Cloud Based Early Warning Drift Detection

Embodiments detect data drift associated with machine learning (“ML”) models. Embodiments identify a first feature stored by a feature store, where the feature store includes an offline store and an online store. Embodiments determine one or more first trained ML models that are using the first feature. For each of the first trained ML models, embodiments invoke the first trained ML model using synthetic data or validation data, generate metrics to determine an accuracy of the first trained ML model and, when the accuracy is below a threshold, generate an alert notifying of a first data drift for the first trained ML model.

One embodiment is directed generally to a computer system, and in particular to a machine learning model and feature store hosted in a cloud based computer system.

BACKGROUND INFORMATION

Cloud service providers provide various services in the “cloud”, meaning over a network, such as the public Internet, and remotely accessible to any network-connected client device. Examples of the services models used by cloud service providers (also referred to herein as “cloud providers” or “providers”) include infrastructure as a service (“IaaS”), platform as a service (“PaaS”), software as a service (“SaaS”), and network as a service (“NaaS”). IaaS providers provide customers with infrastructure resources such as processing, storage, networks, and other computing resources that the customer is able to use to run software. The customer does not manage the infrastructure, but has control over operating systems, storage, and deployed applications, among other things, and may be able to control some networking components, such as firewalls. PaaS providers provide a customer with a platform on which the customer can develop, run, and manage an application without needing to maintain the underlying computing infrastructure. SaaS is a software licensing and delivery model in which software is licensed to a customer on a subscription basis, and is centrally hosted by the cloud provider. Under this model, applications can be accessed, for example, using a web browser. NaaS providers provide network services to customers, for example, by provisioning a virtual network on the network infrastructure operated by another party. In each of these service models, the cloud service provider maintains and manages the hardware and/or software that provide the services, and little, if any, software executes on a user's device.

Customers of cloud service providers, which are also referred to herein as users and tenants, can subscribe to the service provider to obtain access to the particular services provided by the service provider. The service provider can maintain an account for a user or tenant through which the user and/or tenant can access the provider's services. The service provider can further maintain user accounts that are associated with the tenant, for individual users.

One functionality that may be supported by a cloud service provider is the training and implementation of machine learning (“ML”) models. ML models, in general, after being trained, provide predictions based on newly provided data. However, ML models can run into issues related to “data drift” when deployed using real-world current data. Data drift occurs when ML models are passed new inputs that include new values or a skew in data that is no longer representative of the distribution of data in the offline training dataset, or which are not present in training datasets, or new data that is not representative of the training data. This may occur because of a sample selection bias, or because of non-stationary environments wherein the data changes because of a variety of factors, including but not limited to, instances where an adversary tries to work around the existing classifier's learned concepts, or where new data is simply not representative of training data. In other instances, data drift may occur, for example, because of changes in population distribution over time, changes in distribution of a class variable, or changes to definitions of a class (i.e., a changing context that can induce changes in target concepts).

SUMMARY

Embodiments detect data drift associated with machine learning (“ML”) models. Embodiments identify a first feature stored by a feature store, where the feature store includes an offline store and an online store. Embodiments determine one or more first trained ML models that are using the first feature. For each of the first trained ML models, embodiments invoke the first trained ML model using synthetic data or validation data, generate metrics to determine an accuracy of the first trained ML model and, when the accuracy is below a threshold, generate an alert notifying of a first data drift for the first trained ML model.

DETAILED DESCRIPTION

One embodiment detects data drift at a cloud based feature store or of a trained machine learning model using an offline drift detector as well as an online inference detector. Embodiments allow the data drift to be detected before the data drift impacts predictions from production models.

“Data drift” includes “concept drift”, which means that the statistical properties of the target variable, which the machine learning (“ML”) model is trying to predict, changes over time in unforeseen ways. This causes problems because the predictions become less accurate as time passes. The measurement and/or detection of data drift is a complicated process in ML operations and most of the time it is captured as “post the fact”, particularly for concept drift. Some known solutions detect the drift at the data integration stage. However, this may not be fully captured if the ML model uses features in the downstream feature engineering process.

For example, a data scientist can create features out of raw data and store them in a feature store. A transformation is run on the data to get the feature (e.g., Credit score>700 captured as a feature computation to a feature named “Credit track record”). Embodiments measure the drift early at the feature level and provide signals to the ML engineers even before the drift happens so that it can be a proactive measure for them to deep dive or discard based on the drift signal findings. Specifically, in embodiments, data enters the feature store and then the same data fed to train one or more models. In embodiments, when data is ingested in the feature store, a series of tests are run to detect the drift. If the data has been drifted in the feature store, the test will detect it and the same data set will not be fed/available for training the models, therefore preventing model drift.

FIG.1illustrates an example of a system100that includes an ML data and inference drift detection layer system10in accordance to embodiments. ML data and inference drift detection layer system10may be implemented within a computing environment that includes a communication network/cloud104. Network104may be a private network that can communicate with a public network (e.g., the Internet) to access additional services110provided by a cloud services provider. Examples of communication networks include a mobile network, a wireless network, a cellular network, a local area network (“LAN”), a wide area network (“WAN”), other wireless communication networks, or combinations of these and other networks. ML data and inference drift detection layer system10may be administered by a service provider, such as via the Oracle Cloud Infrastructure (“OCI”) from Oracle Corp.

Tenants of the cloud services provider can be organizations or groups whose members include users of services offered by service provider. Services may include or be provided as access to, without limitation, an application, a resource, a file, a document, data, media, or combinations thereof. Users may have individual accounts with the service provider and organizations may have enterprise accounts with the service provider, where an enterprise account encompasses or aggregates a number of individual user accounts.

System100further includes client devices106, which can be any type of device that can access network104and can obtain the benefits of the functionality of ML data and inference drift detection layer system10of detecting data drift of ML models. As disclosed herein, a “client” (also disclosed as a “client system” or a “client device”) may be a device or an application executing on a device. System100includes a number of different types of client devices106that each is able to communicate with network104.

Executing on cloud104are one or more ML models125. Each ML model125can be executed by a customer of cloud104. In embodiments, an ML model125can be accessible to a client106via a representational state transfer application programming interface (“REST API”) and function as an endpoint to the API. ML models125can by any type of machine learning model that, in general, is trained on some training data and validation data and then can process additional incoming “live” data to make predictions. Examples of ML models125include artificial neural networks (“ANN”), decision trees, support-vector machines (“SVM”), Bayesian networks, etc. Training data can be any set of data capable of training ML model125(e.g., a set of features with corresponding labels, such as labeled data for supervised learning). In embodiments, training data can be used to train a ML model125to generate a trained ML model125.

