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

AWS Machine Learning

Overview of the technology EC2 C6i instances are powered by third-generation Intel Xeon Scalable processors (also called Ice Lake) with an all-core turbo frequency of 3.5 Quantizing the model in PyTorch is possible with a few APIs from Intel PyTorch extensions. times greater with INT8 quantization.

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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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Boost inference performance for Mixtral and Llama 2 models with new Amazon SageMaker containers

AWS Machine Learning

Be mindful that LLM token probabilities are generally overconfident without calibration. TensorRT-LLM requires models to be compiled into efficient engines before deployment. Before introducing this API, the KV cache was recomputed for any newly added requests. For more details, refer to the GitHub repo.

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

AWS Machine Learning

In the era of big data and AI, companies are continually seeking ways to use these technologies to gain a competitive edge. Additionally, optimizing the training process and calibrating the parameters can be a complex and iterative process, requiring expertise and careful experimentation.

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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.

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JustCall vs CloudCall: Which is the Best?

JustCall

Businesses have to make sure that they use technology to stay ahead. The fact that you can submit a request with JustCall engineers to expand this library of integrations is a cherry on the cake! Not to forget that those having Premium and Custom plans can request API and Webhook access to do it at their will!