Itaú Unibanco (Itaú), the largest private-sector bank in Brazil, needed to improve the speed, flexibility, and scalability of its machine learning (ML) infrastructure for its more than 3,200 ML users. The bank’s on-premises infrastructure required ordering servers and completing configuration tasks before solutions were available to the data science team.
Reported impact
Itaú is looking to continue improving its standardization. The next step for internal standardization for its data scientists involves having more employees using its IARA solution, which is based on AWS and uses multiple services, including Amazon SageMaker Studio.
Itaú Unibanco (Itaú), the largest private-sector bank in Brazil, needed to improve the speed, flexibility, and scalability of its machine learning (ML) infrastructure for its more than 3,200 ML users. The bank’s on-premises infrastructure required ordering servers and completing configuration tasks before solutions were available to the data science team. Itaú has built a complete solution for its data scientists using AWS. First, data is gathered using AWS Glue , a serverless data integration service that makes it easier to discover, prepare, move, and integrate data from multiple sources for analytics, ML, and application development. This data is then used to start experiments using Amazon SageMaker Studio. Itaú uses Amazon SageMaker Studio as a flexible development solution for its internal data scientists to experiment.
First, data is gathered using AWS Glue , a serverless data integration service that makes it easier to discover, prepare, move, and integrate data from multiple sources for analytics, ML, and application development.
It has over 95,700 employees, about 15,000 of whom work in IT.
Itaú Unibanco (Itaú), the largest private-sector bank in Brazil, needed to improve the speed, flexibility, and scalability of its machine learning (ML) infrastructure for its more than 3,200 ML users.
Itaú has built a complete solution for its data scientists using AWS.
Itaú provides banking services to customers in Brazil, Latin America, and 18 other countries around the world.
The bank’s on-premises infrastructure required ordering servers and completing configuration tasks before solutions were available to the data science team.
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