Part of layer10may be incorporated in a feature store50, also hosted by cloud104. In general, a feature store encompasses the domain knowledge within the cloud based applications which makes it richer to build and access. In machine learning and pattern recognition, a “feature” is an individual measurable property or characteristic of a phenomenon. Choosing informative, discriminating and independent features is a crucial element of effective algorithms in pattern recognition, classification and regression. In data science, a “feature store” can provide a single pane of glass for sharing all available features. When a data scientist starts a new project, he or she can go to the feature store, functioning in part as a catalog, and easily find the features they are looking for. However, a feature store is not only a data layer. It is also a data transformation service enabling users to manipulate raw data and store it as features ready to be used by any machine learning model.

In contrast to generally known feature stores, feature store50also incorporates/integrates drift detection functionality disclosed herein.

FIG.2is a block diagram of an ML data and inference drift detection layer system10ofFIG.1in the form of a computer server/system10in accordance with an embodiment of the present invention. Although shown as a single system, the functionality of system10can be implemented as a distributed system. Further, the functionality disclosed herein can be implemented on separate servers or devices that may be coupled together over a network. Further, one or more components of system10may not be included. One or more components ofFIG.2can also be used to implement any of the elements ofFIG.1andFIG.3, discussed below. Further, system/layer10is integrated with other elements, such as feature store50and/or a REST API server disclosed below.

System10includes a bus12or other communication mechanism for communicating information, and a processor22coupled to bus12for processing information. Processor22may be any type of general or specific purpose processor. System10further includes a memory14for storing information and instructions to be executed by processor22. Memory14can be comprised of any combination of random access memory (“RAM”), read only memory (“ROM”), static storage such as a magnetic or optical disk, or any other type of computer readable media. System10further includes a communication device20, such as a network interface card, to provide access to a network. Therefore, a user may interface with system10directly, or remotely through a network, or any other method.

Processor22is further coupled via bus12to a display24, such as a Liquid Crystal Display (“LCD”). A keyboard26and a cursor control device28, such as a computer mouse, are further coupled to bus12to enable a user to interface with system10.

In one embodiment, memory14stores software modules that provide functionality when executed by processor22. The modules include an operating system15that provides operating system functionality for system10. The modules further include an ML data and inference draft detection layer module16that detects data drift of ML models, and all other functionality disclosed herein. System10can be part of a larger system. Therefore, system10can include one or more additional functional modules18to include the additional functionality, such as any other functionality provided by the Oracle Cloud Infrastructure (“OCI”) from Oracle Corp. A file storage device or database17is coupled to bus12to provide centralized storage for modules16and18, including data regarding previous schema mappings. In one embodiment, database17is a relational database management system (“RDBMS”) that can use Structured Query Language (“SQL”) to manage the stored data.

In one embodiment, database17is implemented as an in-memory database (“IMDB”). An IMDB is a database management system that primarily relies on main memory for computer data storage. It is contrasted with database management systems that employ a disk storage mechanism. Main memory databases are faster than disk-optimized databases because disk access is slower than memory access, the internal optimization algorithms are simpler and execute fewer CPU instructions. Accessing data in memory eliminates seek time when querying the data, which provides faster and more predictable performance than disk.

In one embodiment, database17, when implemented as an IMDB, is implemented based on a distributed data grid. A distributed data grid is a system in which a collection of computer servers work together in one or more clusters to manage information and related operations, such as computations, within a distributed or clustered environment. A distributed data grid can be used to manage application objects and data that are shared across the servers. A distributed data grid provides low response time, high throughput, predictable scalability, continuous availability, and information reliability. In particular examples, distributed data grids, such as, e.g., the “Oracle Coherence” data grid from Oracle Corp., store information in-memory to achieve higher performance, and employ redundancy in keeping copies of that information synchronized across multiple servers, thus ensuring resiliency of the system and continued availability of the data in the event of failure of a server.

FIG.3is a block/flow diagram of a portion of system100that includes ML feature store with drift detection layer system10(shown as drift detector301and/or inference detector320) and related components in accordance to embodiments of the invention.

System100includes one or more data sources301. “Raw” data is stored in different data sources301, such as a database341, an object storage service (“OSS”)342, a streaming service343, a file system (not shown), a data lake (i.e., centralized repository designed to store, process, and secure large amounts of structured, semi-structured, and unstructured data) (not shown), etc.

Feature extraction/engineering is performed at302and the generated features are stored in feature store50. Generating a new feature via feature engineering can require a large amount of work. Due to different requirements during training and serving, features are kept in an offline store312(for offline or batch processing) or an online store314(for real-time processing) part of feature store50. A user may decide where to store the features. A user can choose to ingest in both, but in general most recent features are stored in online while offline contains all historic feature values. Online feature store314serves online applications with data at a low-latency, such as “MySQL.” Offline feature store312in embodiments includes scale-out SQL databases that provide data for developing AI models and make feature governance possible for explainability and transparency, such as “Hive.”

Feature store10, in general, is a data management layer for machine learning that allows to users to share discovered/generated features and create more effective machine learning pipelines. Feature store50, in embodiments, further includes a data drift layer that provides that data drift detection functionality disclosed herein. Features are considered any measurable input that can be used in a predictive model (i.e., any type of ML or artificial intelligence model). For example, a recommendation application may use the total amount per purchase or product category as one of its many features. Features are used to train ML models and make predictions. In general, the more data, the better the predictions.

The features also need to be organized in order to make sense. The data for the features needs to be pulled from somewhere (i.e., data source301) and the features need to be stored after being computed for an ML pipeline to be able to use the features. Feature store50is where the features are stored and organized for the explicit purpose of being used to either train models or make predictions (by applications that have a trained model). Feature store50is a central location within cloud104where groups of features can either be updated or created from multiple different data sources, new datasets can be created or updated from those feature groups for training models or for use in applications that do not want to compute the features but just retrieve them when it needs them to make prediction

After being created and stored, in embodiments at351the engineered features are pulled from offline feature store312for model training at328. In general, a model is trained using both a training dataset and a validation dataset. Once the performance of the model is satisfied (e.g., via automated testing or determined by a data scientist), the model is deployed at326. Model deployment module326is responsible for creating infrastructure in order to host the selected model in a production environment and provides an endpoint via cloud104from which user of the models, via one or more of clients106, provides calls to get the prediction result. The endpoint is accessed via an inference representational state transfer application programming interface (“REST API”) server322.

