name (ARN) identifying the third-party extension to be activated. Activation of extensions is also very easy to achieve (whether using AWS Organizations or not) using the CDK with just a few lines of code, again making use of the aforementioned TypeActivation resource type.

To activate the extensions, I head to the CloudFormation console and click Public extensions from the navigation bar. This takes me to the Registry:Public extensions home page, where I switch to viewing third party resource type extensions.

Viewing third-party types in the registry

The extensions I want are AWSQS::Kubernetes::Resource and AWSQS::Kubernetes::Helm. The Resource extension is used to apply a manifest describing configuration changes to a cluster. In my case, the manifest requests a namespace be created. Clicking the name of the AWSQS::Kubernetes::Resource extension takes me to a page where I can view schema, configuration details, and versions for the extension.

Viewing details of the Resource extension

What happens if you deactivate an extension you’re using, or an extension is withdrawn by the publisher? If you deactivate an extension a stack depends on, any resources created from that extension won’t be affected, but you’ll be unable to perform further stack operations, such as Read, Update, Delete, and List (these will fail until the extension is re-activated). Publishers must request their extensions be withdrawn from the registry (there is no “delete” API). If the request is granted, customers who activated the extension prior to withdrawal can still perform Create/Read/Update/Delete/List operations, using what is effectively a snapshot of the extension in their account.

Clicking Activate takes me to a page where I need to specify the ARN of an execution role that CloudFormation will assume when it runs the code behind the extension. I create a role following this user guide topic, but the basic trust relationship is below for reference.

{ “Version”: “2012-10-17”, “Statement”: [ { “Effect”: “Allow”, “Principal”: { “Service”: “resources.cloudformation.amazonaws.com” }, “Action”: “sts:AssumeRole” } ] }

I also add permissions for the resource types I’m using to my execution role. Details on the permissions needed for the types I chose can be found on GitHub, for Helm, and for Kubernetes (note the GitHub examples include the trust relationship too).

When activating an extension, I can elect to use the default name, which is how I will refer to the type in my templates or CDK applications, or I can enter a new name. The name chosen has to be unique within my account, so if I’ve enabled a version of an extension with its default name, and want to enable a different version, I must change the name. Once I’ve filled in the details, and chosen my versioning strategy (extensions use semantic versioning, and I can elect to accept automatic updates for minor version changes, or to “lock” to a specific version) clicking Activate extension completes the process.

Activating an extension from the registry

That completes the process for the first extension, and I follow the same steps for the AWSQS::Kubernetes::Helm extension. Navigating to Activated extensions I can view a list of all my enabled extensions.

Viewing the list of enabled extensions

I have one more set of permissions to update. Resource types make calls to the Kubernetes API on my behalf so I need to update the aws-auth ConfigMap for my cluster to reference the execution role I just used, otherwise the calls made by the resource types I’m using will fail. To do this, I run the command kubectl edit cm aws-auth -n kube-system at a command prompt. In the text editor that opens, I update the ConfigMap with a new group referencing my CfnRegistryExtensionExecRole, shown below (if you’re following along, be sure to change the account ID and role name to match yours).

apiVersion: v1 data: mapRoles: | – groups: – system:bootstrappers – system:nodes rolearn: arn:aws:iam::111122223333:role/myAmazonEKSNodeRole username: system:node:{{EC2PrivateDNSName}} – groups: – system:masters rolearn: arn:aws:iam::111122223333:role/CfnRegistryExtensionExecRole username: cfnresourcetypes kind: ConfigMap metadata: creationTimestamp: “2021-06-04T20:44:24Z” name: aws-auth namespace: kube-system resourceVersion: “6355” selfLink: /api/v1/namespaces/kube-system/configmaps/aws-auth uid: dc91bfa8-1663-45d0-8954-1e841913b324

Now I’m ready to use the extensions to configure my cluster with a new namespace, Helm, and the kube-state-metrics package. I create a CloudFormation template that uses the extensions, adding parameters for the elements I want to specify when creating a stack: the name of the cluster to update, and the namespace name. The properties for the KubeStateMetrics resource reference the package I want Helm to install.

