All 27 articles, sorted alphabetically
Agentic Safety in 2026
Sandboxing, capability limits, and human-in-the-loop.
Read article →ML A/B testing -- measuring model impact in production
Deep-dive on ML A/B testing: the offline-online impact gap, traffic splitting (control vs treatment), business and guardrail metrics, statistical sign…
Read article →Automated retraining architecture
Deep-dive on automated model retraining: signal-driven triggers (drift, decay, schedule), point-in-time snapshots, reproducible training, slice-aware …
Read article →Batch inference architecture
Deep-dive on offline batch inference: partitioning input into restartable shards, a batch builder that fills each accelerator, an autoscaling model-wo…
Read article →Champion/challenger model evaluation architecture
Deep-dive on champion/challenger evaluation: the champion serves and decides while the challenger shadow-scores the same live inputs risk-free, a metr…
Read article →Data labeling -- the human fuel of supervised learning
Deep-dive on ML data labeling: the labeled-data need, the labeling pipeline, clear guidelines (consistency), quality control (inter-annotator agreemen…
Read article →ML drift detection -- catching silent model degradation
Deep-dive on ML drift detection: data drift, concept drift, and prediction drift, statistical detection methods and training baselines, the ground-tru…
Read article →Ensemble serving architecture
Deep-dive on serving model ensembles in production: running several diverse models on each input and combining their predictions for accuracy and robu…
Read article →AI Evaluation Frameworks
From MMLU to your task-specific eval.
Read article →AI Feature Store Architecture in Depth
A 2500-word walkthrough of AI feature store architecture: sources, pipeline, registry, offline + online stores, point-in-time joins, symmetry, monitor…
Read article →Inference autoscaling architecture
Deep-dive on autoscaling model inference: why GPU utilisation is a broken signal under continuous batching, queue depth and TTFT as control inputs, de…
Read article →Model calibration architecture
Deep-dive on model calibration: why calibration is separate from accuracy and invisible to AUC, temperature/Platt/isotonic calibrators fit on a held-o…
Read article →Model distillation architecture
Deep-dive on knowledge distillation: soft targets and dark knowledge, temperature-softened KL loss, rationale/chain-of-thought distillation, data cura…
Read article →ML model registry
Deep-dive on the ML model registry: immutable model versions, lineage capture for reproducibility, metadata and evaluation metrics, stages and aliases…
Read article →Model serving architecture
Deep-dive on model serving: gateway admission and model routing, dynamic batching economics on GPUs, feature services and prediction caches, shadow an…
Read article →Multi-armed bandit architecture
Deep-dive on multi-armed bandits for online model and variant selection: a policy (epsilon-greedy, UCB, Thompson sampling) that routes each request to…
Read article →AI/ML pipeline architecture
Deep-dive on end-to-end ML pipelines: data lake, feature store, training, model registry, serving, monitoring, governance, and metadata orchestration.
Read article →Semi-supervised learning architecture
Deep-dive on semi-supervised learning: training on a small labeled set plus a large unlabeled pool by exploiting cluster and smoothness structure. Cov…
Read article →ML shadow deployment architecture
Deep-dive on shadow (dark-launch) deployment for ML models: asynchronous traffic mirroring off the response path, prediction logging and comparison, a…
Read article →Synthetic data pipelines
Deep-dive on synthetic training data architecture: seed corpora and prompt grids for engineered diversity, generate-and-critique loops, the cost-order…
Read article →AI Training Pipeline Architecture in Depth
A 2500-word walkthrough of a modern ML/AI training pipeline: ingestion, data lake, feature store, training, registry, eval, serving, and governance.
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