REST API server322includes one or more servers hosted on cloud104that allows each of clients106to obtain web resources/services (e.g., provided by servers110and server10). In a RESTful Web service (i.e., a service obtained via a REST API), requests made to a resource's URI elicit a response with a payload formatted in HTML, XML, JSON, or some other format. For example, the response can confirm that the resource state has been changed. The response can also include hypertext links to related resources. The most common protocol for these requests and responses is HTTP. It provides operations (HTTP methods) such as OPTIONS, GET, POST, PUT, PATCH and DELETE. In embodiments, the REST API request includes inference data that is received by one or more ML models326, which in return provide a prediction.

In general, for an ML model, in the training phase, a developer feeds their model a curated dataset so that it can “learn” everything it needs to about the type of data it will analyze. Then, in the inference phase (initiated via REST API322by a client), the model can make predictions based on live data to produce actionable results.

System150further includes an ML monitoring module324that monitors different metrics to capture the model performance and key performance indicator (“KPI”) metrics.

Embodiments further include a drift detector310that is integrated into the offline store portion of feature store50as part of the data drift layer. In embodiments, features are generated from raw data301via the feature engineering process302and published in feature store50at scheduled intervals for batch data. When the pipeline (i.e., feature engineering process302) generates a new feature value, it notifies drift detector310to begin processing. Drift detection by drift detector310in embodiments is performed on a feature-by-feature basis each time the feature definition pipeline is executed. A new version of the feature is generated when feature definition (transformation logic) changes. Drift detection would happen every time a feature definition pipeline runs both for a new feature version or raw data ingestion.

Raw data can include a column such as gender having values as “Male”, “Female”, “Prefer Not to say” which can be mapped to a feature with a name “gender_encoded” having values as 0, 1, 2 respectively. Here, “gender_encoded” is the feature and 0, 1, 2 are feature values. In embodiments, drift may be detected if a new feature value of, for example, 4, is detected. Similarly, drift may be detected is only a value 0 for the gender_encoded feature is detected instead of a variety of values.

For example, for a deployed model, a dataset has been used to train the model. Drift detector310compares the training dataset with the new dataset using samples of data to detect anomalies between the datasets. The training dataset contains actual features instead of raw data. In certain scenarios, a feature may be the same as raw data when no transformation or feature engineering is performed on the raw data, but that is not typically the case. As features are fed as input into a model (as opposed to raw data), drift detection should happen at the feature level. Due to the feature engineering and extraction process, a drift in raw data might get suppressed or changed, resulting in no deviation in the underlying feature.

Drift detector310processes the data and generates a series of metrics to detect any drift. Data drift can be identified using sequential analysis methods, model-based methods, and time distribution-based methods. Sequential analysis methods such as DDM (drift detection method)/EDDM (early DDM) rely on the error rate to identify the drift detection. A model-based method uses a custom model to identify the drift. Time distribution-based methods use statistical distance calculation methods to calculate drift between probability distributions.

FIG.4illustrates metrics generated by drift detector310in accordance to one embodiment. As shown inFIG.4, the metrics include a mean, median and mode check to detect any skewed data. In embodiments, every time the feature definition pipeline is run, drift detector310will compute statistical properties such as mean, median, mode etc. on the features (i.e., old feature values vs. new feature values) and compare it with previously computed statistic metrics for the features. In other embodiments, other analysis can be done automatically such as boxplot analysis for outliers detection, concept drift due to change in statistical properties of data with time, etc.

Other embodiments use one or more of the following methodologies to detect data by determining the difference between any 2 populations (e.g., old feature values vs. new feature values) include: (1) Kolmogorov-Smirnov (K-S) test, (2) Population Stability Index; (3) Page-Hinkley method, etc.

In general, whenever an execution of a feature pipeline finishes, it will notify drift detector310system about its completion. Drift detector310will then run series of test to see is there is the occurrence of any data drift. A feature pipeline (e.g., feature engineering pipeline302) runs the logic of converting raw data into user defined features. Therefore, the execution of the pipeline terminating results in new feature values being available. In one embodiment, the next step is for drift detector310to determine drift, and then get ingested into the offline feature store if no drift.

If any drift is detected by drift detector310, it will attach a tag with the new version of feature, marking it with a potential data drift label. Embodiments include a quality gate351on the link between offline store312and model training328which will block any feature, on a per feature basis, with the data drift label tag to get consumed further downstream in the pipeline. If no drift detection is found, gate351will open and the features will be ready to get consumed by production models for model training at328.

In other embodiments, drift detector310will implement a predefined series of detection tests will also include an option for users to attach their own drift detection custom test logic as a plug and play module.

System100further includes an inference detector320. In embodiments, inference detector320is a separate component from feature store50and is integrated with server322. Machine learning inference is the process of running data points into a machine learning model to calculate an output, such as a single numerical score or other type of prediction. Specifically, once one or more of models326are deployed in production, each deployed model provides an endpoint322via which a client can call with an input vector to get the prediction result.

When multiple models326are implemented, there may be instances that one of the features stored in feature store50is used by multiple ML models125and all or some of the models are deployed in production (i.e., having an endpoint available for clients to make a prediction call/request via REST API server322).

Embodiments, using feature store50, identify what features are used by which models and then, via a model store/catalog in embodiments, which model deployments are using the model. A model store/catalog is a centralized repository of machine learning models and ensure that model artifacts are immutable and allows data scientists to share models, and reproduce them as needed. After a model is stored in the model store/catalog, it can be deployed as an HTTP endpoint using a model deployments resource. Using a feature means which input features were used to train the model. Therefore, embodiments retrieve all the endpoints in which the feature is being used indirectly (i.e., which endpoints are using the feature store). An endpoint is the HTTP endpoint where the model is deployed and serving inference requests. Embodiments determine all model deployment endpoints that are using a particular feature.

In one embodiment, inference detector320generates some synthetic data for a particular feature. Synthetic data is artificial data that mimics real-world observations and is used to train machine learning models when actual data is difficult or expensive to get. In embodiments, a “python” package can be used to generate synthetic data from real data used as a reference. In other embodiments, inference detector320gets a validation data set from offline store312to use (i.e., data that passes through quality gate351by drift detector312). A validation dataset is the sample of data used to provide an unbiased evaluation of a model fit on the training dataset while tuning model hyperparameters. The evaluation becomes more biased as the skill on the validation dataset is incorporated into the model configuration. The validation set is used to evaluate a given model, but this is for frequent evaluation. Validation data is also used to fine-tune the model hyperparameters. Therefore, the model occasionally sees this data, but it never “learns” from it. Therefore, the validation set affects a model, but only indirectly.

