ADAPTIVE RETRAINING OF AN ARTIFICIAL INTELLIGENCE MODEL BY DETECTING A DATA DRIFT, A CONCEPT DRIFT, AND A MODEL DRIFT

A computer-implemented method, a computer program product, and a computer system for adaptive retraining of an artificial intelligence model. A computer system computes drift magnitude scores for respective drift functions. A computer system computes an aggregated data drift score for a data drift, an aggregated concept drift score for a concept drift, and an aggregated model drift score for a model drift. A computer system computes an overall drift score, based on the aggregated data drift score, the aggregated concept drift score, the aggregated model drift score, a predetermined data drift threshold, a predetermined concept drift threshold, and a predetermined model drift threshold. A computer system determines whether retraining of the artificial intelligence model is required, based on the overall drift score. A computer system performs the retraining of the artificial intelligence model, in response to determining the retraining of the artificial intelligence model is required.

BACKGROUND

The present invention relates generally to artificial intelligence models, and more particularly to adaptive retraining of an artificial intelligence model by detecting a data drift, a concept drift, and a model drift.

In enterprise artificial intelligence (AI) systems, specifically in industrial Internet of Things (IoT) systems, multiple AI models are prepared and deployed to fulfill business requirements. Monitoring the behavior of these AI models and retrain as the model performance degrades by identifying suitable retraining data is essential to the business. Analysis of the models alone is not sufficient to decide on whether to retrain or to select data for the retraining. It is also required to identify changes in input data (features) and targets (ground truth) to perform retraining. Although various algorithms exist to identify various types of drifts, e.g., a data drift, a concept (target) drift, and a model drift, a system that enables an automated analysis of these drifts and the relationship between them is required for business personnel.

SUMMARY

In one aspect, a computer-implemented method for adaptive retraining of an artificial intelligence model is provided. The computer-implemented method includes computing drift magnitude scores for respective drift functions. The computer-implemented method further includes computing an aggregated data drift score for a data drift, an aggregated concept drift score for a concept drift, and an aggregated model drift score for a model drift. The computer-implemented method includes computing an overall drift score, based on the aggregated data drift score, the aggregated concept drift score, the aggregated model drift score, a predetermined data drift threshold, a predetermined concept drift threshold, and a predetermined model drift threshold. The computer-implemented method includes determining whether retraining of the artificial intelligence model is required, based on the overall drift score. The computer-implemented method includes performing the retraining of the artificial intelligence model, in response to determining the retraining of the artificial intelligence model is required.

In another aspect, a computer program product for adaptive retraining of an artificial intelligence model is provided. The computer program product comprises a computer readable storage medium having program instructions embodied therewith, and the program instructions are executable by one or more processors. The program instructions are executable to: compute drift magnitude scores for respective drift functions; compute an aggregated data drift score for a data drift, an aggregated concept drift score for a concept drift, and an aggregated model drift score for a model drift; compute an overall drift score, based on the aggregated data drift score, the aggregated concept drift score, the aggregated model drift score, a predetermined data drift threshold, a predetermined concept drift threshold, and a predetermined model drift threshold; determine whether retraining of the artificial intelligence model is required, based on the overall drift score; and perform the retraining of the artificial intelligence model, in response to determining the retraining of the artificial intelligence model is required.

In yet another aspect, a computer system for adaptive retraining of an artificial intelligence model is provided. The computer system comprises one or more processors, one or more computer readable tangible storage devices, and program instructions stored on at least one of the one or more computer readable tangible storage devices for execution by at least one of the one or more processors. The program instructions are executable to compute drift magnitude scores for respective drift functions. The program instructions are further executable to compute an aggregated data drift score for a data drift, an aggregated concept drift score for a concept drift, and an aggregated model drift score for a model drift. The program instructions are further executable to compute an overall drift score, based on the aggregated data drift score, the aggregated concept drift score, the aggregated model drift score, a predetermined data drift threshold, a predetermined concept drift threshold, and a predetermined model drift threshold. The program instructions are further executable to determine whether retraining of the artificial intelligence model is required, based on the overall drift score. The program instructions are further executable to perform the retraining of the artificial intelligence model, in response to determining the retraining of the artificial intelligence model is required.

