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. How to choose public benchmarks, design robust task-specific evaluations, and monitor model performance continuo…
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.
Read article →Constitutional AI: Anthropic’s Approach to Giving AI a 'Moral Compass'
The rise of powerful Large Language Models (LLMs) has brought with it a critical, existential challenge: AI alignment.
Read article →Self-Improving AI: Are We Close to the 'Recursion Point' Where AI Writes Its Own Better Code?
The ultimate aspiration of Artificial Intelligence research is to create systems that can not only learn but also continually enhance their own intelligence, far…
Read article →Synthetic Data Pipelines: Can AI-Generated Data Actually Make the Next Generation of AI Smarter?
"More data, better models" has been a consistent truth driving the rapid advancements in Artificial Intelligence, particularly for Large Language Models (LLMs).
Read article →The 'Dead Internet' Theory: Is LLM-Generated Content Ruining the Web for Humans?
The "Dead Internet Theory" began as a fringe conspiracy theory, suggesting that sometime around 2016, the internet was largely taken over by bots and AI-generated…
Read article →The Energy Crisis: The Environmental Cost of Training a Frontier Model in 2026
Artificial Intelligence, particularly the rapid advancement of Large Language Models (LLMs), is a testament to human ingenuity.
Read article →World Models: Moving from Text Prediction to Predicting Physical Reality (Sora and Beyond)
Large Language Models (LLMs) have demonstrated astonishing capabilities in text generation, understanding, and even complex reasoning within symbolic domains.
Read article →