Tag: MLOps

11 articles

• Embedding Model Upgrade in Production: Zero-Downtime Migration with Dual Writes, Blue-Green Vector Indexes, and Atomic Alias Switching

  Upgrading an embedding model is more than swapping a model name. This post breaks down production migration strategies—dual writes, background re-embedding, blue-green vector indexes, atomic alias switching, rollback windows, and incremental updates—to avoid recall drift and service disruption.

  08/28/2026 LLM / Embedding / Vector Database / Model Migration / MLOps

• LLM Knowledge Distillation in Production: Controlling Student Model Degradation with Teacher Logprob Contracts, On-Policy Replay, and Top-K Tail Buckets

  A practical engineering guide to productionizing LLM knowledge distillation, covering Teacher Logprob contracts, temperature and divergence selection, On-policy replay, Top-K tail probability buckets, Tokenizer alignment, and student capability gating to prevent silent degradation and deployment failures.

  07/28/2026 LLM / Knowledge Distillation / Model Compression / On-policy Distillation / MLOps

• LLM Inference Reproducibility in Production: Controlling Output Drift with Seed Contracts, Batch Invariance, and Deterministic Kernels

  Same model, prompt, and seed can still yield different outputs. This article covers sampling state, dynamic batching, floating-point reduction, parallel topology, and version fingerprints to provide tiered reproducibility goals, implementation strategies, replay gates, and a deployment checklist.

  07/15/2026 LLM Serving / Reproducibility / vLLM / CUDA / MLOps / Batch Invariance / Deterministic Inference / Seed Contract

• LLM FP8 Fine-Tuning in Production: Amax Scaling, Layer Whitelists, and BF16 Fallback for Stable Convergence

  FP8 reduces compute and memory for LLM fine-tuning, but poor scaling, outliers, and distributed sync issues can cause silent accuracy degradation. This guide provides a complete engineering solution with Amax monitoring, layer whitelists, BF16 fallback, dual-track evaluation, and release gates.

  07/14/2026 LLM Training / FP8 / Transformer Engine / Fine-tuning / MLOps / Mixed Precision Training / Distributed Training / Amax Scaling

• LLM Model Merging in Production: Reducing Parameter Interference with TIES/DARE, Calibration Sets, and Capability Regression Gates

  Model merging combines multiple fine-tuned models without retraining, but parameter conflicts, tokenizer mismatches, and capability overlap amplify production risks. This article covers TIES, DARE, calibration set selection, artifact fingerprints, and regression gates for production deployment.

  07/14/2026 LLM / Model Merging / TIES / DARE / MLOps

• LLM Inference Reproducibility in Production: Controlling Output Drift with Batch Invariance, Fixed Seeds, and Configuration Fingerprints

  This article dissects why LLM inference can still produce drifting outputs even at zero temperature, and provides production-grade methods including fixed seeds, Batch Invariance, environment fingerprints, replay matrices, and tiered determinism gates to stabilize evaluations, audits, caching, and regression tests.

  07/12/2026 LLM Serving / Reproducibility / Batch Invariance / GPU Inference / MLOps

• LLM Model Canary Release in Production: Reducing Version Drift with Shadow Traffic, Compatibility Gates, and Auto-Rollback

  When model providers update, internal fine-tuning changes, or prompts are upgraded, online behavior can silently drift. This article covers the engineering practices of LLM canary releases—shadow traffic, compatibility gates, canary metrics, version registration, and auto-rollback—to help teams build a behavior-compatible release pipeline.

  07/08/2026 LLM Deployment / Canary Release / Model Governance / Shadow Traffic / MLOps

• LLM Fine-tuning Dataset Version Governance in Production: Using Data Lineage, Evaluation Gates, and Rollback Strategies to Prevent SFT Degradation

  A practical guide to versioning LLM fine-tuning datasets in production, covering sample lineage, train-test splits, evaluation gates, experiment tracking, rollback strategies, and pre-release checklists to prevent SFT degradation from dirty data, biased samples, or incorrect labels.

  07/07/2026 LLM Fine-tuning / SFT / Dataset Governance / MLOps / Evals

• LLM Model Artifact Supply Chain in Production: Protecting Against Weight Tampering with Safetensors, Signatures, and Admission Scanning

  A practical guide to securing the LLM model supply chain—from download, signing, and scanning to production admission. Covers safetensors, source verification, SBOMs, canary releases, and rollback strategies to mitigate weight contamination risks for open-source and internal model registries.

  07/06/2026 LLM / MLOps / Supply Chain Security / Model Registry / safetensors

• LLM Fine-Tuning Data Governance: Stabilizing Model Quality with Versioning, Deduplication, and Contamination Detection

  An engineering deep dive into governing LLM fine-tuning datasets as production assets. Covers data version tracking, multi-level deduplication, train/validation contamination detection, quality gates, and model release rollback for trustworthy, reproducible, and auditable management.

  07/03/2026 LLM Fine-Tuning / Data Governance / Data Versioning / MLOps / Production AI

• Continuous Batching in Production: Boosting LLM Serving Throughput with Dynamic Batching

  A systematic deep dive into Continuous Batching for LLM inference serving—covering principles, scheduling, engineering deployment, monitoring metrics, and go-live checklists to reduce queuing waste and maximize GPU throughput.

  07/01/2026 LLM Serving / Continuous Batching / Inference Optimization / KV Cache / MLOps