Inference detector320then randomly selects some endpoints from the list of endpoints that are using the feature. In embodiments, inference detector runs as a scheduled job on a pre-defined time interval.

In embodiments, inference detector320invokes a randomly selected endpoint and captures a series of metrics reflecting the accuracy of the model (e.g., an AUC curve (i.e., area under the ROC curve), precision, recall etc.) and capture the information in a database.

If the newly calculated metrics are below some threshold (for example AOC is less than some threshold value AOCX or Precision is below X, etc.), inference detector320can trigger an alert which will notify the data scientist about possible drift of the data in production. Inference detector320can also get similar feedback information from ML monitoring system324, such as when KPI metrics for recommendation link click conversion goes below 30%, to trigger the alarm.

Therefore, inference detector320proactively determines models that are not performing well before the models are implemented in production.

As an example of the functionality of system100, assume a retail store uses machine learning models that are implemented by cloud104to predict the expected selling revenue for different items available in the store. This will help them to manage their logistics better.

Due to certain temporary changes in the world, assume the customer buying pattern changes. For example, during the pandemic, customer started to buy extra amounts of toilet paper. If the same data starts flowing to the system, the data will be skewed as everyone is buying it. If that same feature is used in inference, system100will not be able to predict the sales properly because the feature values have drifted (e.g., the number of toilet paper sold per day or the average number of toilet paper bought by a customer).

Using embodiments of the invention, users would be performing feature engineering on the raw data and registering features with feature store50using as a prerequisite to model training and inference predictions. Feature store50includes offline store312used for model training and online store314used for inference (i.e., using live data input to the ML model to generate a prediction). Whenever new data enters feature store50, a notification is send to drift detector310to start performing the series of tests (e.g., mean, median, mode, KS test, etc.). The tests will use the feature store historical data which has the previous pattern. The test will compare the new data and feature store historical data, which will detect some drift.

Drift detector310will alert an ML engineer about the possible drift and tag the data set with a “possible drift” tag. The tag can be consumed in different ways by the consumer. For example, if machine learning pipeline sees the tag in a dataset, it will not allow the data to flow to the next steps in the pipeline and model inference will not occur with the drift data and an inaccurate prediction will be avoided. Instead, system100can trigger the retraining of the model with the newly observed data. The ML engineer can then investigate and take the appropriate action depending upon the use case.

In contrast to embodiments of the invention, known drift detector systems generally would need some explicit validation data to detect the drift. Known systems will see the new customer pattern data and the system them needs to provide some expected patterns explicitly. If the system started training the recent temporal change in the pattern of data, then it will start making wrong predictions. For example, it might start predicting a large number of toilet paper needed, which may not be the case next week if people started buying it due to a hoax. Known systems would need some type of manual intervention to respond.

Known drift detection systems generally require users to register their training dataset and provide a list of features which are of interest to users. In order to detect drift, known systems would also need to take the unseen data (i.e., live inference data) from users and then perform statistical tests on these two datasets to detect concept or covariate drift.

In contrast to embodiments of the invention, known systems are generally reactive. For example, a new model may be trained with some drifted data and deployed in production. A user will begin using it, and then the system will detect the performance is degraded and take action. In these known systems, the users first need to invoke the inference endpoint to detect the possible degraded performance of the model due to drifted trained data, which is after the prediction event happened. In contrast, with embodiments, even if users are not using a drifted model, inference detector320will detect it proactively, even though no user has yet to invoke the endpoint.

As another example, assume a financial organization ML model that is provided real time data to predict/detect possible fraud detection. During the pandemic, a large number of people who have not been exposed to online shopping were forced to use it due to lockdown restrictions. Because these users have none or minimal past history, the fraud detection system is under stress because it is not able to detect between fraudulent and non-fraudulent transactions. For example, during certain timeframes people might prefer buying items from offline stores but a senior citizen may avoid online buying channels due to trust issues or because of unfamiliarity. Such data would not be present in the historical/training data so a prediction will start to go wrong (drift) in such scenarios.

In embodiments, drift detector310will see these new user entries as new data and not as drifted data as the tests may not find a changing pattern. For example, the mean and median values may remain substantially similar as with the previous pattern data in feature store50. Therefore, the data will be moved to model training328and after that a newly trained model326for fraud will be deployed in production.

In this example, new data ingested into the feature store was tested and no drift was detected as the statistical nature of the data may remain same. Then, the quality gate passes the data to next steps which trains the model with new data and creates a newly trained model. This process of new data to model training is typically automated, so when new data arrives, the training steps execute to produce the new model trained on new data, which results in a new model created with newly trained data.

However, before an actual customer begins to use the newly deployed model, inference detector320will invoke the model prediction endpoint, using generated data or feature store data and try to predict the model performance. Inference detector320will then compare the model performance metrics with previous historical metrics computed on the same data (e.g., the correlation between input and target label can change between historical data and the generated data or feature store data), and if it is below a configured threshold it will (1) notify the ML engineer and (2) mark the model deployment with a “possible drifted deployment” tag. The system then can roll back the model deployment or block users from accessing this model.

Therefore, with embodiments, this detection happens even before actual users start using the new endpoint. This is proactive action (before the customer sees it) rather than reactive.

In contrast, known drift detection systems generally implement a reactive approach, by waiting until users start seeing deteriorated model performance, not before.

Example Cloud Infrastructure

FIGS.5-8illustrate an example cloud infrastructure that can incorporate the secure on-premises to cloud connector framework system in accordance to embodiments. The cloud infrastructure ofFIG.5-8can be used to implement network/cloud104ofFIG.1and host ML data and inference drift detection layer system10.

In some instances, IaaS customers may access resources and services through a wide area network (“WAN”), such as the Internet, and can use the cloud provider's services to install the remaining elements of an application stack. For example, the user can log in to the IaaS platform to create virtual machines (“VM”s), install operating systems (“OS”s) on each VM, deploy middleware such as databases, create storage buckets for workloads and backups, and even install enterprise software into that VM. Customers can then use the provider's services to perform various functions, including balancing network traffic, troubleshooting application issues, monitoring performance, managing disaster recovery, etc.

In some examples, an infrastructure may have many interconnected elements. For example, there may be one or more virtual private clouds (“VPC”s) (e.g., a potentially on-demand pool of configurable and/or shared computing resources), also known as a core network. In some examples, there may also be one or more security group rules provisioned to define how the security of the network will be set up and one or more virtual machines. Other infrastructure elements may also be provisioned, such as a load balancer, a database, or the like. As more and more infrastructure elements are desired and/or added, the infrastructure may incrementally evolve.

