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5 Capabilities of Business Intelligence for Social Media Monitoring and Analytics

CSM Magazine

In this article, we’ll explore five key capabilities of BI that empower businesses to monitor social media conversations, analyze sentiment, conduct competitor analysis, create customized dashboards and reports, and integrate social media data with other sources for comprehensive analytics.

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Optimize pet profiles for Purina’s Petfinder application using Amazon Rekognition Custom Labels and AWS Step Functions

AWS Machine Learning

The solution uses the following services: Amazon API Gateway is a fully managed service that makes it easy for developers to publish, maintain, monitor, and secure APIs at any scale. Purina’s solution is deployed as an API Gateway HTTP endpoint, which routes the requests to obtain pet attributes.

APIs 97
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Testing times: testingRTC is the smart, synchronized, real-world scenario WebRTC testing solution for the times we live in.

Spearline

Consequently, no other testing solution can provide the range and depth of testing metrics and analytics. And testingRTC offers multiple ways to export these metrics, from direct collection from webhooks, to downloading results in CSV format using the REST API. Happy days! And everything is within your control.

Scripts 98
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Amazon SageMaker Automatic Model Tuning now automatically chooses tuning configurations to improve usability and cost efficiency

AWS Machine Learning

Desired target metrics, improvement monitoring, and convergence detection monitors the performance of the model and assists with early stopping if the models don’t improve after a defined number of training jobs. Autotune uses best practices as well as internal benchmarks for selecting the appropriate ranges.

APIs 76
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How Patsnap used GPT-2 inference on Amazon SageMaker with low latency and cost

AWS Machine Learning

as_trt_engine(output_fpath=trt_path, profiles=profiles) gpt2_trt = GPT2TRTDecoder(gpt2_engine, metadata, config, max_sequence_length=42, batch_size=10) Latency comparison: PyTorch vs. TensorRT JMeter is used for performance benchmarking in this project. implement the model and the inference API. model_fp16.onnx gpt2 and predictor.py

APIs 66
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Evolving Customer Service: Easy Steps to Help Your Customers Through Digital Transformation

Comm100

Artificial intelligence, machine learning, IoT, and analytics are part of the technology stack that every company has actively started using to enhance productivity and efficiency. Ensure you find benchmarks and determine prompt response times for your business for the asynchronous communication channels like Facebook, SMS, and email.

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Face-off Probability, part of NHL Edge IQ: Predicting face-off winners in real time during televised games

AWS Machine Learning

The decision tree provided the cut-offs for each metric, which we included as rules-based logic in the streaming application. At the end, we found that the LightGBM model worked best with well-calibrated accuracy metrics. The second important component of the architecture is Amazon Kinesis Data Analytics for Apache Flink.