# US 2021/0241152 A1 · System and method for a personalized machine learning pipeline selection and result interpretation

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- Number: US 2021/0241152 A1
- Document ID: US20210241152A1
- Status: Published application
- Kind: A1
- Application number: 16/777,964
- Filed: 2020-01-31
- Published: 2021-08-05
- Earliest claimed: 2020-01-31
- Assignees: EMC IP Holding Company LLC
- Applicants: EMC IP Holding Company LLC
- Inventors: Victor Fong; Megan A. Murawski; Amy N. Seibel
- Web page: https://patentgrep.com/patent/US20210241152A1

## CPC

- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- G06N 20/00: Machine learning
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

A method for managing data includes obtaining a request for a machine learning (ML) pipeline selection from a client, wherein the request comprises a training dataset and a domain of the training dataset, and in response to the request: identifying a set of ML pipelines based on the domain, obtaining runtime statistics for the set of ML pipelines using the domain and at least a portion the training dataset, generating, using a user preference model, an ordering of the set of ML pipelines based on the runtime statistics and user preferences, and presenting the ordering, the runtime statistics, and a notification based on the ordering to the client.

## Claims

### Claim 1

A method for managing data, the method comprising:
obtaining a request for a machine learning (ML) pipeline selection from a client, wherein the request comprises a training dataset and a domain of the training dataset; and
in response to the request:
identifying a set of ML pipelines based on the domain;
obtaining runtime statistics for the set of ML pipelines using the domain and at least a portion the training dataset;
generating, using a user preference model, an ordering of the set of ML pipelines based on the runtime statistics and user preferences; and
presenting the ordering, the runtime statistics, and a notification based on the ordering to the client.

### Claim 2 (depends on 1)

The method of claim 1, further comprising:
obtaining a ML selection that specifies a selected ML pipeline of the set of ML pipelines;
providing the selected ML pipeline to the client; and
updating the user preferences based on the ML selection.

### Claim 3 (depends on 2)

The method of claim 2, wherein obtaining the runtime statistics for the set of ML pipeline comprises:
providing inputs of each ML pipeline in the set of ML pipelines into a prediction model to generate the runtime statistics.

### Claim 4 (depends on 3)

The method of claim 3, wherein the inputs comprise at least one of: the size of the training dataset, a type of ML algorithm associated with each ML pipeline, a number of hyper-parameters of each ML pipeline, and a standard dataset.

### Claim 5 (depends on 3)

The method of claim 3, wherein the runtime statistics comprise a plurality of criteria: and wherein the criteria comprise at least one of: an accuracy, a training cost, a training speed, an inferred cost and an inferred speed of each ML pipeline.

### Claim 6 (depends on 3)

The method of claim 3, further comprising:
obtaining ML pipeline telemetry associated with execution of the selected ML pipeline; and
updating the prediction model based on the ML pipeline telemetry.

### Claim 7 (depends on 6)

The method of claim 6, wherein the ML pipeline telemetry is obtained from a ML pipeline execution environment in which the selected ML pipeline is executing.

### Claim 8

A system, comprising:
a processor; and
memory comprising instructions which, when executed by the processor, perform a method, the method comprising:
obtaining a request for a machine learning (ML) pipeline selection from a client, wherein the request comprises a training dataset and a domain of the training dataset; and
in response to the request:
identifying a set of ML pipelines based on the domain;
obtaining runtime statistics for the set of ML pipelines using the domain and the training dataset;
inputting the runtime statistics and user preferences associated with the client into a user preference model to obtain an ordering of the set of ML pipelines; and
presenting the ordering, the runtime statistics, and a notification based on the ordering to the client.

### Claim 9 (depends on 8)

The system of claim 8, the method further comprising:
obtaining a ML selection that specifies a selected ML pipeline of the set of ML pipelines;
providing the selected ML pipeline to the client; and
updating the user preferences based on the ML selection.

### Claim 10 (depends on 9)

The system of claim 9, wherein obtaining the runtime statistics for the set of ML pipeline comprises:
providing inputs of each ML pipeline in the set of ML pipelines into a prediction model to generate the runtime statistics.

### Claim 11 (depends on 10)

The system of claim 10, wherein the inputs comprise at least one of: the size of the training dataset, a type of ML algorithm associated with each ML pipeline, a number of hyper-parameters of each ML pipeline, and a standard dataset.

### Claim 12 (depends on 10)

The system of claim 10, wherein the runtime statistics comprise a plurality of criteria: and wherein the criteria comprise at least one of: an accuracy, a training cost, a training speed, an inferred cost and an inferred speed of each ML pipeline.

### Claim 13 (depends on 8)

The system of claim 8, further comprising:
obtaining ML pipeline telemetry associated with execution of the selected ML pipeline; and
updating the prediction model based on the ML pipeline telemetry.

### Claim 14 (depends on 8)

The system of claim 8, wherein the ML pipeline telemetry is obtained from a ML pipeline execution environment in which the selected ML pipeline is executing.

### Claim 15

A non-transitory computer readable medium comprising computer readable program code, which when executed by a computer processor enables the computer processor to perform a method for managing data, the method comprising:
obtaining a request for a machine learning (ML) pipeline selection from a client, wherein the request comprises a training dataset and a domain of the training dataset; and
in response to the request:
identifying a set of ML pipelines based on the domain;
obtaining runtime statistics for the set of ML pipelines using the domain and the training dataset;
inputting the runtime statistics and user preferences associated with the client into a user preference model to obtain an ordering of the set of ML pipelines; and
presenting the ordering, the runtime statistics, and a notification based on the ordering to the client.

### Claim 16 (depends on 15)

The non-transitory computer readable medium of claim 15, the method further comprising:
obtaining a ML selection that specifies a selected ML pipeline of the set of ML pipelines;
providing the selected ML pipeline to the client; and
updating the user preferences based on the ML selection.

### Claim 17 (depends on 16)

The non-transitory computer readable medium of claim 16, wherein obtaining the runtime statistics for the set of ML pipeline comprises:
providing inputs of each ML pipeline in the set of ML pipelines into a prediction model to generate the runtime statistics.

### Claim 18 (depends on 17)

The non-transitory computer readable medium of claim 17, wherein the inputs comprise at least one of: the size of the training dataset, a type of ML algorithm associated with each ML pipeline, a number of hyper-parameters of each ML pipeline, and a standard dataset.

### Claim 19 (depends on 17)

The non-transitory computer readable medium of claim 17, wherein the runtime statistics comprise a plurality of criteria: and wherein the criteria comprise at least one of: an accuracy, a training cost, a training speed, an inferred cost and an inferred speed of each ML pipeline.

### Claim 20 (depends on 17)

The non-transitory computer readable medium of claim 17, further comprising:
obtaining ML pipeline telemetry associated with execution of the selected ML pipeline; and
updating the prediction model based on the ML pipeline telemetry.