The VCN1106can include a local peering gateway (“LPG”)1110that can be communicatively coupled to a secure shell (“SSH”) VCN1112via an LPG1110contained in the SSH VCN1112. The SSH VCN1112can include an SSH subnet1114, and the SSH VCN1112can be communicatively coupled to a control plane VCN1116via the LPG1110contained in the control plane VCN1116. Also, the SSH VCN1112can be communicatively coupled to a data plane VCN1118via an LPG1110. The control plane VCN1116and the data plane VCN1118can be contained in a service tenancy1119that can be owned and/or operated by the IaaS provider.

The control plane VCN1116can include a control plane demilitarized zone (“DMZ”) tier1120that acts as a perimeter network (e.g., portions of a corporate network between the corporate intranet and external networks). The DMZ-based servers may have restricted responsibilities and help keep security breaches contained. Additionally, the DMZ tier1120can include one or more load balancer (“LB”) subnet(s)1122, a control plane app tier1124that can include app subnet(s)1126, a control plane data tier1128that can include database (DB) subnet(s)1130(e.g., frontend DB subnet(s) and/or backend DB subnet(s)). The LB subnet(s)1122contained in the control plane DMZ tier1120can be communicatively coupled to the app subnet(s)1126contained in the control plane app tier1124and an Internet gateway1134that can be contained in the control plane VCN1116, and the app subnet(s)1126can be communicatively coupled to the DB subnet(s)1130contained in the control plane data tier1128and a service gateway1136and a network address translation (NAT) gateway1138. The control plane VCN1116can include the service gateway1136and the NAT gateway1138.

The control plane VCN1116can include a data plane mirror app tier1140that can include app subnet(s)1126. The app subnet(s)1126contained in the data plane mirror app tier1140can include a virtual network interface controller (VNIC)1142that can execute a compute instance1144. The compute instance1144can communicatively couple the app subnet(s)1126of the data plane mirror app tier1140to app subnet(s)1126that can be contained in a data plane app tier1146.

The data plane VCN1118can include the data plane app tier1146, a data plane DMZ tier1148, and a data plane data tier1150. The data plane DMZ tier1148can include LB subnet(s)1122that can be communicatively coupled to the app subnet(s)1126of the data plane app tier1146and the Internet gateway1134of the data plane VCN1118. The app subnet(s)1126can be communicatively coupled to the service gateway1136of the data plane VCN1118and the NAT gateway1138of the data plane VCN1118. The data plane data tier1150can also include the DB subnet(s)1130that can be communicatively coupled to the app subnet(s)1126of the data plane app tier1146.

The Internet gateway1134of the control plane VCN1116and of the data plane VCN1118can be communicatively coupled to a metadata management service1152that can be communicatively coupled to public Internet1154. Public Internet1154can be communicatively coupled to the NAT gateway1138of the control plane VCN1116and of the data plane VCN1118. The service gateway1136of the control plane VCN1116and of the data plane VCN1118can be communicatively coupled to cloud services1156.

In some examples, the service gateway1136of the control plane VCN1116or of the data plane VCN1118can make application programming interface (“API”) calls to cloud services1156without going through public Internet1154. The API calls to cloud services1156from the service gateway1136can be one-way: the service gateway1136can make API calls to cloud services1156, and cloud services1156can send requested data to the service gateway1136. But, cloud services1156may not initiate API calls to the service gateway1136.

In some examples, the secure host tenancy1104can be directly connected to the service tenancy1119, which may be otherwise isolated. The secure host subnet1108can communicate with the SSH subnet1114through an LPG1110that may enable two-way communication over an otherwise isolated system. Connecting the secure host subnet1108to the SSH subnet1114may give the secure host subnet1108access to other entities within the service tenancy1119.

The control plane VCN1116may allow users of the service tenancy1119to set up or otherwise provision desired resources. Desired resources provisioned in the control plane VCN1116may be deployed or otherwise used in the data plane VCN1118. In some examples, the control plane VCN1116can be isolated from the data plane VCN1118, and the data plane mirror app tier1140of the control plane VCN1116can communicate with the data plane app tier1146of the data plane VCN1118via VNICs1142that can be contained in the data plane mirror app tier1140and the data plane app tier1146.

In some examples, users of the system, or customers, can make requests, for example create, read, update, or delete (“CRUD”) operations, through public Internet1154that can communicate the requests to the metadata management service1152. The metadata management service1152can communicate the request to the control plane VCN1116through the Internet gateway1134. The request can be received by the LB subnet(s)1122contained in the control plane DMZ tier1120. The LB subnet(s)1122may determine that the request is valid, and in response to this determination, the LB subnet(s)1122can transmit the request to app subnet(s)1126contained in the control plane app tier1124. If the request is validated and requires a call to public Internet1154, the call to public Internet1154may be transmitted to the NAT gateway1138that can make the call to public Internet1154. Memory that may be desired to be stored by the request can be stored in the DB subnet(s)1130.

In some examples, the data plane mirror app tier1140can facilitate direct communication between the control plane VCN1116and the data plane VCN1118. For example, changes, updates, or other suitable modifications to configuration may be desired to be applied to the resources contained in the data plane VCN1118. Via a VNIC1142, the control plane VCN1116can directly communicate with, and can thereby execute the changes, updates, or other suitable modifications to configuration to, resources contained in the data plane VCN1118.

In some embodiments, the control plane VCN1116and the data plane VCN1118can be contained in the service tenancy1119. In this case, the user, or the customer, of the system may not own or operate either the control plane VCN1116or the data plane VCN1118. Instead, the IaaS provider may own or operate the control plane VCN1116and the data plane VCN1118, both of which may be contained in the service tenancy1119. This embodiment can enable isolation of networks that may prevent users or customers from interacting with other users', or other customers', resources. Also, this embodiment may allow users or customers of the system to store databases privately without needing to rely on public Internet1154, which may not have a desired level of security, for storage.

In other embodiments, the LB subnet(s)1122contained in the control plane VCN1116can be configured to receive a signal from the service gateway1136. In this embodiment, the control plane VCN1116and the data plane VCN1118may be configured to be called by a customer of the IaaS provider without calling public Internet1154. Customers of the IaaS provider may desire this embodiment since database(s) that the customers use may be controlled by the IaaS provider and may be stored on the service tenancy1119, which may be isolated from public Internet1154.