AWSTemplateFormatVersion: “2010-09-09” Parameters: ClusterName: Type: String Namespace: Type: String Resources: KubeStateMetrics: Type: AWSQS::Kubernetes::Helm Properties: ClusterID: !Ref ClusterName Name: kube-state-metrics Namespace: !GetAtt KubeNamespace.Name Repository: https://prometheus-community.github.io/helm-charts Chart: prometheus-community/kube-state-metrics KubeNamespace: Type: AWSQS::Kubernetes::Resource Properties: ClusterName: !Ref ClusterName Namespace: default Manifest: !Sub | apiVersion: v1 kind: Namespace metadata: name: ${Namespace} labels: name: ${Namespace}

On the Stacks page of the CloudFormation console, I click Create stack, upload my template, and then give my stack a name and the values for my declared parameters.

Launching a stack with my activated extensions

I click Next to proceed through the rest of the wizard, leaving other settings at their default values, and then Create stack to complete the process.

Once stack creation is complete, I verify my changes using the kubectl command line tool. I first check that the new namespace, newsblog-sample-namespace, is present with the command kubectl get namespaces. I then run the kubectl get all –namespace newsblog-sample-namespace command to verify the kube-state-metrics package is installed.

Verifying the extensions applied by changes

Extensions can also be used with the AWS Cloud Development Kit. To wrap up this exploration of using the new registry, I’ve included an example below of a CDK application snippet in TypeScript that achieves the same effect, using the same extensions, as the YAML template I showed earlier (I could also have written this using any of the languages supported by the CDK – C#, Java, or Python).

import {Stack, Construct, CfnResource} from ‘@aws-cdk/core’; export class UnoStack extends Stack { constructor(scope: Construct, id: string) { super(scope, id); const clusterName = ‘newsblog-cluster’; const namespace = ‘newsblog-sample-namespace’; const kubeNamespace = new CfnResource(this, ‘KubeNamespace’, { type: ‘AWSQS::Kubernetes::Resource’, properties: { ClusterName: clusterName, Namespace: ‘default’, Manifest: this.toJsonString({ apiVersion: ‘v1’, kind: ‘Namespace’, metadata: { name: namespace, labels: { name: namespace, } }, }), }, }); new CfnResource(this, ‘KubeStateMetrics’, { type: ‘AWSQS::Kubernetes::Helm’, properties: { ClusterID: clusterName, Name: ‘kube-state-metrics’, Namespace: kubeNamespace.getAtt(‘Name’).toString(), Repository: ‘https://prometheus-community.github.io/helm-charts’, Chart: ‘prometheus-community/kube-state-metrics’, }, }); } };

As mentioned earlier in this post, I don’t have much experience with the Kubernetes API, and Kubernetes in general. However, by making use of the resource types in the public registry, in conjunction with CloudFormation, I was able to easily configure my cluster using a familiar environment, without needing to resort to the API or bespoke tool chains.

Get Started with the CloudFormation Public Registry
Pricing for the public registry is the same as for the existing registry and private resource types. There is no additional charge for using native AWS resource types; for third-party resource types you will incur charges based on the number of handler operations (add, delete, list, etc.) you run per month. For details, see the AWS CloudFormation Pricing page. The new public registry is available today in the US East (N. Virginia, Ohio), US West (Oregon, N. California), Canada (Central), Europe (Ireland, Frankfurt, London, Stockholm, Paris, Milan), Asia Pacific (Hong Kong, Mumbai, Osaka, Singapore, Sydney, Seoul, Tokyo), South America (Sao Paulo), Middle East (Bahrain), and Africa (Cape Town) AWS Regions.

For more information, see the AWS CloudFormation User Guide and User Guide for Extension Development, and start publishing or using extensions today!

— Steve

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Amazon

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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