Remove APIs Remove Benchmark Remove Entertainment Remove Metrics
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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

The decision tree provided the cut-offs for each metric, which we included as rules-based logic in the streaming application. At the end, we found that the LightGBM model worked best with well-calibrated accuracy metrics. Media Application Architect with 25+ years of experience, with focus on Media and Entertainment.

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FMOps/LLMOps: Operationalize generative AI and differences with MLOps

AWS Machine Learning

In this scenario, the generative AI application, designed by the consumer, must interact with the fine-tuner backend via APIs to deliver this functionality to the end-users. If an organization has no AI/ML experts in their team, then an API service might be better suited for them. 15K available FM reference Step 1.

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How Games24x7 transformed their retraining MLOps pipelines with Amazon SageMaker

AWS Machine Learning

Games24x7 is one of India’s most valuable multi-game platforms and entertains over 100 million gamers across various skill games. All the training and evaluation metrics were inspected manually from Amazon Simple Storage Service (Amazon S3). This is a guest blog post co-written with Hussain Jagirdar from Games24x7. cpu-py39-ubuntu20.04-sagemaker",

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