What challenges did Itaú face before AI implementation?

Itaú's on-premises ML infrastructure required manual server ordering and configuration before data scientists could begin work. The process could take up to six months and created a waiting list of more than 100 ML models.

What was the impact of the AI solution?

The company reduced model development and deployment time from up to six months to 3-5 days in some cases. It increased staff productivity through standardization and reduced costs compared with its on-premises infrastructure. As of 2024, around 3,200 unique users had used Amazon SageMaker Studio.

Source

Evidence: Medium50/100

Itaú: Standardizing and accelerating ML deployments with Amazon SageMaker Studio

Use case typeMLOps platformUpdated Jul 15, 2026

Itaú improved the speed, flexibility, scalability, and productivity of its machine learning infrastructure by moving part of its ML environment to AWS. The bank built an end-to-end ML development and deployment solution using Amazon SageMaker Studio, Amazon SageMaker deployment options, AWS Glue, and Amazon CloudWatch.

Organization: Itaú
Industry: Finance
Location: Brazil
Published: July 2026

Reported outcomes

3-5 days

model development and deployment timeTime & speed

Strategic outcomes

Other strategic outcomeIncreased staff productivityCost efficiencyReduced infrastructure costsScale & capacitySupported more than 3,200 ML users

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Model development and deployment time: 3-5 days decrease

AWS Customer StoriesJul 15, 2026Customer storyInferred claimMedium evidence strength

reduced deployment time from up to 6 months to 3–5 days in some cases

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Itaú
Provider: AWS
Maturity: Unknown
Linked source: AWS Customer Stories

No explicit deployment-stage evidence found.

Customer identity supportedSource describes one deploymentMaturity evidence evaluated

Primary read

Use case focus

Showing 3 of 3

1MLOps platform
2Machine learning operations
3Infrastructure modernization

Itaú's on-premises ML infrastructure required manual server ordering and configuration before data scientists could begin work.
The process could take up to six months and created a waiting list of more than 100 ML models.

Itaú adopted AWS as a strategic cloud provider and used Amazon SageMaker Studio for an integrated web-based ML development environment.
The solution also used AWS Glue for data integration, Amazon SageMaker Endpoints, Batch Transform, and Asynchronous Inference for deployment, and Amazon CloudWatch for monitoring.

Technologies

Amazon SageMaker Studio AWS Glue Amazon SageMaker Endpoints Amazon SageMaker Batch Transform Amazon SageMaker Asynchronous Inference Amazon CloudWatch

The company reduced model development and deployment time from up to six months to 3-5 days in some cases.
It increased staff productivity through standardization and reduced costs compared with its on-premises infrastructure.
As of 2024, around 3,200 unique users had used Amazon SageMaker Studio.

Architecture

Itaú built an end-to-end ML development and deployment pipeline on AWS centered on Amazon SageMaker Studio. Data flows through AWS Glue into SageMaker Studio for experimentation, then models are deployed with SageMaker Endpoints, Batch Transform, and Asynchronous Inference, with Amazon CloudWatch used for monitoring and operational visibility.

Sources & evidence1

Evidence: Medium50/100Evidence strength