DETAILED DESCRIPTION

An AI system includes input data, predictive model and parameters, model predictions, ground truth, and various types of drift functions to analyze the data elements and drift thresholds.FIG.1is a diagram illustrating a problem setting of predictive model110, in accordance with one embodiment of the present invention. Input sensor data (X)120is generated in periodic interval from multiple sensors installed in various assets and operations. Feature extracted data (X′)130is prepared from timeseries of input sensor data (X)120using feature extraction techniques. Predictive model or AI model (M)110may use either input sensor data (X)120or feature extracted data (X′)130for training. Predictive model or AI model (M)110is trained on input sensor data (X)120or feature extracted data (X′)130and outputs predictions (Ŷ)140in example 1 and/or predictions (Ŷ)150in example 2 for all the subsequent input data. In example 1, predictions (Ŷ)140is classification output; in example 2, predictions (Ŷ)150is regression output. In example 1, ground truth (Y)160is available for predictive model (M)110; in example 2, ground truth (Y)170is available for predictive model (M)110.

The AI system has following drifts. The data drift is a drift in input data. The analysis of the data drift requires only the input data, e.g., timeseries of input sensor data (X)120or feature extracted data (X′)130. The concept drift is a drift in target labels (e.g., ground truth (Y)160or ground truth (Y)170) or the relationship between target labels and input data. The model drift is a drift in predictions of predictive model or AI model (M)110, the relationship between the predictions (e.g., predictions (Ŷ)140and/or predictions (Ŷ)150) and the target labels, or the relationship between the input data, the predictions, and the target labels. The analysis of the model drift may include comparison of model parameters or optimization parameters emitted during model preparation. A client model (such as predictive model (M)110) is required in order to analyze the model drift.

Although the data drift, the concept drift, and the model drift have been explored individually, an automated system, which simultaneously considers all these drift types to perform retraining, does not exist. The data drift has been studied without analyzing model quality. In the data drift driven adaptive retraining, if the drift in input data is identified, a predictive model is retrained anticipating a drift in model performance. The data drift driven adaptive retraining has following drawbacks: (1) The predictive model is retrained without confirming a drift in model predictions. (2) A drift in ground truth (or the concept drift) is not addressed. (3) There is no mechanism to accommodate multiple drift functions from other drift types. Although multiple definitions of the concept drift exist, concept drift driven adaptive retraining, in majority of the cases, is to analyze the target variable. Drawbacks of the concept drift driven adaptive retraining are as follows: (1) The concept drift alone is not the reason for model degradation. (2) There is no mechanism to accommodate multiple drift functions from other drift types. In the quality driven adaptive retraining, model quality is observed over a time period and the abrupt model drift is identified using moving average techniques. In identifying the model quality, model predictions against ground truth are analyzed. The quality driven adaptive retraining has following drawbacks: (1) The reason for the model drift is not explained. (2) There is no mechanism to accommodate multiple drift functions. (3) There is no mechanism or method to anticipate possible model degradation prior to happening. (4) The drift in ground truth (or the concept drift) is not addressed as well.

Embodiments of the present invention disclose a system and method for quantifying different types of drifts in training artificial intelligence (AI) models, e.g., the data drift, the concept drift, and the model drift, using proper drift functions/algorithms. The disclosed system and method detect the drifts and notify users to retrain the AI models when required, using combined analysis of the above drifts. The disclosed system and method select appropriate data required for retraining using the above analysis and perform retraining of the AI model.

In embodiments of the present invention, the disclosed system and method train a model on given training data, deploy the model in production, monitor the model over the time, and retrain the model as required. The disclosed system comprises two major modules: a model training module and an adaptive retraining module. The disclosed system and method are implemented on one or more computing devices or servers. A computing device or server is described in more detail in later paragraphs with reference toFIG.8. The disclosed system and method may be implemented in a cloud computing environment. The cloud computing environment is described in more detail in later paragraphs with reference toFIG.9andFIG.10.

The model training module trains and deploys a predictive model or AI model. From a pool of pre-selected models, the model training module chooses a best model suitable for a given dataset and a problem, using manual analysis or automated selection such as optimization-based selection and grid search. The model training module determines model parameters for the best model, using hyperparameter search. The model training module trains the best model using training data and ground truth. Part of the training data may be used as validation data. The model training module deploys the best model in production. Test data is scored using the deployed best model and predictions are obtained by the model training module. The deployed best model is selected to be monitored by the adaptive retrain module. The implementation of the adaptive retrain module includes two phases: phase 1—computing retraining score, and phase 2—identifying required retraining data and creating a new AI model.

