7 Steps to Build Your First MLOps Pipeline

You have a model that performs well in testing, but the uncertainty of production still keeps you up at night. One configuration error or a shift in data can quickly turn reliable predictions into inconsistent results for real users.

Moving a model from experimentation to production is more than deployment. MLOps brings DevOps discipline into the data-driven world of machine learning. Unlike traditional software, ML systems must manage changing datasets, experiment history, and regular retraining. 

Without a systematic approach for versioning, testing, and monitoring, reliability is hard to maintain. Here is a step-by-step process to build your first MLOps pipeline. You’ll learn how to connect experimentation to production through a complete, reproducible pipeline that grows with your business needs.

What is an MLOps pipeline?

An MLOps pipeline is an automated workflow that covers the end-to-end machine learning lifecycle from data ingestion to monitoring in production. It applies DevOps principles to the unique demands of ML, including data, features, and models, ensuring reproducibility at every stage.

Where a standard software pipeline tracks only code, an MLOps pipeline must track three connected streams:

• Data – raw inputs, processed features, transformations

• Models – trained artifacts, hyperparameters, evaluation metrics

• Application code – logic and infrastructure for serving predictions

Because changes in any stream can affect predictions, MLOps operates as a continuous loop. Teams design, build, deploy, monitor, and retrain whenever data shifts or business needs change.

This complexity is why experts view MLOps as a continuous loop rather than a linear path. You design, build, deploy, monitor, and circle back when data shifts or your business needs change. This progression follows a maturity model:

• Level 0: mostly manual, brittle scripts; deployments are rare and risky

• Level 1: automated training and basic deployment bring repeatability, but monitoring is minimal

• Level 2: full CI/CD, robust observability, and rollback paths make releases routine and safe

Knowing where you stand clarifies your next move. At level 0, simply automating data checks is progress. At level 1, adding solid monitoring unlocks reliability. The goal remains constant—connecting experimentation to production so your model delivers consistent value.

Core components of an MLOps pipeline

A complete pipeline covers every phase of the ML lifecycle.

• Business and ML solution design starts when you convert business goals into measurable KPIs, defining success metrics, constraints, and service pattern (batch, real-time, or streaming). Clear targets here become quality gates later.

• Data ingestion and preparation involves creating automated pipelines that clean, validate, and version every data transformation. When you version datasets, you ensure training consistency, but true reproducibility also requires versioning code, controlling environments, and managing randomness.

• Experimentation and model development gives you space to test algorithms and features, with every experiment logged alongside code, data references, and metrics so valuable work never disappears.

• Training pipeline takes over once your experimentation stabilizes. Automated jobs retrain your model when code changes or new data arrives. Containerized environments prevent dependency issues, and each run creates a versioned artifact.

• Model validation and evaluation acts as your quality control. Before promotion, automated tests check accuracy, speed, fairness, and stability. Failing any threshold stops the process, preventing weak models from reaching users.

• Model packaging and artifact management ensures your approved models are containerized and stored with metadata linking them to their exact data snapshot and code version, making audits or rollbacks simple.

• CI/CD for ML coordinates the workflow where continuous integration checks new code, while continuous delivery promotes artifacts through testing environments with automated checks and, when needed, manual approvals.

• Deployment and serving makes your models available to answer requests—through REST APIs, batch jobs, or edge devices. Safe rollout strategies like blue/green or canary deployments limit damage if issues appear.

• Monitoring and observability provides real-time dashboards tracking input quality, prediction patterns, response times, and business metrics. Alerts trigger when performance drops below agreed standards, eliminating blind spots common in first ML deployments.

• Continuous improvement closes the loop as monitoring insights drive retraining or feature updates. Since everything is versioned, you can reproduce issues, fix them, and roll forward confidently.

These components work together as a system. Data quality feeds experimentation; validation protects deployment; monitoring triggers the next training cycle. Your MLOps pipeline isn't just handoffs—it's a living system that adapts as data, infrastructure, and business needs evolve.

Why is an MLOps pipeline important?

Implementing a comprehensive MLOps pipeline is a business necessity for any organization serious about AI. Without proper MLOps practices, you face exponential technical debt as models multiply, critical knowledge remains trapped in individual team members' heads, and production issues take days instead of minutes to resolve.

The most compelling reason to invest in MLOps is risk reduction. A single hallucinating model or data leak can damage customer trust irreparably, while silent performance degradation steadily erodes business value. Proper pipelines create safety nets that catch issues before they reach users.

