repoGitHubTrust 82 · PrimaryPublished 1mo agoLive · yesterday
tensorflow/serving
A flexible, high-performance serving system for machine learning models
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 55%Lattice Labs →
- PossiblePossibly related (embedding) · 49%Scaling the Horizon, Not the Parameters: Reaching Trillion-Parameter Performance with a 35B Agent →
- PossiblePossibly related (embedding) · 48%Optimize model training on Amazon SageMaker AI with NVIDIA Blackwell →
- PossiblePossibly related (embedding) · 48%How Outpost VFX Uses AWS to Accelerate AI Model Training for Visual Effects →
- PossiblePossibly related (embedding) · 47%Monitor and debug generative AI inference with SageMaker detailed metrics and Insights dashboard on CloudWatch →
- FuzzySimilar title/name (fuzzy) · 84%TraceLab: Characterizing Coding Agent Workloads for LLM Serving →
“Fuzzy title match (0.92): “TraceLab: Characterizing Coding Agent Workloads for LLM Serv” ≈ “tensorflow/serving””
- FuzzySimilar title/name (fuzzy) · 84%SMetric: Rethink LLM Scheduling for Serving Agents with Balanced Session-centric Scheduling →
“Fuzzy title match (0.92): “SMetric: Rethink LLM Scheduling for Serving Agents with Bala” ≈ “tensorflow/serving””
- FuzzySimilar title/name (fuzzy) · 84%PagedWeight: Efficient MoE LLM Serving with Dynamic Quality-Aware Weight Quantization →
“Fuzzy title match (0.92): “PagedWeight: Efficient MoE LLM Serving with Dynamic Quality-” ≈ “tensorflow/serving””
Related to
Implements
paperScaling the Horizon, Not the Parameters: Reaching Trillion-Parameter Performance with a 35B AgentpaperTraceLab: Characterizing Coding Agent Workloads for LLM ServingpaperSMetric: Rethink LLM Scheduling for Serving Agents with Balanced Session-centric SchedulingpaperPagedWeight: Efficient MoE LLM Serving with Dynamic Quality-Aware Weight QuantizationpaperTalaria: Session-Aware Serverless Serving of Hundred-Billion-Parameter LLMs
Covers
Implements (incoming)
paperBeyond Adam: SOAP and Muon for Faster, Label-Efficient Training of Machine Learning Interatomic PotentialspaperTabPack: Efficient Hyperparameter Ensembles for Tabular Deep LearningpaperQuantitative Gaussian-Process limits of Tensor ProgramspaperThe Large Cancer Assistant (LCA): A Model-Agnostic Orchestration Framework for Scalable Clinical Decision Support in OncologypaperSystematic Evaluation of Learning Rate Scheduling Strategies Across Heterogeneous Architectures
Related across the graph
paperTraceLab: Characterizing Coding Agent Workloads for LLM ServingpaperSMetric: Rethink LLM Scheduling for Serving Agents with Balanced Session-centric SchedulingpaperTabPack: Efficient Hyperparameter Ensembles for Tabular Deep LearningpaperTalaria: Session-Aware Serverless Serving of Hundred-Billion-Parameter LLMspaperQuantitative Gaussian-Process limits of Tensor ProgramsnewsMonitor and debug generative AI inference with SageMaker detailed metrics and Insights dashboard on CloudWatchpaperBeyond Adam: SOAP and Muon for Faster, Label-Efficient Training of Machine Learning Interatomic PotentialspaperScaling the Horizon, Not the Parameters: Reaching Trillion-Parameter Performance with a 35B AgentnewsOptimize model training on Amazon SageMaker AI with NVIDIA BlackwellcompanyLattice LabspaperSystematic Evaluation of Learning Rate Scheduling Strategies Across Heterogeneous ArchitecturesnewsHow Outpost VFX Uses AWS to Accelerate AI Model Training for Visual EffectspaperPagedWeight: Efficient MoE LLM Serving with Dynamic Quality-Aware Weight QuantizationpaperThe Large Cancer Assistant (LCA): A Model-Agnostic Orchestration Framework for Scalable Clinical Decision Support in Oncology