Google llc (20240104394). Platform for Automatic Production of Machine Learning Models and Deployment Pipelines simplified abstract
Contents
- 1 Platform for Automatic Production of Machine Learning Models and Deployment Pipelines
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Platform for Automatic Production of Machine Learning Models and Deployment Pipelines - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.9.1 Unanswered Questions
- 1.9.2 How does the computing system handle data privacy and security concerns when importing and exporting datasets and models?
- 1.9.3 What level of customization or fine-tuning capabilities does the computing system offer for the generated machine learning models and deployment pipelines?
- 1.10 Original Abstract Submitted
Platform for Automatic Production of Machine Learning Models and Deployment Pipelines
Organization Name
Inventor(s)
Amy Skerry-ryan of Mountain View CA (US)
Quentin Lascombes De Laroussilhe of Zurich (CH)
Ronald Rong Yang of Cupertino CA (US)
Carla Marie Riggi of Richmond CA (US)
Chansoo Lee of Pittsburgh PA (US)
Jordan Arthur Grimstad of Zurich (CH)
Christopher Mark Lamb of San Francisco CA (US)
Joseph Michael Moran of Arlington MA (US)
Nihesh Anderson Klutto Milleth of Mountain View CA (US)
Noah Weston Hadfield-menell of San Francisco CA (US)
Volodymyr Shtenovych of Zurich (CH)
Ziqi Huang of Mountain View CA (US)
Michael David Gerard of Orange CA (US)
Mehadi Seid Hassen of Milpitas CA (US)
Platform for Automatic Production of Machine Learning Models and Deployment Pipelines - A simplified explanation of the abstract
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
- Amy Skerry-ryan of Mountain View CA (US)
- Quentin Lascombes De Laroussilhe of Zurich (CH)
- Ronald Rong Yang of Cupertino CA (US)
- Carla Marie Riggi of Richmond CA (US)
- Chansoo Lee of Pittsburgh PA (US)
- Jordan Arthur Grimstad of Zurich (CH)
- Christopher Mark Lamb of San Francisco CA (US)
- Joseph Michael Moran of Arlington MA (US)
- Nihesh Anderson Klutto Milleth of Mountain View CA (US)
- Noah Weston Hadfield-menell of San Francisco CA (US)
- Volodymyr Shtenovych of Zurich (CH)
- Ziqi Huang of Mountain View CA (US)
- Sagi Perel of Irvine CA (US)
- Michael David Gerard of Orange CA (US)
- Mehadi Seid Hassen of Milpitas CA (US)
- G06N3/10
- G06N3/045
- G06N3/08