Platform for Automatic Production of Machine Learning Models and Deployment Pipelines

This abstract first appeared for US patent application 20240104394 titled 'Platform for Automatic Production of Machine Learning Models and Deployment Pipelines

Simplified Explanation

The abstract describes a computing system that can automatically generate production-ready machine learning models and deployment pipelines from minimal input information such as a raw training dataset.

The computing system imports a training dataset provided by a user.
It executes an origination machine learning pipeline to select and train a machine learning model for the dataset.
The origination pipeline also generates a deployment machine learning pipeline for deploying the model.
The system exports the model and deployment pipeline for deployment.

Potential Applications

This technology could be applied in various industries such as healthcare, finance, marketing, and more for automating the process of creating and deploying machine learning models.

Problems Solved

1. Eliminates the need for manual model selection and deployment processes, saving time and resources. 2. Streamlines the machine learning pipeline creation process, making it more efficient and accessible to users with minimal expertise.

Benefits

1. Increased efficiency in developing machine learning models. 2. Automation of deployment pipelines reduces human error and speeds up the deployment process. 3. Enables users with minimal expertise to create and deploy machine learning models effectively.

Potential Commercial Applications

Automated machine learning model generation and deployment technology can be utilized in industries such as e-commerce, healthcare, finance, and more to improve decision-making processes and enhance customer experiences.

Possible Prior Art

One possible prior art could be AutoML platforms that automate the machine learning model selection and training process. These platforms may have similarities in automating the deployment pipeline creation as well.

Unanswered Questions

How does the computing system handle data privacy and security concerns when importing and exporting datasets and models?

The article does not address the specific measures or protocols in place to ensure data privacy and security throughout the import and export processes.

What level of customization or fine-tuning capabilities does the computing system offer for the generated machine learning models and deployment pipelines?

The article does not mention the extent to which users can customize or fine-tune the automatically generated models and pipelines to meet specific requirements or preferences.

Original Abstract Submitted

provided are computing systems, methods, and platforms that automatically produce production-ready machine learning models and deployment pipelines from minimal input information such as a raw training dataset. in particular, one example computing system can import a training dataset associated with a user. the computing system can execute an origination machine learning pipeline to perform a model architecture search that selects and trains a machine learning model for the training dataset. execution of the origination machine learning pipeline can also result in generation of a deployment machine learning pipeline configured to enable deployment of the machine learning model (e.g., running the machine learning model to produce inferences and/or optionally other tasks such as re-training and/or re-tuning the model). the computing system can export the machine learning model and the deployment machine learning pipeline for deployment of the machine learning model with the deployment machine learning pipeline

Google llc (20240104394). Platform for Automatic Production of Machine Learning Models and Deployment Pipelines simplified abstract

Contents

Platform for Automatic Production of Machine Learning Models and Deployment Pipelines

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