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Model Monitoring After Deployment: From Drift Alerts to Operational Decisions

Model monitoring is not a dashboard placed next to a deployed model. It is the feedback system that determines whether the assumptions used during development still hold after deployment. A production model can keep returning vali...

• data analysis and statistics
• evaluation and experimentation
• production ml

Image and Video Models Beyond Generation: Evaluating Visual Embeddings and Temporal Retrieval

Image and video models are often presented through generation: a prompt becomes a picture, a clip, or a stylized scene. Generation is important, but many production systems create more value by understanding existing media. Visual...

• deep learning
• evaluation and experimentation
• multimodal and generative ai

OCR Is Not Enough: Building a Document Understanding Pipeline With Validation and Human Review

OCR is often described as converting an image into text. That definition is correct, but it is too narrow for most production document workflows. A business system rarely needs an unstructured transcript of an invoice, application...

• evaluation and experimentation
• ocr and document ai
• production ml

Useful AI Agents Need Boundaries: Designing Tool Use, Stops, and Human Review

An AI agent is useful when it can complete a bounded class of work under explicit constraints. It is not useful merely because it generates a plan, calls several tools, or produces a long transcript that resembles autonomous reaso...

• ai agents
• evaluation and experimentation
• production ml

Reliable ML Pipelines: How to Make Model Promotion Reproducible and Auditable

A machine learning pipeline is reliable when another person can rerun it, inspect its artifacts, and reconstruct why a model was promoted, blocked, or sent for review. The trained model is only one artifact in a larger chain. The...

• evaluation and experimentation
• production ml

Feature Engineering for Tabular ML: Build Features From the Past, Not the Future

Deep learning has automated many forms of representation learning, but tabular machine learning still rewards deliberate feature engineering. In business data, useful predictive structure often appears as recency, frequency, ratio...

• classical machine learning
• evaluation and experimentation
• production ml

From Notebook to Dependency: Where Data Science Ends and AI Engineering Begins

Data Science and AI Engineering overlap, but they are not interchangeable. The difference becomes visible when a model stops being an analytical result and becomes a dependency that other systems, teams, or customers rely on. A da...

• data analysis and statistics
• evaluation and experimentation
• production ml

Why One LLM Evaluation Score Is Not Enough: A Diagnostic Rubric for Answer Quality

LLM evaluation becomes unreliable when every answer is compressed into one number. A response can be fluent but unsupported, factually correct but irrelevant, complete but difficult to use, or concise because it omitted the most i...

RAG Evaluation Starts With Retrieval: How to Diagnose Evidence Failures Before Tuning the LLM

Retrieval-Augmented Generation is often discussed as an LLM architecture. In practice, however, many weak RAG answers originate before the language model receives a prompt. The retriever may return a document about the right subje...

A Model Can Stay Online and Still Fail: Monitoring Input Drift with PSI

A machine learning service can return successful responses, remain within its latency target, and still become less reliable. No exception is required. The application code may be unchanged while the data reaching the model gradua...

• data analysis and statistics
• evaluation and experimentation
• production ml