Embodiments of the present invention disclose an automated system that perform adaptive retraining by analyzing the data drift, the concept drift, and the model drift together. In embodiments of the present invention, an overall drifting score or a retraining score, which considers the relationship between the data drift, the concept drift, and the model drift, is computed. Embodiments of the present invention develop a 3-level drift scoring technique. First, a score for each drift function that resembles magnitude of change occurred in the data is computed. Second, a score for each of the data drift, the concept drift, and the model drift by aggregation of scores from the respective drift functions. Third, the overall drift score (or the retraining score) is computed by considering the second-level scores, i.e., an aggregated data drift score, an aggregated concept drift score, and an aggregated model drift score. Embodiments of the present invention select appropriate retraining data by a combination of sampling and relabeling techniques, based on the 3-level drift scores. Embodiments of the present invention allow users to take an informed decision in automated manner using combined analysis of data, concept, and model drifts.

In embodiments of the present invention, any algorithm which is used to identify a drift in input data, a drift in ground truth, a drift in model predictions, or relationship among them is called a drift function. Based on the type of data that the algorithm considers, the drift function is categorized into one of the categories: the data drift functions, the concept drift functions, and the model drift functions. The proposed system and method re-categorize all the available drift functions ƒ1, ƒ2, ƒ3, . . . ƒninto data drift functions, concept drift functions, and model drift functions as per drift definitions. Drift thresholds, including a drift threshold (τƒi) for a drift function ƒi, a data drift threshold (τdd), a concept drift threshold (τcd), and a model drift threshold (τmd), can be set either by predefined values or by users.

In computation of the 3-level drift scores, the inputs include a batch of test data, model predictions, and ground truth. The outputs in the first-level drift score computation include a drift magnitude score (sƒi) and a drift flag ζƒifor a drift function ƒi(i=1, . . . , n). The outputs in the second-level drift score computation include an aggregated data drift score (sdd) and its drift flag (ζdd), an aggregated concept drift score (scd) and its drift flag (ζcd), and an aggregated model drift score (ζmd) and its drift flag (ζmd). The outputs in the third-level drift score computation include a retraining score (s) and a retraining flag ζ. The retraining score (s) may also called as an overall drift score or a total drift score. In computation of the 3-level drift scores, the data elements from the model training phase, i.e., training data, validation data, model predictions for training or validation data, ground truth, model parameters, optimization statistics, etc., can be used as a base in the adaptive retrain module. Computation of the 3-level drift scores will be discussed in detail in later paragraphs of this document.

In the third-level drift score computation in phase 1 (computing retrain score) mentioned above, the retraining score (s) and retraining flag ζ are obtained. Based on the retraining score (s) and retraining flag ζ, the disclosed system and method determine whether retraining of predictive model or AI model is required. If the retraining flag ζ is true, the disclosed system and method perform the retraining.

In the second-level drift score computation in phase 1 (computing retrain score) mentioned above, the aggregated data drift score (sdd), the data drift flag (ζdd), the aggregated concept drift score (scd), the concept drift flag (ζcd), the aggregated model drift score (smd), and the model drift flag (ζcd) are obtained. In response to determining that the data drift flag (ζdd) is true, the disclosed system and method determines that the data drift is present. In response to determining that the concept drift flag (ζcd) is true, the disclosed system and method determines that the concept drift is present. In response to determining that the model drift flag (ζcd) is true, the disclosed system and method determine that the model drift is present.

In phase 2 (identifying required retraining data and creating a new AI model) mentioned above, in response to determining that the data drift is present, the disclosed system and method select new training data from the drifted period using known sampling techniques. In response to determining that the data drift is not present, the disclosed system and method use the original training data. Different strategies can be used to prepare new training data from the drifted period.

In phase 2 (identifying required retraining data and creating a new AI model) mentioned above, in response to determining that the concept drift is present, the disclosed system and method apply known relabeling techniques on the selected training data. The disclosed system and method may take new label definitions as inputs or deduce new label definitions from the drifted data.

In phase 2 (identifying required retraining data and creating a new AI model) mentioned above, in response to determining that the model drift is present and either the concept drift or the data drift is present, model selection is ignored by the disclosed system and method. Otherwise, the disclosed system and method perform model selection to select a new AI model. Finally, hyperparameter search is performed on the selected new AI model.

