Seldon Case Studies How Capital One reduced model deployment time from months to minutes

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	How Capital One reduced model deployment time from months to minutes Seldon

How Capital One reduced model deployment time from months to minutes

Seldon

Technology Category	Analytics & Modeling - Machine Learning
Applicable Industries	Finance & Insurance
Applicable Functions	Product Research & Development
Use Cases	Predictive Maintenance
Services	System Integration
Challenge	Capital One, a leading US retail bank, was facing significant delays in their machine learning (ML) deployment pipeline. The data science teams were heavily reliant on the engineering department to test, deploy, or upgrade models. This resulted in month-long lag times and the need to redeploy entire applications for updates to existing models. Scaling up projects was only possible by using more developer resources and people power, which further strained the already overstretched teams. The bank needed a robust, scalable, and flexible approach to the deployment of ML models to support its millions of customers and users of their mobile banking app. Read More
About Customer	Capital One is a leading retail bank in the United States. It serves millions of customers and has a widely used mobile banking app. The bank has a large team of data scientists and engineers who work on deploying machine learning models to improve various aspects of the bank's operations. However, the process of deploying these models was slow and inefficient, often taking months to complete. This was largely due to the heavy reliance of the data science teams on the engineering department for testing, deploying, or upgrading models. Read More
Solution	Capital One deployed Seldon Core to create a 'Model as a Service' (MaaS) platform using ML-based real-time decisioning. This platform was designed to do the heavy lifting in packaging and containerizing models for developers. It formed the building blocks for a number of internal applications, features, models, and rules, and allowed data scientists, analysts, and engineering groups to collaborate efficiently. By operationalizing model deployment, data scientists could deploy and safely test their models without placing a burden on tech engineering teams. Seldon’s components allowed the team to wrap a variety of ML models into containers, then fit everything together to represent a service graph that could be seamlessly deployed into their platform. The entire application is wrapped around a service that goes through their CI/CD process to ensure Capital One as an organization have met the versioning, registering, testing, and governance requirements. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Applicable Industries

Finance & Insurance

Applicable Functions

Product Research & Development

Use Cases

Predictive Maintenance

Services

System Integration

Challenge

Capital One, a leading US retail bank, was facing significant delays in their machine learning (ML) deployment pipeline. The data science teams were heavily reliant on the engineering department to test, deploy, or upgrade models. This resulted in month-long lag times and the need to redeploy entire applications for updates to existing models. Scaling up projects was only possible by using more developer resources and people power, which further strained the already overstretched teams. The bank needed a robust, scalable, and flexible approach to the deployment of ML models to support its millions of customers and users of their mobile banking app.

About Customer

Capital One is a leading retail bank in the United States. It serves millions of customers and has a widely used mobile banking app. The bank has a large team of data scientists and engineers who work on deploying machine learning models to improve various aspects of the bank's operations. However, the process of deploying these models was slow and inefficient, often taking months to complete. This was largely due to the heavy reliance of the data science teams on the engineering department for testing, deploying, or upgrading models.

Solution

Capital One deployed Seldon Core to create a 'Model as a Service' (MaaS) platform using ML-based real-time decisioning. This platform was designed to do the heavy lifting in packaging and containerizing models for developers. It formed the building blocks for a number of internal applications, features, models, and rules, and allowed data scientists, analysts, and engineering groups to collaborate efficiently. By operationalizing model deployment, data scientists could deploy and safely test their models without placing a burden on tech engineering teams. Seldon’s components allowed the team to wrap a variety of ML models into containers, then fit everything together to represent a service graph that could be seamlessly deployed into their platform. The entire application is wrapped around a service that goes through their CI/CD process to ensure Capital One as an organization have met the versioning, registering, testing, and governance requirements.

Impact #1	The deployment of Seldon Core significantly reduced the time it took to deploy machine learning models, from months to minutes.
Impact #2	The data science team was able to test, update, and deploy models far quicker.
Impact #3	The process of versioning, vulnerability scanning, containerizing, deployment, testing, and promoting to production is all taken care of in this rapid process.

Impact #1

The deployment of Seldon Core significantly reduced the time it took to deploy machine learning models, from months to minutes.

Impact #2

The data science team was able to test, update, and deploy models far quicker.

Impact #3

The process of versioning, vulnerability scanning, containerizing, deployment, testing, and promoting to production is all taken care of in this rapid process.

Benefit #1	Reduced model deployment time from months to minutes.
Benefit #2	Increased efficiency in the data science team.
Benefit #3	Improved collaboration between data scientists, analysts, and engineering groups.

Benefit #1

Reduced model deployment time from months to minutes.

Benefit #2

Increased efficiency in the data science team.

Benefit #3

Improved collaboration between data scientists, analysts, and engineering groups.

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Overview

How Capital One reduced model deployment time from months to minutes

Operational Impact

Quantitative Benefit