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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

We explored nearest neighbors, decision trees, neural networks, and also collaborative filtering in terms of algorithms, while trying different sampling strategies (filtering, random, stratified, and time-based sampling) and evaluated performance on Area Under the Curve (AUC) and calibration distribution along with Brier score loss.

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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. Before introducing this API, the KV cache was recomputed for any newly added requests. Be mindful that LLM token probabilities are generally overconfident without calibration.

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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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Auto-labeling module for deep learning-based Advanced Driver Assistance Systems on AWS

AWS Machine Learning

AV/ADAS teams need to label several thousand frames from scratch, and rely on techniques like label consolidation, automatic calibration, frame selection, frame sequence interpolation, and active learning to get a single labeled dataset. His core interests include deep learning and serverless technologies.

APIs 79
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Detection and high-frequency monitoring of methane emission point sources using Amazon SageMaker geospatial capabilities

AWS Machine Learning

Given that the SearchRasterDataCollection API uses polygons or multi-polygons to define an area of interest (AOI), our approach involves expanding the point coordinates into a bounding box first and then using that polygon to query for Sentinel-2 imagery using SearchRasterDateCollection.

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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.