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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

Overall, ML use cases require a readily available integrated solution to industrialize and streamline the process that takes an ML model from development to production deployment at scale using MLOps. AWS CDK provides the ability to manage changes for the complete solution. Rama Lanka lapalli is a Sr.

Analytics 127
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Streamline Sales Processes with Enterprise CPQ

Cincom

As the pioneer of CPQ technology, Cincom offers industry-leading solutions that enhance sales capabilities while solving common pain points. 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.

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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.

APIs 93
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Amazon Bedrock Custom Model Import now generally available

AWS Machine Learning

models demonstrate state-of-the-art performance on a wide range of industry benchmarks and introduce features to help you build a new generation of AI experiences. These new models provide enhanced capabilities and broader applicability across various use cases. With a focus on responsible innovation and system-level safety, the Llama 3.2

APIs 139
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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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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.