# US 2021/0241088 A1 · Automated ml microservice and function generation for cloud native platforms

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- Number: US 2021/0241088 A1
- Document ID: US20210241088A1
- Status: Published application
- Kind: A1
- Application number: 16/776,961
- Filed: 2020-01-30
- Published: 2021-08-05
- Earliest claimed: 2020-01-30
- Assignees: EMC IP Holding Company LLC
- Applicants: EMC IP Holding Company LLC
- Inventors: Victor Fong
- Web page: https://patentgrep.com/patent/US20210241088A1

## CPC

- G06N 3/08: Learning methods
- G06N 20/00: Machine learning
- G06N 3/04: Architecture, e.g. interconnection topology
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

One example method includes receiving, as an input, an ML pipeline definition, identifying a group of layers required to be created for the ML pipeline definition, for one of more of the layers, receiving input concerning one or more characteristics of the layer, creating the layers for which input has been received, and packaging the created layers with the ML pipeline definition to create a production-ready ML model.

## Claims

### Claim 1

A method, comprising:
receiving, as an input, an ML pipeline definition;
identifying a group of layers required to be created for the ML pipeline definition;
for one of more of the layers, receiving input concerning one or more characteristics of the layer;
creating the layers for which input has been received; and
packaging the created layers with the ML pipeline definition to create a production-ready ML model.

### Claim 2 (depends on 1)

The method as recited in claim 1, wherein the production-ready ML model comprises an inference microservice.

### Claim 3 (depends on 2)

The method as recited in claim 2, further comprising implementing a training microservice operable to implement ML training of the inference microservice.

### Claim 4 (depends on 1)

The method as recited in claim 1, wherein the production-ready ML model is operable in a cloud native environment.

### Claim 5 (depends on 1)

The method as recited in claim 1, wherein the created layers comprise a pipeline execution layer, a microservice/function wrapper layer, an input/output data transformation layer, a test suites layer, a deployment definition layer, and an integration wrapper layer.

### Claim 6 (depends on 1)

The method as recited in claim 1, wherein the production-ready ML model is not constrained for use with any particular vendor.

### Claim 7 (depends on 1)

The method as recited in claim 1, wherein one or more of the layers is created with a respective user-customizable template.

### Claim 8 (depends on 1)

The method as recited in claim 1, further comprising creating the ML pipeline definition.

### Claim 9 (depends on 1)

The method as recited in claim 1, further comprising deploying the production-ready ML model in a production environment.

### Claim 10 (depends on 1)

The method as recited in claim 1, wherein one of the created layers is a microservice/function wrapper layer that specifies an inference deployment platform, a training deployment platform, and an invocation mechanism.

### Claim 11

A non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations comprising:
receiving, as an input, an ML pipeline definition;
identifying a group of layers required to be created for the ML pipeline definition;
for one of more of the layers, receiving input concerning one or more characteristics of the layer;
creating the layers for which input has been received; and
packaging the created layers with the ML pipeline definition to create a production-ready ML model.

### Claim 12 (depends on 11)

The non-transitory storage medium as recited in claim 11, wherein the production-ready ML model comprises an inference microservice.

### Claim 13 (depends on 12)

The non-transitory storage medium as recited in claim 12, wherein the operations further comprise implementing a training microservice operable to implement ML training of the inference microservice.

### Claim 14 (depends on 11)

The non-transitory storage medium as recited in claim 11, wherein the production-ready ML model is operable in a cloud native environment.

### Claim 15 (depends on 11)

The non-transitory storage medium as recited in claim 11, wherein the created layers comprise a pipeline execution layer, a microservice/function wrapper layer, an input/output data transformation layer, a test suites layer, a deployment definition layer, and an integration wrapper layer.

### Claim 16 (depends on 11)

The non-transitory storage medium as recited in claim 11, wherein the production-ready ML model is not constrained for use with any particular vendor.

### Claim 17 (depends on 1)

The non-transitory storage medium as recited in claim 1, wherein one or more of the layers is created with a respective user-customizable template.

### Claim 18 (depends on 1)

The non-transitory storage medium as recited in claim 1, wherein the operations further comprise creating the ML pipeline definition.

### Claim 19 (depends on 1)

The non-transitory storage medium as recited in claim 1, wherein the operations further comprise deploying the production-ready ML model in a production environment.

### Claim 20 (depends on 1)

The non-transitory storage medium as recited in claim 1, wherein one of the created layers is a microservice/function wrapper layer that specifies an inference deployment platform, a training deployment platform, and an invocation mechanism.