FIG.6is a block diagram1200illustrating another example pattern of an IaaS architecture, according to at least one embodiment. Service operators1202(e.g. service operators1102) can be communicatively coupled to a secure host tenancy1204(e.g. the secure host tenancy1104) that can include a virtual cloud network (VCN)1206(e.g. the VCN1106) and a secure host subnet1208(e.g. the secure host subnet1108). The VCN1206can include a local peering gateway (LPG)1210(e.g. the LPG1110) that can be communicatively coupled to a secure shell (SSH) VCN1212(e.g. the SSH VCN111210) via an LPG1110contained in the SSH VCN1212. The SSH VCN1212can include an SSH subnet1214(e.g. the SSH subnet1114), and the SSH VCN1212can be communicatively coupled to a control plane VCN1216(e.g. the control plane VCN1116) via an LPG1210contained in the control plane VCN1216. The control plane VCN1216can be contained in a service tenancy1219(e.g. the service tenancy1119), and the data plane VCN1218(e.g. the data plane VCN1118) can be contained in a customer tenancy1221that may be owned or operated by users, or customers, of the system.

The control plane VCN1216can include a control plane DMZ tier1220(e.g. the control plane DMZ tier1120) that can include LB subnet(s)1222(e.g. LB subnet(s)1122), a control plane app tier1224(e.g. the control plane app tier1124) that can include app subnet(s)1226(e.g. app subnet(s)1126), a control plane data tier1228(e.g. the control plane data tier1128) that can include database (DB) subnet(s)1230(e.g. similar to DB subnet(s)1130). The LB subnet(s)1222contained in the control plane DMZ tier1220can be communicatively coupled to the app subnet(s)1226contained in the control plane app tier1224and an Internet gateway1234(e.g. the Internet gateway1134) that can be contained in the control plane VCN1216, and the app subnet(s)1226can be communicatively coupled to the DB subnet(s)1230contained in the control plane data tier1228and a service gateway1236and a network address translation (NAT) gateway1238(e.g. the NAT gateway1138). The control plane VCN1216can include the service gateway1236and the NAT gateway1238.

The control plane VCN1216can include a data plane mirror app tier1240(e.g. the data plane mirror app tier1140) that can include app subnet(s)1226. The app subnet(s)1226contained in the data plane mirror app tier1240can include a virtual network interface controller (VNIC)1242(e.g. the VNIC of1142) that can execute a compute instance1244(e.g. similar to the compute instance1144). The compute instance1244can facilitate communication between the app subnet(s)1226of the data plane mirror app tier1240and the app subnet(s)1226that can be contained in a data plane app tier1246(e.g. the data plane app tier1146) via the VNIC1242contained in the data plane mirror app tier1240and the VNIC1242contained in the data plane app tier1246.

The Internet gateway1234contained in the control plane VCN1216can be communicatively coupled to a metadata management service1252(e.g. the metadata management service1152) that can be communicatively coupled to public Internet1254(e.g. public Internet1154). Public Internet1254can be communicatively coupled to the NAT gateway1238contained in the control plane VCN1216. The service gateway1236contained in the control plane VCN1216can be communicatively couple to cloud services1256(e.g. cloud services1156).

In some examples, the data plane VCN1218can be contained in the customer tenancy1221. In this case, the IaaS provider may provide the control plane VCN1216for each customer, and the IaaS provider may, for each customer, set up a unique compute instance1244that is contained in the service tenancy1219. Each compute instance1244may allow communication between the control plane VCN1216, contained in the service tenancy1219, and the data plane VCN1218that is contained in the customer tenancy1221. The compute instance1244may allow resources that are provisioned in the control plane VCN1216that is contained in the service tenancy1219, to be deployed or otherwise used in the data plane VCN1218that is contained in the customer tenancy1221.

In other examples, the customer of the IaaS provider may have databases that live in the customer tenancy1221. In this example, the control plane VCN1216can include the data plane mirror app tier1240that can include app subnet(s)1226. The data plane mirror app tier1240can reside in the data plane VCN1218, but the data plane mirror app tier1240may not live in the data plane VCN1218. That is, the data plane mirror app tier1240may have access to the customer tenancy1221, but the data plane mirror app tier1240may not exist in the data plane VCN1218or be owned or operated by the customer of the IaaS provider. The data plane mirror app tier1240may be configured to make calls to the data plane VCN1218, but may not be configured to make calls to any entity contained in the control plane VCN1216. The customer may desire to deploy or otherwise use resources in the data plane VCN1218that are provisioned in the control plane VCN1216, and the data plane mirror app tier1240can facilitate the desired deployment, or other usage of resources, of the customer.

In some embodiments, the customer of the IaaS provider can apply filters to the data plane VCN1218. In this embodiment, the customer can determine what the data plane VCN1218can access, and the customer may restrict access to public Internet1254from the data plane VCN1218. The IaaS provider may not be able to apply filters or otherwise control access of the data plane VCN1218to any outside networks or databases. Applying filters and controls by the customer onto the data plane VCN1218, contained in the customer tenancy1221, can help isolate the data plane VCN1218from other customers and from public Internet1254.

In some embodiments, cloud services1256can be called by the service gateway1236to access services that may not exist on public Internet1254, on the control plane VCN1216, or on the data plane VCN1218. The connection between cloud services1256and the control plane VCN1216or the data plane VCN1218may not be live or continuous. Cloud services1256may exist on a different network owned or operated by the IaaS provider. Cloud services1256may be configured to receive calls from the service gateway1236and may be configured to not receive calls from public Internet1254. Some cloud services1256may be isolated from other cloud services1256, and the control plane VCN1216may be isolated from cloud services1256that may not be in the same region as the control plane VCN1216. For example, the control plane VCN1216may be located in “Region 1,” and cloud service “Deployment 8,” may be located in Region 1 and in “Region 2.” If a call to Deployment 8 is made by the service gateway1236contained in the control plane VCN1216located in Region 1, the call may be transmitted to Deployment 8 in Region 1. In this example, the control plane VCN1216, or Deployment 8 in Region 1, may not be communicatively coupled to, or otherwise in communication with, Deployment 8 in Region 2.

