Training & Deployment¶

Table of Contents

Training
- Large-Scale ML & Distributed Training
- Fine-Tuning & LLMs
Deployment
- Model Productionization & Scaling

Training¶

Large-Scale ML & Distributed Training¶

Parallelization: Data parallelism vs model parallelism.
Gradient Accumulation: Handling large batch sizes.
Federated Learning: Privacy-preserving distributed learning.

Fine-Tuning & LLMs¶

Efficient Fine-Tuning: LoRA, QLoRA, adapters, prompt tuning.
Memory-Efficient Training: Flash Attention, ZeRO Offloading, activation checkpointing.
Inference Optimization: KV caching, speculative decoding, grouped-query attention.
Long-Context Adaptation: RoPE interpolation, Hyena operators, recurrent memory transformers.
Safety & Alignment: RLHF, constitutional AI, preference tuning.

Deployment¶

Model Productionization & Scaling¶

Latency vs Accuracy Tradeoffs: Quantization, distillation, pruning.
Efficient Inference: TensorRT, ONNX, model sharding, mixed precision training.