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anyscale
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Ray in 2023: Ray in Reflection
anyscale
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32
Evaluating LLM Applications is hard
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43
Developing and serving RAG-Based LLM applications in production
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48
Ray_Essentials__Introduction_to_Ray_for_machine_learning.pdf
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75
How to build a serverless database cloud service
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53
Multi-Region/Cloud Ray Pipeline with Distributed Caching
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66
Modern Compute Stack for Scaling Large AI/ML/LLM Workloads
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42
5 Painful Lessons using LLMs
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58
How continuous batching enables 23x throughput in LLM inference
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630
Ray Community LLM August Meetup
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33
Bay Area Ray June Meetup Community Talks
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25
Offline Batch Inference: Comparing Ray, Apache Spark, SageMaker
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60
Streaming distributed execution across. CPUs and GPUs
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56
Ray NYC ML/AI meetup community talks March 22, 2023
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85
Daft: Pyton Distributed DataFrame for Complex data
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89
State of Ray Serve in 2.0
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160
Scaling to/from zero on demand with Serve Handle API
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100
Smart shortcuts for bootstrapping a modern NLP project
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120
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