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The Subtleties within Support with Humio [Podcast]

Nicereply

Variances in your industry, solution, and product quality will change what makes a great Customer Experience. Metrics drive our businesses, but who determines those metrics differs from company to company. In Conversation with Camille Acey, Global Head of Customer Experience at Humio.

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Driving advanced analytics outcomes at scale using Amazon SageMaker powered PwC’s Machine Learning Ops Accelerator

AWS Machine Learning

AWS CDK provides the ability to manage changes for the complete solution. The automated pipeline includes steps for out-of-the-box model storage and metric tracking. About the Authors Kiran Kumar Ballari is a Principal Solutions Architect at Amazon Web Services (AWS).

Analytics 122
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Large-scale revenue forecasting at Bosch with Amazon Forecast and Amazon SageMaker custom models

AWS Machine Learning

Bosch is a multinational corporation with entities operating in multiple sectors, including automotive, industrial solutions, and consumer goods. These metrics provide business planning insights at different levels of aggregation and enable data-driven decision-making. Evaluation metrics. Evaluation. Backtest windows.

APIs 89
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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning

Query training results: This step calls the Lambda function to fetch the metrics of the completed training job from the earlier model training step. RMSE threshold: This step verifies the trained model metric (RMSE) against a defined threshold to decide whether to proceed towards endpoint deployment or reject this model.

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Streamline Sales Processes with Enterprise CPQ

Cincom

Expand Deal Opportunities Consolidated usage metrics and customer purchase history within Cincom CPQ provide valuable insights for identifying timely upsell options to maximize deal size. Cincom CPQ empowers providers to know their customers and respond appropriately.

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Amazon Bedrock Custom Model Import now generally available

AWS Machine Learning

As we continue to develop this feature to meet our customers’ needs, there are a few points to be aware of: Define your test suite and acceptance metrics before starting the journey. Best practices to consider: This feature brings significant advantages for hosting your fine-tuned models efficiently.

APIs 135
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Run your local machine learning code as Amazon SageMaker Training jobs with minimal code changes

AWS Machine Learning

trainer.train() trainer.save_model(model_dir) After you run the training job, you can run the rest of the cells in the notebook to inspect the evaluation metrics and classify the text on our trained model. Define metrics to log run.log_metric("metric_a", 0.5) Common metrics may include accuracy or loss. Vikram Elango is a Sr.