The disclosed system and method may replace the previously deployed AI model with the new AI model. Alternatively, the disclosed system and method may compare both the previously deployed AI model with the new AI model against each other using drift results over future period of data, and then the disclosed system and method select a winner as the production model.

Computation of the first-level drift scores is as follows. A drift function ƒihas a threshold τƒi(>0) set to either a user provided value or a default value. ƒicomputes metrics for a given batch of data either by comparing against training data or by comparing against recent data over a window or any other means. Example of a metric is standard deviation (std). Finally, a drift magnitude score (sƒi) and a drift flag (ζƒi) is computed for ƒi. For example, the drift magnitude score (sƒi) and drift flag (ζƒi) are computed by

The drift magnitude score (sƒi) and drift flag (ζƒi) are computed for all data drift functions, concept drift functions, and model drift functions ƒ1, ƒ2, ƒ3, . . . ƒn.

Computation of the second-level drift scores is as follows. Each of the data drift, the concept drift, and the model drift has at least one drift function. The data drift, the concept drift, and the model drift have thresholds τdd, τcd, and τmd, respectively; the thresholds can be set either by predefined values or by users. For example, the data drift has drift function ƒ1, ƒ2, . . . ƒmwhich evaluate either univariate or multivariate input data. The aggregated data drift score (sdd) and the data drift flag (ζdd) are computed from drift magnitude scores and drift flags corresponding to all the data drift functions ƒ1, ƒ2, . . . ƒm. For example, the aggregated data drift score (sdd) and the data drift flag (ζdd) are computed by

The aggregated concept drift score (scd), the concept drift flag (ζcd), the aggregated model drift score (smd), and model drift flag (ζmd) are computed in a similar way as the aggregated data drift score (sdd) and the data drift flag (ζdd).

Computation of the third-level drift scores is as follows. The retraining score or overall drift score (s) and the retraining flag ζ are computed based on the aggregated data drift score (sdd) and its drift flag (ζdd), the aggregated concept drift score (scd) and its drift flag (ζcd), and the aggregated model drift score (smd) and its drift flag (ζmd). The aggregated data drift score (sdd), the aggregated concept drift score (scd), and the aggregated model drift score (smd) can be aggregated values over a period. An example of aggregation is the average of the values. For example, the retraining score or overall drift score (s) and the retraining flag ζ are computed by

The above computation is deduced from an approximated relationship between various kinds of drifts.

FIG.2is a flowchart showing operational steps of adaptive retraining of an artificial intelligence model by detecting a data drift, a concept drift, and a model drift, in accordance with one embodiment of the present invention. The operational steps presented inFIG.2are implemented by the disclosed system which is hosted by one or more computing devices or servers. A computing device or server is described in more detail in later paragraphs with reference toFIG.8. The operational steps presented inFIG.2may be implemented in a cloud computing environment. The cloud computing environment is described in more detail in later paragraphs with reference toFIG.9andFIG.10.

At step201, the one or more computing devices or servers select an artificial intelligence (AI) model for a dataset and a problem. From a pool of pre-selected models, the one or more computing devices or servers choose a best model suitable for the given dataset and the problem. At step202, the one or more computing devices or servers determine parameters for the AI model using hyperparameter search. At step203, the one or more computing devices or servers train the AI model using training data and ground truth. At step204, the one or more computing devices or servers deploy the AI model and obtain predictions using the AI model. Steps201-204are operational steps in the model training phase. The data elements from the model training phase, including training data, validation data, model predictions for training or validation data, ground truth, model parameters, and optimization statistics, can be used to compute 3-level drift scores in the following steps.

At step205, the one or more computing devices or servers compute drift magnitude scores for respective drift functions. Drift functions ƒ1, ƒ2, ƒ3, . . . ƒnare available, each of which is an algorithm used to identify a drift in input data, a drift in ground truth, a drift in model predictions, or a relationship among them. The one or more computing devices or servers categorize all the available drift functions ƒ1, ƒ2, ƒ3, . . . ƒninto data drift functions, concept drift functions, and model drift functions. For a drift function ƒ1, the one or more computing devices or servers compute a drift magnitude score (sƒi), using equation 1 described in a previous paragraph in this document. For a drift function ƒi, the one or more computing devices or servers also compute a drift flag (ζƒi), using equation 2 described in a previous paragraph in this document. At step205, the one or more computing devices or servers compute the first-level drift scores.

