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Accelerate Amazon SageMaker inference with C6i Intel-based Amazon EC2 instances

AWS Machine Learning

Import intel extensions for PyTorch to help with quantization and optimization and import torch for array manipulations: import intel_extension_for_pytorch as ipex import torch Apply model calibration for 100 iterations. Quantizing the model in PyTorch is possible with a few APIs from Intel PyTorch extensions.

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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

Based on 10 years of historical data, hundreds of thousands of face-offs were used to engineer over 70 features fed into the model to provide real-time probabilities. By continuously listening to NHL’s expertise and testing hypotheses, AWS’s scientists engineered over 100 features that correlate to the face-off event.

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Detection and high-frequency monitoring of methane emission point sources using Amazon SageMaker geospatial capabilities

AWS Machine Learning

Amazon SageMaker geospatial capabilities make it easier for data scientists and machine learning engineers to build, train, and deploy models using geospatial data. fractional change in reflectance yields good results but this can change from scene to scene and you will have to calibrate this for your specific use case.

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Operationalize LLM Evaluation at Scale using Amazon SageMaker Clarify and MLOps services

AWS Machine Learning

Evaluating these models allows continuous model improvement, calibration and debugging. Once in production, ML consumers utilize the model via application-triggered inference through direct invocation or API calls, with feedback loops to model owners for ongoing performance evaluation. html") s3_object = s3.Object(bucket_name=output_bucket,

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Detect fraudulent transactions using machine learning with Amazon SageMaker

AWS Machine Learning

To demonstrate how you can use this solution in your existing business infrastructures, we also include an example of making REST API calls to the deployed model endpoint, using AWS Lambda to trigger both the RCF and XGBoost models. This adds a useful calibration to our model. Prerequisites. Launch the solution.

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Model management for LoRA fine-tuned models using Llama2 and Amazon SageMaker

AWS Machine Learning

Additionally, optimizing the training process and calibrating the parameters can be a complex and iterative process, requiring expertise and careful experimentation. During fine-tuning, we integrate SageMaker Experiments Plus with the Transformers API to automatically log metrics like gradient, loss, etc.

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JustCall vs Talkdesk: An In-Depth Comparison 

JustCall

Some of these include: AdaAgent Assist, Airkit Assist, Hub Auto, Reach, Balto, Calabrio, PCI Pan Digital Agent Assist, Pypestream, Verint, Zingtree, Talkdesk also offers API access for all plans. When trained and calibrated correctly, the virtual agent can seamlessly guide callers to the correct resolution through self-servicing.