FIG.7is a block diagram1300illustrating another example pattern of an IaaS architecture, according to at least one embodiment. Service operators1302(e.g. service operators1102) can be communicatively coupled to a secure host tenancy1304(e.g. the secure host tenancy1104) that can include a virtual cloud network (VCN)1306(e.g. the VCN1106) and a secure host subnet1308(e.g. the secure host subnet1108). The VCN1306can include an LPG1310(e.g. the LPG1110) that can be communicatively coupled to an SSH VCN1312(e.g. the SSH VCN1112) via an LPG1310contained in the SSH VCN1312. The SSH VCN1312can include an SSH subnet1314(e.g. the SSH subnet1114), and the SSH VCN1312can be communicatively coupled to a control plane VCN1316(e.g. the control plane VCN1116) via an LPG1310contained in the control plane VCN1316and to a data plane VCN1318(e.g. the data plane1118) via an LPG1310contained in the data plane VCN1318. The control plane VCN1316and the data plane VCN1318can be contained in a service tenancy1319(e.g. the service tenancy1119).

The control plane VCN1316can include a control plane DMZ tier1320(e.g. the control plane DMZ tier1120) that can include load balancer (“LB”) subnet(s)1322(e.g. LB subnet(s)1122), a control plane app tier1324(e.g. the control plane app tier1124) that can include app subnet(s)1326(e.g. similar to app subnet(s)1126), a control plane data tier1328(e.g. the control plane data tier1128) that can include DB subnet(s)1330. The LB subnet(s)1322contained in the control plane DMZ tier1320can be communicatively coupled to the app subnet(s)1326contained in the control plane app tier1324and to an Internet gateway1334(e.g. the Internet gateway1134) that can be contained in the control plane VCN1316, and the app subnet(s)1326can be communicatively coupled to the DB subnet(s)1330contained in the control plane data tier1328and to a service gateway1336(e.g. the service gateway) and a network address translation (NAT) gateway1338(e.g. the NAT gateway1138). The control plane VCN1316can include the service gateway1336and the NAT gateway1338.

The data plane VCN1318can include a data plane app tier1346(e.g. the data plane app tier1146), a data plane DMZ tier1348(e.g. the data plane DMZ tier1148), and a data plane data tier1350(e.g. the data plane data tier1150ofFIG.11). The data plane DMZ tier1348can include LB subnet(s)1322that can be communicatively coupled to trusted app subnet(s)1360and untrusted app subnet(s)1362of the data plane app tier1346and the Internet gateway1334contained in the data plane VCN1318. The trusted app subnet(s)1360can be communicatively coupled to the service gateway1336contained in the data plane VCN1318, the NAT gateway1338contained in the data plane VCN1318, and DB subnet(s)1330contained in the data plane data tier1350. The untrusted app subnet(s)1362can be communicatively coupled to the service gateway1336contained in the data plane VCN1318and DB subnet(s)1330contained in the data plane data tier1350. The data plane data tier1350can include DB subnet(s)1330that can be communicatively coupled to the service gateway1336contained in the data plane VCN1318.

The untrusted app subnet(s)1362can include one or more primary VNICs1364(1)-(N) that can be communicatively coupled to tenant virtual machines (VMs)1366(1)-(N). Each tenant VM1366(1)-(N) can be communicatively coupled to a respective app subnet1367(1)-(N) that can be contained in respective container egress VCNs1368(1)-(N) that can be contained in respective customer tenancies1370(1)-(N). Respective secondary VNICs1372(1)-(N) can facilitate communication between the untrusted app subnet(s)1362contained in the data plane VCN1318and the app subnet contained in the container egress VCNs1368(1)-(N). Each container egress VCNs1368(1)-(N) can include a NAT gateway1338that can be communicatively coupled to public Internet1354(e.g. public Internet1154).

The Internet gateway1334contained in the control plane VCN1316and contained in the data plane VCN1318can be communicatively coupled to a metadata management service1352(e.g. the metadata management system1152) that can be communicatively coupled to public Internet1354. Public Internet1354can be communicatively coupled to the NAT gateway1338contained in the control plane VCN1316and contained in the data plane VCN1318. The service gateway1336contained in the control plane VCN1316and contained in the data plane VCN1318can be communicatively couple to cloud services1356.

In some examples, the customer of the IaaS provider may grant temporary network access to the IaaS provider and request a function to be attached to the data plane tier app1346. Code to run the function may be executed in the VMs1366(1)-(N), and the code may not be configured to run anywhere else on the data plane VCN1318. Each VM1366(1)-(N) may be connected to one customer tenancy1370. Respective containers1371(1)-(N) contained in the VMs1366(1)-(N) may be configured to run the code. In this case, there can be a dual isolation (e.g., the containers1371(1)-(N) running code, where the containers1371(1)-(N) may be contained in at least the VM1366(1)-(N) that are contained in the untrusted app subnet(s)1362), which may help prevent incorrect or otherwise undesirable code from damaging the network of the IaaS provider or from damaging a network of a different customer. The containers1371(1)-(N) may be communicatively coupled to the customer tenancy1370and may be configured to transmit or receive data from the customer tenancy1370. The containers1371(1)-(N) may not be configured to transmit or receive data from any other entity in the data plane VCN1318. Upon completion of running the code, the IaaS provider may kill or otherwise dispose of the containers1371(1)-(N).

In some embodiments, the trusted app subnet(s)1360may run code that may be owned or operated by the IaaS provider. In this embodiment, the trusted app subnet(s)1360may be communicatively coupled to the DB subnet(s)1330and be configured to execute CRUD operations in the DB subnet(s)1330. The untrusted app subnet(s)1362may be communicatively coupled to the DB subnet(s)1330, but in this embodiment, the untrusted app subnet(s) may be configured to execute read operations in the DB subnet(s)1330. The containers1371(1)-(N) that can be contained in the VM1366(1)-(N) of each customer and that may run code from the customer may not be communicatively coupled with the DB subnet(s)1330.

In other embodiments, the control plane VCN1316and the data plane VCN1318may not be directly communicatively coupled. In this embodiment, there may be no direct communication between the control plane VCN1316and the data plane VCN1318. However, communication can occur indirectly through at least one method. An LPG1310may be established by the IaaS provider that can facilitate communication between the control plane VCN1316and the data plane VCN1318. In another example, the control plane VCN1316or the data plane VCN1318can make a call to cloud services1356via the service gateway1336. For example, a call to cloud services1356from the control plane VCN1316can include a request for a service that can communicate with the data plane VCN1318.

