Ml

5 items

AI/ML Workshop — ml , onboarding , privacy
A carefully curated set of practical, highly reproducible machine learning examples (PyTorch, Hugging Face, NumPy) featuring MPS-aware benchmarks and rigorous experiment hygiene for local hardware.
ML Experiments — ml
Guidance for reproducible, resource-aware machine learning experiments; leveraging lightweight MLOps primitives, strict environment versioning, seed management, and rigorous, deterministic model evaluation protocols.
Model Serving & Inference — ml , monitoring
Principles for highly available production ML inference; utilizing immutable model registries, dynamic request micro-batching, safe canary rollouts, graceful fallback degradation, and efficient GPU memory management.
Privacy-Preserving Federated Learning Platform — algorithms , distributed-systems , ml and +1 more
A secure platform design for advanced federated learning pipelines; training models directly across edge devices without sharing raw telemetry, utilizing secure local aggregation and robust privacy safeguards.
Ragchain — ml , privacy , retrieval
A comprehensive local RAG stack (ChromaDB + Ollama) designed for strictly private, reproducible retrieval and LLM inference; heavily focusing on hybrid retrieval strategies and index versioning.

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