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From coordinating teams to configuring quotes, enterprise companies face immense challenges keeping sales operations running smoothly. Fortunately, Configure, Price, Quote (CPQ) software provides specialized solutions purpose-built for streamlining sales in enterprise environments. Customers engage in whichever way they prefer.
Hyper-focused on moving innovation and shareholder value forward, Kate leads with the strategic and go-to-market skills developed over 20 years as a growth-oriented B2B enterprise technology disruptor. Kate champions digital innovations that create the best customer experience and solutions.
The solution takes advantage of AWS native features from Amazon SageMaker , building a flexible and extensible framework around this. 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.
Its AI/ML solutions drive enhanced operational efficiency, productivity, and customer experience for many of their enterprise clients. Query training results: This step calls the Lambda function to fetch the metrics of the completed training job from the earlier model training step.
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.
Be backed by enterprise grade security and privacy tooling. 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. Ease of deployment through a fully managed, serverless, service.
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