AI / ML
MLOps — models in production, not in notebooks
I build the infrastructure for ML: reproducible training pipelines, versioning of data and models, automated deployment and quality monitoring in production.
What is MLOps
MLOps brings DevOps practices — CI/CD, automation, versioning and monitoring — to the machine learning lifecycle. The key difference from ordinary DevOps is that you have to manage three independently changing artifacts at once: code, data and the model. Ordinary CI/CD has no idea what to do with a large model file, a feature-drift alert or a retraining trigger. The goal is for models to run reliably in production without degrading, and to retrain on a trigger when drift occurs — instead of staying in notebooks.
What the service includes
Platform design
I assess the current maturity of your processes and design a target ML pipeline and platform that fit your tasks and infrastructure.
Infrastructure
I set up containerization with Docker, orchestration on Kubernetes and work with GPU resources, and separate the dev/staging/prod environments.
Data and features
I introduce data versioning and a feature store: an offline store for training and an online store for low-latency inference.
Experiments and registry
I set up tracking of hyperparameters, metrics and artifacts, a model registry, and reproducibility of training from run to run.
CI/CD/CT and deployment
I automate training pipelines with data and model tests, packaging and serving via REST/gRPC for batch and online inference, canary/shadow/A-B rollouts, and continuous training on a trigger or schedule.
Monitoring and support
I set up monitoring of quality, drift and latency with alerting and retraining triggers, maintain lineage, audit and model cards. I train your team and provide support under an SLA.
Stages of ML model development
- 01
Problem framing and data
I fix the business goal and success metrics, organize data collection and labeling, and run exploratory data analysis (EDA).
- 02
Data and feature preparation
I perform cleaning and feature engineering, version the data and publish features to a feature store as a single source for training and inference.
- 03
Experiments and training
I train models with tracking of hyperparameters and metrics, select the best one and register it in the model registry.
- 04
Validation
I run offline evaluation against the metrics, check the model's robustness and fairness, and approve it for production.
- 05
Deployment
I package the model and roll it out as an API via canary, shadow or A-B testing, to compare it safely against the current version.
- 06
Monitoring and operations
I watch prediction quality, data drift, latency and cost, and set up alerting on deviations.
- 07
Retraining (CT)
On a drift signal or on schedule, the cycle returns to the data preparation and training steps — a feedback loop that closes the lifecycle.
Example of an MLOps implementation
On the left is the lifecycle (data → features → training → validation → deployment → monitoring) closed into a loop: drift monitoring feeds the flow back to training (continuous training). On the right are the cross-cutting platform services that operate at every stage; the stack is mostly open-source and deploys in the client's infrastructure.
The lifecycle as a loop
Drift monitoring loops back to training — continuous training. This is the key difference from linear DevOps, where an artifact is shipped once and does not degrade on its own.
Orchestration
Airflow or Kubeflow run all the lifecycle pipelines — from data preparation to retraining — on schedules and triggers.
Tracking and registry
MLflow tracks experiments, stores the model registry and ensures reproducibility from run to run.
Feature store
Feast serves features from the offline store for training and from the online store for low-latency inference, protecting against train/serving skew.
CI/CD/CT
Git and GitHub Actions automate training, data and model tests, and retraining, tying code, data and the model into a single process.
Containers and compute
Docker, Kubernetes and GPUs provide a single reproducible environment for training and serving across dev/staging/prod.
Tech stack
Clients
Testimonials
MLOps is the path from a notebook experiment to a reliable production service. I build the infrastructure in which training is reproducible, deployment is automated, and model quality is tracked in real time. When data drifts, the model retrains on a trigger instead of silently degrading. As a result, the model doesn’t stay a notebook demo — it runs reliably and evolves as part of the product.
Shall we discuss your task?
FAQ
How is MLOps different from ordinary DevOps? +
In DevOps you manage one artifact — code. In MLOps there are three, and they change independently: code, data and the model. Because of this you need separate practices — data and model versioning, experiment tracking, drift monitoring and continuous training — that ordinary CI/CD does not have.
What is continuous training and why retrain a model? +
The distribution of production data changes over time (data and concept drift), and a model trained on past data gradually degrades. Continuous training is automated retraining on a drift trigger or on schedule. Monitoring detects the deviation and launches the retraining cycle, closing the feedback loop without manual intervention.
Is a cloud product like SageMaker, Vertex AI or Azure ML required? +
No. A full platform can be assembled on open-source: MLflow for tracking and registry, Docker and Kubernetes for compute and serving, Kubeflow or Airflow for orchestration. Managed platforms speed up the start but lock you into a vendor; an open-source stack deploys in the client's own infrastructure. The choice depends on your data requirements, budget and team.
What is a feature store and why have offline and online stores? +
A feature store is a single source of features for training and inference. The offline store serves historical data for training, the online store serves the same features with low latency during inference. A shared layer protects against train/serving skew, where the production model receives features computed differently than during training.
Which maturity level should I start from and how long does it take? +
MLOps maturity is described in levels: from a manual process (level 0) to an automated training pipeline (level 1) and full CI/CD/CT (level 2). I start with a working, reproducible pipeline and add automation as the processes and team are ready. I don't quote fixed timelines — the scope depends on the current state and the target level.
Do you do LLMOps? +
Yes, optionally and on top of the same platform. This means RAG with a vector store, tools for evaluating answer quality, and guardrails for LLM behavior. The same principles of versioning, monitoring and CI/CD apply to working with foundation models.