Tag: mlops

Why custom LLM logging leaves you flying blind in production, and how OpenTelemetry's GenAI semantic conventions turn every model call, tool invocation, and agent step into a traceable, cost-accountable span.

How a compact Python ML cheatsheet becomes useful when synthetic demos, metrics, pipelines, and version drift are tied to the model-review decisions they can actually defend.

How to turn DSPy and RAG evaluation into a production release loop with golden sets, retrieval checks, generation rubrics, regression thresholds, and versioned prompt programs.

A practical ML terminology guide for model reviews where feature definitions, data splits, task type, optimization behavior, overfitting risk, regularization, ensembles, and embeddings need to be discussed precisely.

A production-friendly pattern for pairing scikit-learn preprocessing graphs with PyTorch models so training and inference use the same feature contract.

Why tabular models drift between notebooks and production when preprocessing, sample metadata, hyperparameter search, and persistence are not treated as one scikit-learn pipeline contract.

A production-focused Vertex AI post on turning raw data, BigQuery features, online feature serving, model endpoints, monitoring, and retraining into one governed ML loop instead of another platform checklist.

A Vertex AI architecture map for teams that need to decide which Google Cloud AI services belong in the ML lifecycle, where ownership changes hands, and which older assumptions are now unsafe.

A practical AI strategy framework with a worked example that connects business levers, data readiness, pilots, evaluation, governance, deployment, and operating metrics.

A practical runbook for scoring changed rows close to the data using Snowflake Streams and Tasks or BigQuery scheduled queries and remote models.

A practical map of NVIDIA NeMo for teams that want to curate data, fine-tune open-source LLMs, evaluate them, and move from research checkpoints to production inference.

A practical playbook for turning classifier scores into reliable probabilities that can support ranking, thresholds, SLAs, and cost-sensitive decisions.

A production-friendly scikit-learn pattern for mixed tabular data, class imbalance, calibrated probabilities, threshold selection, and model persistence.