Taking models from notebook to production and keeping them healthy there.
20 in this topic
The practices that take machine learning from notebook to reliable production: versioning, automation, deployment and monitoring.
MLOps & deployment 2 min read 26 Dec 2025
Batch scoring, real-time APIs, streaming and on-device inference: choosing how predictions reach users, and deploying safely.
MLOps & deployment 2 min read 25 Dec 2025
What to monitor after deployment — data, predictions, outcomes and operations — and how to respond when things change.
MLOps & deployment 2 min read 24 Dec 2025
Why every production model needs a version, metadata and lineage, and how a model registry manages promotion and rollback.
MLOps & deployment 2 min read 23 Dec 2025
Continuous integration and delivery adapted to ML: testing code, data and models automatically before deployment.
MLOps & deployment 2 min read 22 Dec 2025
Recording parameters, metrics, data and artefacts for every training run, so results can be compared and reproduced.
MLOps & deployment 2 min read 21 Dec 2025
Reducing latency and cost of inference with batching, caching, quantisation, distillation and the right hardware.
MLOps & deployment 2 min read 20 Dec 2025
Keeping features consistent between training and serving, computing them on time, and avoiding training–serving skew.
MLOps & deployment 2 min read 19 Dec 2025
The operational practices specific to language model applications: prompt versioning, evaluation, observability, cost and safety.
MLOps & deployment 2 min read 18 Dec 2025
When and how to retrain production models — scheduled, triggered or continuous — and how to do it safely.
MLOps & deployment 2 min read 17 Dec 2025
Safely rolling out new models by testing them on live traffic before they fully replace the old ones.
MLOps & deployment 1 min read 16 Dec 2025
Keeping track of which models are running, who owns them, how they were validated and when they need review.
MLOps & deployment 1 min read 15 Dec 2025
Choosing and managing GPUs for training and inference: cloud versus on-premises, sizing, utilisation and costs.
MLOps & deployment 1 min read 14 Dec 2025
The techniques behind fast, affordable LLM inference: batching, KV caching, quantisation and serving frameworks.
MLOps & deployment 1 min read 13 Dec 2025
What to track once an LLM application is live: quality, safety, cost, latency and user feedback.
MLOps & deployment 1 min read 12 Dec 2025
Treating prompts as production artefacts: version control, testing, deployment and rollback.
MLOps & deployment 1 min read 11 Dec 2025
Practical ways to reduce the cost of training, serving and calling models without hurting quality.
MLOps & deployment 1 min read 10 Dec 2025
Beyond accuracy: unit tests, data tests, model behaviour tests and integration tests for ML systems.
MLOps & deployment 1 min read 9 Dec 2025
Running models on phones, devices and sensors: benefits, constraints and optimisation techniques.
MLOps & deployment 1 min read 8 Dec 2025
The shared infrastructure that lets teams build, deploy and monitor models consistently, and how to grow it.
MLOps & deployment 1 min read 7 Dec 2025