At step206, the one or more computing devices or servers compute an aggregated data drift score, an aggregated concept drift score, and an aggregated model drift score. Based on the drift magnitude score (sƒi) and the drift flag (ζƒi) (the first-level drift scores) computed at step205, the one or more computing devices or servers compute the aggregated data drift score (sdd), using equation 3 described in a previous paragraph in this document. The one or more computing devices or servers at step206also compute the data drift flag (ζdd), using equation 4 described in a previous paragraph in this document, based on the aggregated data drift score (sdd) and a predetermined threshold (τdd) of the data shift. Similarly, at step206, the one or more computing devices or servers compute the aggregated concept drift score (scd), the concept drift flag (ζcd), the aggregated model drift score (smd), and model drift flag (ζcd). At step206, the one or more computing devices or servers compute the second-level drift scores.

At step207, the one or more computing devices or servers compute a retraining score (or an overall drift score), based on the aggregated data drift score, the aggregated concept drift score, and the aggregated model drift score. Based on the aggregated data drift score (sdd), the aggregated concept drift score (scd), and aggregated model drift score (smd) that are computed at step206, and also based on the predetermined threshold (τdd) of the data shift, the predetermined threshold (τcd) of the concept shift, and the predetermined threshold (rind) of the model drift, the one or more computing devices or servers compute the retraining score or overall drift score (s), using equations 5-9 described in a previous paragraph in this document. At step207, the one or more computing devices or servers also compute a retraining flag (ζ), using equation 10 described in a previous paragraph in this document. At step207, the one or more computing devices or servers compute the third-level drift scores.

At step208, the one or more computing devices or servers determine whether retaining of the AI model is required, based on the retraining score (the retraining score may also called as an overall drift score or a total drift score). To determine whether the retaining of the AI model is required, the one or more computing devices or servers determines whether the retraining flag ζ is true or false. The retraining score or overall drift score (s) and retraining flag ζ have been computed at step207by the one or more computing devices or servers.

In response to determining that the retaining the AI model is not required (NO branch of decision block209), the one or more computing devices or servers does not perform the retraining of the AI model. In other words, in response to determining that the retraining flag ζ is false, the one or more computing devices or servers will not take any action to retrain the AI model.

In response to determining that retaining the AI model is required (YES branch of decision block209), at step210, the one or more computing devices or servers perform the retraining of the AI model. In other words, in response to determining that the retraining flag ζ is true, the one or more computing devices or servers perform the retraining.

FIG.3is a flowchart showing operational steps of adaptive retraining of an artificial intelligence model by detecting a data drift, a concept drift, and a model drift, in accordance with another embodiment of the present invention. The operational steps presented inFIG.3are implemented by the disclosed system which is hosted by one or more computing devices or servers. A computing device or server is described in more detail in later paragraphs with reference toFIG.8. The operational steps may be implemented in a cloud computing environment. The cloud computing environment is described in more detail in later paragraphs with reference toFIG.9andFIG.10.

The aggregated data drift score, aggregated concept drift score, and aggregated model drift score have been computed at step206by the one or more computing devices or servers. The aggregated data drift score, aggregated concept drift score, and aggregated model drift score are used for the operating steps presented inFIG.3.

At step301, the one or more computing devices or servers determine whether the data drift is present. To determine whether the data drift is present, the one or more computing devices or servers determine whether the data drift flag (ζdd) (which is computed at step206) is true or false. If the data drift flag (ζdd) is true, the data drift is present; if the data drift flag (ζdd) is false, the data drift is not present.

In response to determining that the data drift is present or the data drift flag (ζdd) is true (YES branch of decision block301), at step302, the one or more computing devices or servers use new training data for the retraining of the AI model. The new training data may be an entirely new training dataset. The new training data may be combination of the drifted training data and non-drifted training data (or the one or more computing devices or servers incorporate the drifted training data into new data). Selecting the new training data from the drifted period uses known sampling techniques.

In response to determining that the data drift is not present or the drift flag (ζdd) is false (NO branch of decision block301), at step303, the one or more computing devices or servers use original data which has been used in the model training phase.

After either step302or step303, the one or more computing devices or servers at step304determine whether the concept drift is present. To determine whether the concept drift is present, the one or more computing devices or servers determine whether the concept drift flag (ζcd) (which is computed at step206) is true or false. If the concept drift flag (ζdd) is true, the concept drift is present; if the concept drift flag (ζcd) is false, the concept drift is not present.

