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A review of purpose-built accelerators for financial services

AWS Machine Learning

SIMD describes computers with multiple processing elements that perform the same operation on multiple data points simultaneously. SIMT describes processors that are able to operate on data vectors and arrays (as opposed to just scalars), and therefore handle big data workloads efficiently.

Benchmark 109
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3 Ultimate Factors of Business Performance

ClearAction

Is customer engagement, artificial intelligence, digital marketing, predictive analytics, big data, or some other “shiny object” the key to driving business performance? Poor traditions allow weak accountability for acting on customer needs. 3 Ultimate Factors of Business Performance Lynn Hunsaker.

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Achieve rapid time-to-value business outcomes with faster ML model training using Amazon SageMaker Canvas

AWS Machine Learning

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. His knowledge ranges from application architecture to big data, analytics, and machine learning.

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How to Bring Agile Innovation to Customer Success

Totango

An agile approach brings the full power of big data analytics to bear on customer success. Agile CS goals should be quantified in terms of measurable objectives and benchmarks. This provides transparency and accountability and empowers a data-driven approach to customer success. Define How to Measure Success.

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3 Ultimate Factors of Business Performance

ClearAction

Is customer engagement, artificial intelligence, digital marketing, predictive analytics, big data, or some other “shiny object” the key to driving business performance? Poor traditions allow weak accountability for acting on customer needs. 3 Ultimate Factors of Business Performance Lynn Hunsaker.

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MLOps foundation roadmap for enterprises with Amazon SageMaker

AWS Machine Learning

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. The following figure illustrates this architecture.

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How Twilio used Amazon SageMaker MLOps pipelines with PrestoDB to enable frequent model retraining and optimized batch transform

AWS Machine Learning

Here, we predict whether an order is a high_value_order or a low_value_order based on the orderpriority as given from the TPC-H data. For more information on the TPC-H data, its database entities, relationships, and characteristics, refer to TPC Benchmark H. Follow the instructions in the GitHub README.md

Scripts 124