FIG.8is a block diagram1400illustrating another example pattern of an IaaS architecture, according to at least one embodiment. Service operators1402(e.g. service operators1102) can be communicatively coupled to a secure host tenancy1404(e.g. the secure host tenancy1104) that can include a virtual cloud network (“VCN”)1406(e.g. the VCN1106) and a secure host subnet1408(e.g. the secure host subnet1108). The VCN1406can include an LPG1410(e.g. the LPG1110) that can be communicatively coupled to an SSH VCN1412(e.g. the SSH VCN1112) via an LPG1410contained in the SSH VCN1412. The SSH VCN1412can include an SSH subnet1414(e.g. the SSH subnet1114), and the SSH VCN1412can be communicatively coupled to a control plane VCN1416(e.g. the control plane VCN1116) via an LPG1410contained in the control plane VCN1416and to a data plane VCN1418(e.g. the data plane1118) via an LPG1410contained in the data plane VCN1418. The control plane VCN1416and the data plane VCN1418can be contained in a service tenancy1419(e.g. the service tenancy1119).

The control plane VCN1416can include a control plane DMZ tier1420(e.g. the control plane DMZ tier1120) that can include LB subnet(s)1422(e.g. LB subnet(s)1122), a control plane app tier1424(e.g. the control plane app tier1124) that can include app subnet(s)1426(e.g. app subnet(s)1126), a control plane data tier1428(e.g. the control plane data tier1128) that can include DB subnet(s)1430(e.g. DB subnet(s)1330). The LB subnet(s)1422contained in the control plane DMZ tier1420can be communicatively coupled to the app subnet(s)1426contained in the control plane app tier1424and to an Internet gateway1434(e.g. the Internet gateway1134) that can be contained in the control plane VCN1416, and the app subnet(s)1426can be communicatively coupled to the DB subnet(s)1430contained in the control plane data tier1428and to a service gateway1436(e.g. the service gateway ofFIG.11) and a network address translation (NAT) gateway1438(e.g. the NAT gateway1138ofFIG.11). The control plane VCN1416can include the service gateway1436and the NAT gateway1438.

The data plane VCN1418can include a data plane app tier1446(e.g. the data plane app tier1146), a data plane DMZ tier1448(e.g. the data plane DMZ tier1148), and a data plane data tier1450(e.g. the data plane data tier1150). The data plane DMZ tier1448can include LB subnet(s)1422that can be communicatively coupled to trusted app subnet(s)1460(e.g. trusted app subnet(s)1360) and untrusted app subnet(s)1462(e.g. untrusted app subnet(s)1362) of the data plane app tier1446and the Internet gateway1434contained in the data plane VCN1418. The trusted app subnet(s)1460can be communicatively coupled to the service gateway1436contained in the data plane VCN1418, the NAT gateway1438contained in the data plane VCN1418, and DB subnet(s)1430contained in the data plane data tier1450. The untrusted app subnet(s)1462can be communicatively coupled to the service gateway1436contained in the data plane VCN1418and DB subnet(s)1430contained in the data plane data tier1450. The data plane data tier1450can include DB subnet(s)1430that can be communicatively coupled to the service gateway1436contained in the data plane VCN1418.

The untrusted app subnet(s)1462can include primary VNICs1464(1)-(N) that can be communicatively coupled to tenant virtual machines (VMs)1466(1)-(N) residing within the untrusted app subnet(s)1462. Each tenant VM1466(1)-(N) can run code in a respective container1467(1)-(N), and be communicatively coupled to an app subnet1426that can be contained in a data plane app tier1446that can be contained in a container egress VCN1468. Respective secondary VNICs1472(1)-(N) can facilitate communication between the untrusted app subnet(s)1462contained in the data plane VCN1418and the app subnet contained in the container egress VCN1468. The container egress VCN can include a NAT gateway1438that can be communicatively coupled to public Internet1454(e.g. public Internet1154).

The Internet gateway1434contained in the control plane VCN1416and contained in the data plane VCN1418can be communicatively coupled to a metadata management service1452(e.g. the metadata management system1152) that can be communicatively coupled to public Internet1454. Public Internet1454can be communicatively coupled to the NAT gateway1438contained in the control plane VCN1416and contained in the data plane VCN1418. The service gateway1436contained in the control plane VCN1416and contained in the data plane VCN1418can be communicatively couple to cloud services1456.

In some examples, the pattern illustrated by the architecture of block diagram1400ofFIG.7may be considered an exception to the pattern illustrated by the architecture of block diagram1300ofFIG.6and may be desirable for a customer of the IaaS provider if the IaaS provider cannot directly communicate with the customer (e.g., a disconnected region). The respective containers1467(1)-(N) that are contained in the VMs1466(1)-(N) for each customer can be accessed in real-time by the customer. The containers1467(1)-(N) may be configured to make calls to respective secondary VNICs1472(1)-(N) contained in app subnet(s)1426of the data plane app tier1446that can be contained in the container egress VCN1468. The secondary VNICs1472(1)-(N) can transmit the calls to the NAT gateway1438that may transmit the calls to public Internet1454. In this example, the containers1467(1)-(N) that can be accessed in real-time by the customer can be isolated from the control plane VCN1416and can be isolated from other entities contained in the data plane VCN1418. The containers1467(1)-(N) may also be isolated from resources from other customers.

In other examples, the customer can use the containers1467(1)-(N) to call cloud services1456. In this example, the customer may run code in the containers1467(1)-(N) that requests a service from cloud services1456. The containers1467(1)-(N) can transmit this request to the secondary VNICs1472(1)-(N) that can transmit the request to the NAT gateway that can transmit the request to public Internet1454. Public Internet1454can transmit the request to LB subnet(s)1422contained in the control plane VCN1416via the Internet gateway1434. In response to determining the request is valid, the LB subnet(s) can transmit the request to app subnet(s)1426that can transmit the request to cloud services1456via the service gateway1436.

As disclosed, embodiments detect data draft at a feature store and, if detected, prevent the drifted data to be used to train a model. Further, embodiments detect data drift for a trained model, and if detected, prevent the trained model from providing predictions in response to inference requests.

The features, structures, or characteristics of the disclosure described throughout this specification may be combined in any suitable manner in one or more embodiments. For example, the usage of “one embodiment,” “some embodiments,” “certain embodiment,” “certain embodiments,” or other similar language, throughout this specification refers to the fact that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the present disclosure. Thus, appearances of the phrases “one embodiment,” “some embodiments,” “a certain embodiment,” “certain embodiments,” or other similar language, throughout this specification do not necessarily all refer to the same group of embodiments, and the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

One having ordinary skill in the art will readily understand that the embodiments as discussed above may be practiced with steps in a different order, and/or with elements in configurations that are different than those which are disclosed. Therefore, although this disclosure considers the outlined embodiments, it would be apparent to those of skill in the art that certain modifications, variations, and alternative constructions would be apparent, while remaining within the spirit and scope of this disclosure. In order to determine the metes and bounds of the disclosure, therefore, reference should be made to the appended claims.