In response to determining that the concept drift is present or the concept drift flag (ζcd) is true (YES branch of decision block304), at step305, the one or more computing devices or servers incorporate drifted targets into a dataset for the retraining of the AI model.

In response to determining that the concept drift is not present or the concept drift flag (ζcd) is false (NO branch of decision block304), at step306, the one or more computing devices or servers determine whether the model drift is present. To determine whether the model drift is present, the one or more computing devices or servers determine whether the model drift flag (ζcd) (which is computed at step206) is true or false. If the model drift flag (ζcd) is true, the model drift is present; if the model drift flag (ζcd) is false, the model drift is not present.

In response to determining that the concept drift is present or the model drift flag (ζcd) is true (YES branch of decision block306), at step307, the one or more computing devices or servers determine whether either data drift or the concept drift is present. In response to determining that the concept drift is not present or the model drift flag (ζcd) is false (NO branch of decision block306), at step308, the one or more computing devices or servers determine whether the concept drift is present.

In response to determining that one of the data drift and the concept drift is present (YES branch of decision block307), the one or more computing devices or servers at step310perform hyperparameter search. In response to determining that neither the data drift nor the concept drift is present (NO branch of decision block307), the one or more computing devices or servers at step309perform model selection and then perform hyperparameter search.

In response to determining that the concept drift is present (YES branch of decision block308), the one or more computing devices or servers at step309perform model selection and then perform hyperparameter search. In response to determining that the concept drift is not present (NO branch of decision block308), the one or more computing devices or servers will not take any action to retrain the AI model.

FIG.4illustrates an example of relationships between a data drift score, a concept drift score, a model drift score, and an overall drift score, in accordance with another embodiment of the present invention. As shown in the first row inFIG.4, when a data drift, a concept drift, and a model drift are present (i.e., data drift flag (ζdd), concept drift flag (ζcd), and model flag (ζmd) are true) and when the overall drift score (which is calculated based on the aggregated data drift score, the aggregated concept drift score, the aggregated model drift score) indicates that retraining is required, the disclosed system and method incorporate drifted input data and drifted targets into new training data, and the disclosed system and method perform hyperparameter search.

As shown in the second row inFIG.4, when a data drift and a concept drift are present (i.e., data drift flag (ζdd) and concept drift flag (ζcd) are true), when a model drift is not present (model drift flag (ζmd) is false), and when the overall drift score indicates that retraining is required, the disclosed system and method incorporate drifted input data and drifted targets into new training data, the disclosed system and method perform new model selection.

As shown in the third row inFIG.4, when a data drift and a model drift are present (i.e., data drift flag (ζdd) and model drift flag (ζmd) are true), when a concept drift is not present (concept drift flag (ζcd) is false), and when the overall drift score indicates that retraining is required, the disclosed system and method incorporate drifted input data into new training data and perform hyperparameter search.

As shown in the fourth row inFIG.4, when a data drift is not present (data drift flag (ζdd) is false), when a concept drift and a model drift are present (concept drift flag (ζcd), and model drift flag (ζmd) are true), and when the overall drift score indicates that retraining is required, the disclosed system and method incorporate drifted targets into new training data and perform hyperparameter search.

As shown in the fifth row inFIG.4, when a data drift and a model drift are not present (i.e., data drift flag (ζdd) and model flag (ζmd) are false), when a concept drift is present (concept drift flag (ζcd) is true), and when the overall drift score indicates that retraining is required, the disclosed system and method perform new model selection and retrain the new model with new training data along with target relabeling.

As shown in the sixth row inFIG.4, when a data drift and a concept drift are not present (i.e., data drift flag (ζdd) and concept flag (ζcd) are false), when a model drift is present (model drift flag (ζmd) is true), and when the overall drift score indicates that retraining is required, it is indicated that the present deployed model may not be robust. Therefore, the disclosed system and method perform new model selection; for example, the disclosed system and method select a different model from pipeline optimization.

As shown in the seventh row inFIG.4, when a data drift is present (i.e., data drift flag (ζdd) is true), when a concept drift and a model drift are not present (concept drift flag (ζcd) and model drift flag (ζmd) are false), and when the overall drift score indicates that retraining is not required, the disclosed system and method issue a warning of the data drift.

As shown in the eighth row inFIG.4, when a data drift, a concept drift, and a model drift are not present (i.e., data drift flag (ζdd), concept drift flag (ζcd), and model flag (ζmd) are false) and when the overall drift score indicates that retraining is not required, it is indicated that the currently deployed model is in a good shape. Therefore, the disclosed system and method do not take any action for adaptive retraining.

