shalabh05/Shalabh_Textdataset · Datasets at Hugging Face

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Solution \| Achieving an Agile DevOps Culture Using AWS ML Solutions
Opportunity \| Using Amazon SageMaker to Reduce Time to Value for NatWest Group
AWS Services Used
from 2–4 weeks to hours
Outcome \| Deploying Innovative Services at Scale Using Amazon SageMaker
中文 (繁體)
Bahasa Indonesia
NatWest Group employees now have fast and simple access to the data and tools that they need to build and train ML models. “We modernized our technology stack, simplified data access, and standardized our governance and operational procedures in a way that maintains the right risk behaviors,” says McMahon. “Using Amazon SageMaker, we can go from an idea on a whiteboard to a working ML solution in production in a few months versus 1 year or more.” NatWest Group launched its first offerings in November 2022, reducing its time to value from 12–18 months to only 7.
30+ ML use cases
AWS Service Catalog allows organizations to create and manage catalogs of IT services that are approved for use on AWS.
Ρусский
Organizations of all sizes across all industries are transforming their businesses and delivering on their missions every day using AWS. Contact our experts and start your own AWS journey today.
عربي
To remain competitive in the fast-paced financial services industry, NatWest Group is under pressure to deliver increasingly personalized and premier services to its 19 million customers. The bank has built a variety of workflows to explore its data and build machine learning (ML) solutions that provide a bespoke experience based on customer demands. However, its legacy processes were slow and inconsistent, and NatWest Group wanted to accelerate its time to business value with ML.
中文 (简体)

Amazon SageMaker Studio
About NatWest Group
Overview
built in 4 months
The bank turned to Amazon Web Services (AWS) and adopted Amazon SageMaker, a service that data scientists and engineers use to build, train, and deploy ML models for virtually any use case with fully managed infrastructure, tools, and workflows. By centralizing its ML processes on AWS, NatWest Group has reduced the time that it takes to launch new products and services by several months and has embraced a more agile culture among its data science teams.
In April 2022, NatWest Group launched an enterprise-wide, centralized ML workflow, which it powers by using Amazon SageMaker. And because the bank already had a presence on Amazon Simple Storage Service (Amazon S3)—an object storage service offering industry-leading scalability, data availability, security, and performance—this was the service of choice for its data lake migration. With simpler access to data and powerful ML tools, its data science teams have built over 30 ML use cases on Amazon SageMaker in the first 4 months after launch. These use cases include a solution that tailors marketing campaigns to specific customer segments and an application that automates simple fraud detection tasks so that investigators can focus on difficult, higher-value cases.
Get Started
Reduced time to value

Customer Stories / Financial Services
“There’s so much that we’ve gained from using our data intelligently,” says Greig Cowan, head of data science for data innovation at NatWest Group. “On AWS, we have opened up many new avenues and opportunities for us to detect fraud, tailor our marketing, and understand our customers and their needs.”
Türkçe
English
720+
Promotes self-service environment
NatWest Group is a British banking company that offers a wide range of services for personal, business, and corporate customers. It serves 19 million customers throughout the United Kingdom and Ireland.
Greig Cowan Head of data science for data innovation, NatWest Group
If you want to launch an environment for data science work, it could take 2–4 weeks. On AWS, we can spin up that environment within a few hours. At most, it takes 1 day.”
AWS Service Catalog
Amazon Simple Storage Service (Amazon S3) is an object storage service offering industry-leading scalability, data availability, security, and performance.
To accelerate its employees’ workflows, NatWest Group uses AWS Service Catalog, which organizations use to create, organize, and govern infrastructure-as-code templates. Before the bank adopted this solution, data scientists or engineers would need to contact a centralized team if they wanted to provision an ML environment. Previously, it would take 2–4 weeks before the infrastructure was ready to use. Now, NatWest Group can launch a template from AWS Service Catalog and spin up an ML environment in just a few hours. Its data teams can begin working on projects much sooner and have more time to focus on building powerful ML models. This self-service environment not only empowers data science teams to derive business value faster, but it also encourages consistency. “As a large organization, we want to make sure anything that we build is scalable and consistent,” says McMahon. “On AWS, we have standardized our approach to data using a consistent language and framework, which can be rolled out across different use cases.”
Reduced time to provision environment
Deutsch

Tiếng Việt
Amazon S3
Italiano
ไทย
Learn how NatWest Group used Amazon SageMaker to create personalized customer journeys with secure machine learning.
To learn more, visit aws.amazon.com/financial-services/machine-learning/.
Learn more »
AWS courses completed
from 12–18 months to 7
NatWest Group has adopted a number of features on Amazon SageMaker to streamline its ML workflows with the security and governance required of a major financial institution. In particular, NatWest Group adopted Amazon SageMaker Studio, a single web-based visual interface where it can perform all ML development steps. Because Amazon SageMaker Studio is simple to use and configure, new users can quickly set it up and start building ML models sooner.
Português
Amazon SageMaker




AWS Partner Network (APN) Blog








Accelerate Your Analytics Journey on AWS with DXC Analytics and AI Platform



by

Dhiraj Thakur
and

Murali Gowda
\| on

27 JUN 2023
\| in

Analytics
,
Artificial Intelligence
,
AWS Partner Network
,
Customer Solutions
,
Intermediate (200)
,
Thought Leadership
\|

