How to Set Up an MLOps Pipeline

From ad-hoc experiments to a repeatable ML delivery system - the pipeline that scales.

Most ML teams start with Jupyter notebooks and end with untracked experiments unnamed model versions and manual deployment steps nobody remembers. An MLOps pipeline replaces this with a repeatable system: tracked experiments versioned models automated testing and reliable deployment. This guide covers the setup that grows from 1 model to 100 without collapsing.

No fluff. Production-grade answers from engineers who ship AI into real products.

The MLOps Maturity Levels (Start at Level 1 Not Level 3)

Level 0: manual everything. Notebooks no tracking deploy by copying files. Where most teams start. Level 1: experiment tracking and model registry. MLflow or W&B for experiments model versioning basic automated evaluation before promotion. The right starting point for teams shipping their first production model. Level 2: automated training pipelines. Triggered retraining CI/CD for models feature store A/B deployment. Worth building when you have 3+ models in production. Level 3: fully automated ML systems. Self-healing continuous learning automated drift response. Only at extreme enterprise scale.

At Valletta Software, we focus on:

Experiment tracking: MLflow or Weights and Biases - log params metrics artifacts on every run

Model registry: MLflow Model Registry or SageMaker - version staging production archived stages

Training pipelines: Airflow or Prefect for orchestration - no manual jupyter nbconvert in prod

Feature store: Feast or Hopsworks for shared features - prevents train/serve skew

CI/CD for ML: retrain on schedule or data trigger test eval promote or reject - not manual

Data versioning: DVC for dataset version control - link model versions to exact data used

Reproducibility: seed all random operations log library versions - anyone can rebuild any run

The Feature Store and Why Train/Serve Skew Kills Production Models

Train/serve skew: the model was trained on features computed one way served on features computed differently. The model performs perfectly in evaluation and fails silently in production.

We give you more than just people. We give you top performers who drive results.

Train/serve skew prevention: same feature computation code runs in training and serving

Feature store: Feast Hopsworks Tecton - single source of truth for feature definitions

Online store: Redis for low-latency feature serving - not recompute on every request

Offline store: data warehouse for training data retrieval - point-in-time correct joins

Feature validation: Great Expectations or Pandera - validate feature distributions before training

Schema registry: track feature schema versions - breaking changes caught before training

Monitoring: compare online feature distributions to training distribution - catch skew early

Your team, your direction. We handle the rest. Rates from EUR 45/h.

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View MLOps Engineers

Build RAG pipelines, agents, and LLM integrations from day one

Ship AI features 3x faster with AI-native tooling and methodology

Deploy to production - not just Jupyter notebooks and prototypes

Evaluate output quality - hallucination detection, cost optimization, monitoring

How to Set Up an MLOps Pipeline - With Engineers Who Build Them From Scratch

Forget the hype. We make AI work in the real world.

Our engineers are trained in the latest AI tooling - Copilot, Claude Code, Cursor, LangChain, and vector databases - and use them daily to ship production AI features, not just prototypes.

Choose from a solo dev, mini team, or full squad. All powered by AI and ready to build from day one.

Lets keep it simple.

Our MLOps engineers set up MLflow experiment tracking model registry Airflow training pipelines Feast feature store and drift monitoring - starting at the maturity level that matches your team not over-engineering Level 3 for a team with 2 models.

How we work