Beyond risk, MLOps dramatically accelerates your ability to innovate. Teams without pipelines spend most of their time on manual data preparation, deployment troubleshooting, and monitoring setup rather than building new capabilities. Automation frees your best talent to solve novel problems instead of fighting infrastructure fires.

Finally, MLOps provides the governance foundation increasingly required by regulators and enterprise customers. As AI regulation intensifies, organizations without clear model lineage, bias monitoring, and auditability face compliance nightmares. A well-designed pipeline builds these capabilities in from the start rather than bolting them on reactively.

Step-by-step guide to building your first MLOps pipeline

Building a production pipeline becomes much simpler when you break it into focused steps, each solving a problem you're likely facing now.

This approach blends proven practices with practical shortcuts so you can start small and expand later. Adapt these recommendations to your current tools—nothing here requires specific cloud providers or orchestrators.

Step 1 – Define your objectives and success metrics

Many models fail before launch because teams never agree on what success looks like. Before writing pipeline code, focus on business objectives, not just accuracy. Define the user problem, the decision your model influences, and the cost of mistakes.

Start by setting concrete KPIs—like increased conversion rates or reduced manual reviews—that will serve as quality gates. Also, establish technical thresholds. Specify precision, recall, speed, and freshness requirements for any model to ship. 

Using Galileo's custom metrics feature, you can define business-specific evaluation criteria that align directly with your KPIs.

Document these in a simple requirements sheet with stakeholder sign-off. This prevents scope creep and late surprises. Include your serving strategy, too. Is this batch, online, or streaming? That choice determines latency budgets and uptime requirements, affecting everything from feature design to infrastructure costs.

At higher MLOps levels, you'll always want to pair clear KPIs with explicit SLAs. Focus on one high-impact use case first so your automation stays simple and debuggable.

Step 2 – Set up data ingestion and version control

Reproducibility vanishes when you move data manually from laptops. Replace ad-hoc scripts with automated ingestion that validates schemas every run. Even a basic nightly job that fetches tables, removes bad records, and logs checksums beats copy-paste workflows.

Galileo's Data Bench can automatically identify schema changes, outliers, and distribution shifts across batches before they impact training.

Make sure to version both raw data snapshots and every transformation. Tools like DVC work with Git to give your datasets the same branching and tracking that your code has. This lets you trace any prediction back to its training data—a key audit requirement.

As your features stabilize, store vetted columns in a feature store to avoid rebuilding the same logic for each model. Whether you use a tool or build your own, you'll get consistent features across training and serving, plus faster experimentation.

Quick win: add schema validation to your CI. If column types change or required fields vanish, the pipeline fails early instead of training on bad data.

Step 3 – Implement reproducible model training and experiment tracking

Without systematic tracking, you'll wonder "Which settings worked last month?" or retrain blindly when metrics drop. Start by containerizing your training environment—use identical Docker images locally and on clusters to avoid "works on my machine" problems.

Essential components for reproducible ML experiments include:

• Experiment tracking tools that record code versions, dataset references, parameters, and metrics

• Metadata management that creates a searchable history you can reproduce with one command

• Version control for both code and data to maintain complete lineage for auditability

• Environment standardization through containers to ensure consistent dependencies

• Parameterization of all configuration variables to avoid hardcoded settings. Galileo's prompt versioning and organization features help manage prompt variations systematically

Big datasets make iteration slow. Distributed training or managed services speed things up, but standardize evaluation first. Match offline metrics to your business KPIs; don't optimize for F1  when the business needs faster approvals.

Small change, big impact: fix random seeds and log them. Running the same code should produce identical models unless the data actually changes.

Step 4 – Automate model validation and testing

Manual checks miss edge cases that break in production. Move validation earlier by automating it right after training. Create gates that block promotion when precision falls below targets, latency exceeds limits, or fairness metrics slip for key groups.

Go beyond accuracy testing. Performance tests simulate production traffic to find memory issues or GPU bottlenecks. Integration tests run the full data-to-prediction flow on realistic samples, ensuring feature pipelines, model code, and serving all work together. This complete testing defines mature MLOps.

Implement drift detection to prevent silent failures. Create validation sets that mirror real traffic and run canary tests against them. If a new model behaves oddly on that data, you'll catch it before users do.

For generative AI applications, Galileo helps identify blind spots and track changes in model behavior, particularly crucial for debugging hallucinations in LLMs.

Practical tip: maintain a library of known problem cases—rare classes, extreme values, tricky inputs—and include them in every test suite.