Permalink
\|

Comments
\|

Share

Solution | Achieving an Agile DevOps Culture Using AWS ML Solutions

Opportunity | Using Amazon SageMaker to Reduce Time to Value for NatWest Group

AWS Services Used

from 2–4 weeks to hours

Outcome | Deploying Innovative Services at Scale Using Amazon SageMaker

中文 (繁體)

Bahasa Indonesia

NatWest Group employees now have fast and simple access to the data and tools that they need to build and train ML models. “We modernized our technology stack, simplified data access, and standardized our governance and operational procedures in a way that maintains the right risk behaviors,” says McMahon. “Using Amazon SageMaker, we can go from an idea on a whiteboard to a working ML solution in production in a few months versus 1 year or more.” NatWest Group launched its first offerings in November 2022, reducing its time to value from 12–18 months to only 7.

30+ ML use cases

AWS Service Catalog allows organizations to create and manage catalogs of IT services that are approved for use on AWS.

Ρусский

Organizations of all sizes across all industries are transforming their businesses and delivering on their missions every day using AWS. Contact our experts and start your own AWS journey today.

عربي

To remain competitive in the fast-paced financial services industry, NatWest Group is under pressure to deliver increasingly personalized and premier services to its 19 million customers. The bank has built a variety of workflows to explore its data and build machine learning (ML) solutions that provide a bespoke experience based on customer demands. However, its legacy processes were slow and inconsistent, and NatWest Group wanted to accelerate its time to business value with ML.

中文 (简体)

Amazon SageMaker Studio

About NatWest Group

Overview

built in 4 months

The bank turned to Amazon Web Services (AWS) and adopted Amazon SageMaker, a service that data scientists and engineers use to build, train, and deploy ML models for virtually any use case with fully managed infrastructure, tools, and workflows. By centralizing its ML processes on AWS, NatWest Group has reduced the time that it takes to launch new products and services by several months and has embraced a more agile culture among its data science teams.

In April 2022, NatWest Group launched an enterprise-wide, centralized ML workflow, which it powers by using Amazon SageMaker. And because the bank already had a presence on Amazon Simple Storage Service (Amazon S3)—an object storage service offering industry-leading scalability, data availability, security, and performance—this was the service of choice for its data lake migration. With simpler access to data and powerful ML tools, its data science teams have built over 30 ML use cases on Amazon SageMaker in the first 4 months after launch. These use cases include a solution that tailors marketing campaigns to specific customer segments and an application that automates simple fraud detection tasks so that investigators can focus on difficult, higher-value cases.

Get Started

Reduced time to value

Customer Stories / Financial Services

“There’s so much that we’ve gained from using our data intelligently,” says Greig Cowan, head of data science for data innovation at NatWest Group. “On AWS, we have opened up many new avenues and opportunities for us to detect fraud, tailor our marketing, and understand our customers and their needs.”

Türkçe

English

720+

Promotes self-service environment

NatWest Group is a British banking company that offers a wide range of services for personal, business, and corporate customers. It serves 19 million customers throughout the United Kingdom and Ireland.

Greig Cowan Head of data science for data innovation, NatWest Group

If you want to launch an environment for data science work, it could take 2–4 weeks. On AWS, we can spin up that environment within a few hours. At most, it takes 1 day.”

AWS Service Catalog

Amazon Simple Storage Service (Amazon S3) is an object storage service offering industry-leading scalability, data availability, security, and performance.

To accelerate its employees’ workflows, NatWest Group uses AWS Service Catalog, which organizations use to create, organize, and govern infrastructure-as-code templates. Before the bank adopted this solution, data scientists or engineers would need to contact a centralized team if they wanted to provision an ML environment. Previously, it would take 2–4 weeks before the infrastructure was ready to use. Now, NatWest Group can launch a template from AWS Service Catalog and spin up an ML environment in just a few hours. Its data teams can begin working on projects much sooner and have more time to focus on building powerful ML models. This self-service environment not only empowers data science teams to derive business value faster, but it also encourages consistency. “As a large organization, we want to make sure anything that we build is scalable and consistent,” says McMahon. “On AWS, we have standardized our approach to data using a consistent language and framework, which can be rolled out across different use cases.”

Reduced time to provision environment

Deutsch

Tiếng Việt

Amazon S3

Italiano

ไทย

Learn how NatWest Group used Amazon SageMaker to create personalized customer journeys with secure machine learning.

To learn more, visit aws.amazon.com/financial-services/machine-learning/.

Learn more »

AWS courses completed

from 12–18 months to 7

NatWest Group has adopted a number of features on Amazon SageMaker to streamline its ML workflows with the security and governance required of a major financial institution. In particular, NatWest Group adopted Amazon SageMaker Studio, a single web-based visual interface where it can perform all ML development steps. Because Amazon SageMaker Studio is simple to use and configure, new users can quickly set it up and start building ML models sooner.

Português

Amazon SageMaker

AWS Partner Network (APN) Blog

Accelerate Your Analytics Journey on AWS with DXC Analytics and AI Platform

by

Dhiraj Thakur

and

Murali Gowda

| on

27 JUN 2023

| in

Analytics

,

Artificial Intelligence

,

AWS Partner Network

,

Customer Solutions

,

Intermediate (200)

,

Thought Leadership

|

Permalink

|

Comments

|

Share