Ml
- 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.