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AI/ML-driven actionable insights and themes for Amazon third-party sellers using AWS

Flipboard

This post presents a solution that uses a workflow and AWS AI and machine learning (ML) services to provide actionable insights based on those transcripts. We use multiple AWS AI/ML services, such as Contact Lens for Amazon Connect and Amazon SageMaker , and utilize a combined architecture. Validation set 11 1500 0.82

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Machine Learning with MATLAB and Amazon SageMaker

Flipboard

In recent years, MathWorks has brought many product offerings into the cloud, especially on Amazon Web Services (AWS). First, we extract features from a subset of the full dataset using the Diagnostic Feature Designer app, and then run the model training locally with a MATLAB decision tree model.  Either Ubuntu or Linux.

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How Light & Wonder built a predictive maintenance solution for gaming machines on AWS

AWS Machine Learning Blog

Working with AWS, Light & Wonder recently developed an industry-first secure solution, Light & Wonder Connect (LnW Connect), to stream telemetry and machine health data from roughly half a million electronic gaming machines distributed across its casino customer base globally when LnW Connect reaches its full potential.

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Build generative AI agents with Amazon Bedrock, Amazon DynamoDB, Amazon Kendra, Amazon Lex, and LangChain

AWS Machine Learning Blog

Instead of only fulfilling predefined intents through a static decision tree, agents are autonomous within the context of their suite of available tools. Amazon Lex then invokes an AWS Lambda handler for user intent fulfillment. AWS Identity and Access Management (IAM) permissions for the preceding resources.

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How Dialog Axiata used Amazon SageMaker to scale ML models in production with AI Factory and reduced customer churn within 3 months

AWS Machine Learning Blog

In 2022, Dialog Axiata made significant progress in their digital transformation efforts, with AWS playing a key role in this journey. Dialog Axiata runs some of their business-critical telecom workloads on AWS, including Charging Gateway, Payment Gateway, Campaign Management System, SuperApp, and various analytics tasks.

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What is Data-driven vs AI-driven Practices?

Pickl AI

To confirm seamless integration, you can use tools like Apache Hadoop, Microsoft Power BI, or Snowflake to process structured data and Elasticsearch or AWS for unstructured data. Develop Hybrid Models Combine traditional analytical methods with modern algorithms such as decision trees, neural networks, and support vector machines.

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Genomics England uses Amazon SageMaker to predict cancer subtypes and patient survival from multi-modal data

AWS Machine Learning Blog

We provide insights on interpretability, robustness, and best practices of architecting complex ML workflows on AWS with Amazon SageMaker. by AWS, which aimed to mitigate the limitations of PORPOISE. 2022 , tree-based models tend to overfit less when confronted with high-dimensional data with many largely uninformative features).