AWS Machine Learning

MARCH 3, 2023

Machine learning (ML) can help companies make better business decisions through advanced analytics. We estimated these numbers by running benchmark tests on different dataset sizes from 0.5 Under the hood, SageMaker Canvas uses multiple AutoML technologies to automatically build the best ML models for your data.

Benchmark 84

Benchmark Big data Banking Analytics 84

CSM Magazine

DECEMBER 4, 2023

Furthermore, the integration of digital technologies, including artificial intelligence, blockchain, and big data, augments these ESG capabilities. The dynamic nature of ESG metrics and their multifaceted relationship with CFP necessitates a detailed and layered analytical approach.

Government 105

Government Morale Finance industry standards 105

Verint

JULY 28, 2017

In a recent ETCIO.com article , Firstsource’s Aparajita Gupta, senior vice president, digital support and analytics, describes how using Verint Speech and Text Analytics helped the India-based business process outsourcer analyze huge volumes of data and—importantly for its customers—find root causes to a variety of challenges.

Analytics 28

Analytics Benchmark Business Process Outsourcing outsourcing 28

AWS Machine Learning

SEPTEMBER 11, 2024

The financial services industry (FSI) is no exception to this, and is a well-established producer and consumer of data and analytics. These activities cover disparate fields such as basic data processing, analytics, and machine learning (ML). The union of advances in hardware and ML has led us to the current day.

Benchmark 92

Benchmark Banking Analytics Big data 92

Customer Interactions

FEBRUARY 14, 2013

When we think about the necessity of security, banking is an industry that immediately comes to mind. For banks, investing in security is simply a part of day-to-day business. Millennium bcp, Portugal’s largest bank,is an excellent example of how an enterprise can enhance its security while also achieving enormous savings.

Banking 33

Banking Big data voip Benchmark 33

AWS Machine Learning

JUNE 24, 2022

Data scientists collaborate with ML engineers in a separate environment to build robust and production-ready algorithms and source code, orchestrated using Amazon SageMaker Pipelines. The generated models are stored and benchmarked in the Amazon SageMaker model registry.

Enterprise 108

Enterprise Engineering Accountability APIs 108