FIG.5is a diagram illustrating a first example of adaptive retraining of an artificial intelligence model by detecting a data drift, a concept drift, and a model drift, in accordance with another embodiment of the present invention. An AI model M0is trained on a historic training data, by using model selection and hyperparameter search. M0is deployed and configured for adaptive retraining with following thresholds: data drift threshold τdd=0.7, concept drift threshold τcd=0.8, and model drift threshold τmd=0.9. Upon arrival of the first batch of production data and when ground truth is available, drift algorithms are applied on the model M0and the data. The drift scores are computed and obtained as follows: aggregated data drift score sdd=0.8, aggregated concept drift score scd=0.5, aggregated model drift score smd=1.2, and overall drift score (or retraining score) s=1. Further, the computation of drift scores gives data drift flag ζdd=True, concept drift flag ζcd=False, model drift flag ζmd=True, and retraining flag ζ=True. Based on the drift scores and drift flags, M0is set to retrain and new train data is selected using known sampling techniques. A new AI model M1 is created after hyperparameter search.

FIG.6is a diagram illustrating a second example of adaptive retraining of an artificial intelligence model by detecting a data drift, a concept drift, and a model drift, in accordance with another embodiment of the present invention. An AI model M0is trained on a historic training data, by using model selection and hyperparameter search. M0is deployed and configured for adaptive retraining with following thresholds: data drift threshold τdd=0.7, concept drift threshold τcd=0.8, and model drift threshold τmd=0.9. Upon arrival of the first batch of production data and when ground truth is available, drift algorithms are applied on the model M0and the data. The drift scores are computed and obtained as follows: aggregated data drift score sdd=0.6, aggregated concept drift score scd=0.9, aggregated model drift score smd=1.2, and overall drift score (or retraining score) s=1. Further, the computation of drift scores gives data drift flag ζdd=False, concept drift flag ζcd=True, model drift flag ζmd=True, and retraining flag ζ=True. Based on the drift scores and drift flags, M0is set to retrain and new train data is selected after relabeling the ground truth as per new definitions. A new AI model M1is created after hyperparameter search.

FIG.7is a diagram illustrating a third example of adaptive retraining of an artificial intelligence model by detecting a data drift, a concept drift, and a model drift, in accordance with another embodiment of the present invention. An AI model M0is trained on a historic training data, by using model selection and hyperparameter search. M0is deployed and configured for adaptive retraining with following thresholds: data drift threshold τdd=0.7, concept drift threshold τcd=0.8, and model drift threshold τmd=0.9. Upon arrival of the first batch of production data and when ground truth is available, drift algorithms are applied on the model M0and the data. The drift scores are computed and obtained as follows: aggregated data drift score sdd=0.6, aggregated concept drift score scd=0.7, aggregated model drift score smd=0.8, and overall drift score (or retraining score) s=0.8. Further, the computation of drift scores gives data drift flag ζdd=False, concept drift flag ζcd=False, model drift flag ζmd=False, and retraining flag ζ=False. Based on the drift scores and drift flags, retraining is not required.

FIG.8is a diagram illustrating components of computing device or server800, in accordance with one embodiment of the present invention, in accordance with one embodiment of the present invention. It should be appreciated thatFIG.8provides only an illustration of one implementation and does not imply any limitations; different embodiments may be implemented.

Referring toFIG.8, computing device or server800includes processor(s)820, memory810, and tangible storage device(s)830. InFIG.8, communications among the above-mentioned components of computing device or server800are denoted by numeral890. Memory810includes ROM(s) (Read Only Memory)811, RAM(s) (Random Access Memory)813, and cache(s)815. One or more operating systems831and one or more computer programs833reside on one or more computer readable tangible storage device(s)830.

Computing device or server800further includes I/O interface(s)850. I/O interface(s)850allows for input and output of data with external device(s)860that may be connected to computing device or server800. Computing device or server800further includes network interface(s)840for communications between computing device or server800and a computer network.

Characteristics are as follows:

Service Models are as follows:

Deployment Models are as follows:

Workloads layer90provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation91; software development and lifecycle management92; virtual classroom education delivery93; data analytics processing94; transaction processing95; and function96. Function96in the present invention is the functionality of adaptive retraining of an artificial intelligence model by detecting data drift, concept drift, and model drift.