MLOps i LLMOps

MLOps and LLMOps · id mlops

Utrzymywanie powtarzalnych, obserwowalnych wdrożeń.

Keeping deployments repeatable and observable.

In one sentence

To organizowanie treningu, wersji i wdrożeń modeli tak, żeby dało się je śledzić, porównywać i utrzymywać.

This organises model training, versions and deployments so they can be tracked, compared and maintained.

Problem it solves

Nie wiesz, skąd pochodzi działający model, dlaczego zmieniły się wyniki ani jak bezpiecznie wrócić do poprzedniej wersji.

You cannot trace the running model, explain changed results or safely return to an earlier version.

What it does

Rejestry łączą dane, eksperymenty i wersje, a testowane potoki oraz wspólne interfejsy porządkują wdrażanie i obsługę zmian.

Registries connect data, experiments and versions, while tested pipelines and shared interfaces organise deployment and change management.

Everyday example

Po gorszych odpowiedziach asystenta ustalasz wersję modelu i instrukcji, porównujesz testy oraz przywracasz sprawdzony wariant.

After an assistant's responses worsen, you identify model and prompt versions, compare tests and restore a verified variant.

This concerns you if you…

utrzymujesz model poza pojedynczym eksperymentem
potrzebujesz powtarzalnych wdrożeń i historii zmian
maintain a model beyond a single experiment
need repeatable deployments and change history

Examples

Rejestr modeli · CI/CD · monitoring
Model registry · CI/CD · monitoring

Subtopics

Automatyczne retrenowanie i CI/CD dla ML (Continuous training and CI/CD for ML): Automating an ML pipeline: retraining and redeploying a model after a defined trigger such as a schedule, new data, quality degradation or detected drift, while testing and deploying the pipeline itself like code.
Bramka LLM: jedno API do wielu modeli (LLM gateway (AI gateway)): An intermediate layer in front of many providers and self-hosted models: one unified API, central keys and access control, rate limits and budgets, fallbacks, load balancing and cost logging.
Magazyn cech (feature store) (Feature store): A shared feature platform for training and production: an offline store with feature history for building training sets and an online store serving current features at low latency. Shared definitions and point-in-time joins help limit training-serving skew and leakage from the future.
Rejestr i wersjonowanie modeli (Model registry and versioning): A central registry of model versions with metadata and lineage to experiment runs. Mutable aliases such as champion or production can decouple production-version selection from deployment code and make promotion or rollback easier.
Wersjonowanie i rejestr promptów (Prompt management and versioning): Storing prompts as immutable, numbered versions with metadata and a production alias. Linking versions to traces and evaluations supports comparison; when an application loads prompts dynamically through an alias, rollback is possible without redeploying code.
Śledzenie eksperymentów (parametry, metryki, artefakty) (Experiment tracking): Logging training runs — hyperparameters, metrics, artefacts, and code and data versions — to support result comparison, lineage and reconstruction of experiment conditions.

Sources

Stanford CS336 · Language Modeling from Scratch (2025) · reference: Budowa modeli, dane, trening, systemy, inferencja i post-training.
Full Stack Deep Learning (2022, archiwum) · reference: Pełny cykl systemu ML: dane, testy, wdrażanie, monitoring, zespoły i etyka.
FSDL · LLM Bootcamp (2023, archiwum) · reference: Aplikacje LLM, prompting, retrieval, UX i LLMOps.
DeepLearning.AI — course catalogue · course catalogue: 42 topic labels with counts, about 130 courses.