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.
Use case type
MLOps platform
MLOps platform groups 2 documented AI deployments in the AI Use Case Hub. Adoption so far spans Finance and Public Sector, led by Brazil. Teams most often build it with Amazon SageMaker Studio and AWS Glue. Browse the company examples below to see how teams put it into production.
Use cases
2
Examples
2
Industries
2
Timeline
1 mo
Data updated 1 day ago
Adoption over time
Documented cases per month
The first 2 cases of this type were documented this month — the monthly history will appear once a full month closes.
Company examples
Use cases of this type
2 shown from 2 use cases
GovTech built MAESTRO, an end-to-end AI/ML development platform to scale generative AI adoption across Singapore government agencies.The platform helps agencies create cost-efficient generative AI tools and production-ready use cases with a no-code ML interface.
Common questions
MLOps platform at a glance
- How many mlops platform use cases are documented?
- The AI Use Case Hub documents 2 real mlops platform deployments across 2 industries, with 2 detailed company examples you can browse.
- Which industries adopt mlops platform the most?
- MLOps platform is most common in Finance (50%) and Public Sector (50%).
- Which countries lead in mlops platform?
- Brazil leads documented mlops platform deployments, followed by Singapore.
- What technologies are used for mlops platform?
- Teams most often build mlops platform with Amazon SageMaker Studio, AWS Glue and Amazon SageMaker Endpoints.
- What results do companies report from mlops platform?
- Across the 2 deployments reporting outcomes, companies most often cite cost efficiency (100%), other strategic outcome (50%) and scale & capacity (50%).