Step 5 – Deploy with CI/CD integration

Moving model files to servers manually might work once, but it won't scale. Coordinate three pipelines working together: data, model, and application. Any change—new data, feature code updates, or API changes—should trigger automated build, test, and deploy cycles.

Essential CI/CD components for ML deployments include:

• Containerized packaging that bundles your model with its preprocessing code to ensure consistency between testing and production

• Promotion workflows that move models through dev, staging, and production environments with appropriate validation gates

• Automated regression testing that validates model performance against benchmarks before any production deployment

• Rollback mechanisms that allow immediate reversion to previous stable versions when issues arise

• Deployment strategies like blue/green or canary approaches that limit risk during rollout. Galileo's runtime protection can stop prompt attacks, data leaks, and hallucinations in production

• Version tagging and tracking to maintain clear lineage between deployed models and their source code/data

Follow a clear promotion path: dev to staging to production, with automated tests at each step and manual approval only where risks demand it.

Implement blue/green or canary deployments to send small amounts of traffic to new models, watch metrics, and roll back instantly if needed. Real-time APIs aren't always necessary. For bulk predictions—like nightly recommendations—batch jobs can be cheaper and simpler to run.

Consider special cases like mobile that may need embedded models. Match deployment style to your speed needs, budget, and resource limits rather than defaulting to online APIs. Tag every deployed version in your registry. If metrics drop, you should be able to revert with one command.

Step 6 – Monitor performance and set up alerting

Once your model serves real users, blind spots become liabilities. Good monitoring covers four areas: functional metrics, data quality, system health, and business impact. Set up dashboards to show accuracy alongside feature drift (PSI), response times, and revenue indicators so you spot patterns instantly.

Galileo provides end-to-end visibility with clear, visual tracking of your AI application's performance—from prompt design to production, all in one unified interface.

Sudden outage alerts are obvious, but slow degradation often causes more harm. Set checks that catch gradual drops in precision or creeping drift in important features.

Store monitoring data with model and data version IDs; you'll find root causes faster when you can connect an error spike with last night's data refresh. Remember that infrastructure issues can look like model problems. Slow responses might come from noisy neighboring services, not concept drift. Track CPU, GPU, and memory in the same view to isolate problems quickly.

Create runbooks so anyone on call—ML engineer, data scientist, or SRE—knows exactly how to investigate, retrain, or roll back.

Step 7 – Establish a feedback loop for continuous improvement

Shipping version one is just the beginning. Data patterns evolve, product needs change, and performance bars rise. Set clear triggers for retraining: drops in production metrics, significant feature drift, or regular schedules. Automate retraining so it runs, validates, and registers new models without constant supervision.

Maintain a model registry with lifecycle states—staging, production, archived—and add notes explaining why each version was promoted or retired. This transparency helps with compliance and makes onboarding new team members easier. Mature MLOps treats retraining as routine, not an emergency.

Using Galileo's Luna-2 evaluation model reduces both latency and cost for metric evaluations during retraining cycles.

User feedback is invaluable. Whether from manual reviews, customer reports, or downstream metrics, channel that information back into feature engineering and labeling. Over time, you'll build knowledge of failure patterns and fixes that speed up incident response.

Schedule regular model reviews with stakeholders. These meetings reveal changing business priorities early, letting you adjust goals before your pipeline optimizes for outdated targets.

By working through these seven steps, you'll replace one-off heroics with systematic automation, versioning, and visibility. The result is a pipeline that grows with your data volume and your organization's ambitions—ready for whatever your next model needs.

Accelerate your MLOps pipeline with Galileo

Even the most robust MLOps pipeline can't catch every edge case before deployment. Galileo bridges this critical gap by providing comprehensive ML observability specifically designed for today's complex AI systems.

• Detect data issues before they impact training with Galileo's data bench that automatically identifies schema changes, outliers, and distribution shifts across batches, allowing you to fix quality problems at the source

• Compare model performance across segments during experimentation to quickly identify which candidates handle edge cases best, eliminating hours of manual analysis when tuning parameters

• Validate models with realistic traffic in pre-production using shadow mode and segment-level metrics that catch subtle quality issues that traditional test suites miss

• Monitor production systems holistically with dashboards that track feature changes, prediction patterns, and business metrics in a unified view, with automated alerts and remediation when thresholds are breached

Start with Galileo to illuminate your entire ML pipeline and prevent quality issues before they reach your users.