CI/CD for MLOps

You can learn how CI/CD helps Machine Learning projects

Course #5 of MLOps Specialization · Coming Soon

Most ML models fail in production not because the model is bad — but because there’s no reliable process behind it. Every deployment is manual. Every retrain is ad hoc. Every rollback is a scramble. CI/CD for MLOps gives you the process that makes ML production-ready: automated pipelines for data, training, packaging, and deployment, with safety gates at every stage.

  • Build CI/CD pipelines purpose-built for ML — covering data contracts, training smoke tests, and model packaging in a single automated flow
  • Implement safe rollout patterns — shadow traffic, canary releases, and blue/green — at the model layer, not just infrastructure
  • Set up promotion gates that enforce metric thresholds, eval reports, and approver checks before any model reaches production
  • Apply supply chain security basics, observability hooks, and cost guards without leaving CI/CD
  • Leave the course with a golden pipeline template ready to apply to your own ML projects
Join Waitlist — Get Early Access Be first in line · Subscriber pricing at launch
⚙️
15 Content ModulesData CI to Cost-Aware CI/CD
📋
3 Case StudiesChurn · Time Series · LLM Inference
🏁
Capstone ProjectGolden pipeline template
🏆
Certificate of CompletionStep 5 of the MLOps Specialization
🔄
GitHub Actions · Docker · MLflowCourse #5 · Coming Soon

Sound familiar?

Your ML deployments work — until they don’t. And when they don’t, nobody knows why.

ML teams that lack CI/CD don’t just deploy slowly — they deploy dangerously. Manual steps, no automated tests, no promotion gates, no rollback procedure. When a bad model reaches production, the best-case outcome is a scrambled hotfix. The worst case is business impact that takes days to trace back to a model that slipped through unverified.

🤹
“Deploying a new model version is a team event, not an automated process.”
If deploying a model requires Slack messages, manual notebook runs, and someone watching the metrics dashboard live — you don’t have a deployment process. You have a ritual. CI/CD replaces rituals with reproducible automated pipelines that work the same way every time.
🔍
“I know CI/CD for software — but ML models don’t fit the standard pipeline.”
Training runs aren’t builds. Model evaluation isn’t a unit test. Drift isn’t a bug. The patterns that work for software CI/CD need to be extended — not replaced — to handle data contracts, smoke training, model packaging, and serving pattern selection. This course teaches exactly that extension.
💥
“A bad model reached production and we didn’t catch it until users reported problems.”
Without automated evaluation gates, shadow traffic validation, and rollback-ready deployments, bad models will reach production. This isn’t a rare failure — it’s the predictable outcome of deploying ML without CI/CD. The question is how long it takes you to catch it.
This course was built to solve exactly that. CI/CD for MLOps gives you the complete automation stack — from the first data contract check to the last canary promotion — so your team delivers ML updates with the speed and safety that production systems require.

What makes this different

Not a DevOps CI/CD course with ML bolted on.
ML CI/CD designed from first principles.

Standard CI/CD courses treat model deployment like any other artifact deployment. This course is built around the problems that are unique to ML — data quality, training reproducibility, model evaluation, drift-triggered retraining — and builds a complete automation stack around them.

✓

Three distinct CI types — Data CI, Training CI, Packaging CI

Most CI/CD courses cover code CI. ML needs three more: Data CI for schema and quality checks on every change, Training CI for reproducible smoke runs and eval reports, and Model Packaging CI for container builds, image scanning, and format validation. Each is a separate module with its own patterns.

✓

One project, end to end: Customer Churn Prediction Service

The Customer Churn Prediction Service — an API + batch pipeline — runs through every module. You see how Data CI feeds Training CI, how Training CI gates Model Packaging, how the registry gates deployment, and how the deployment pipeline feeds into monitoring. The pipeline is one connected system, not isolated exercises.

✓

Safe rollout patterns applied specifically to ML models

Shadow traffic, canary releases, and blue/green aren’t new to DevOps — but their application to ML models has specific wrinkles. Rollback criteria at the model layer, auto-rollback on SLO breach, shadow comparison of model outputs: these are covered as ML-specific patterns, not generic DevOps theory.

✓

Golden pipeline template you take away from the course

The Reuse & Templates module and the Capstone produce a reusable pipeline template — the golden pipeline that covers train → package → register → deploy for any new ML model. You leave the course with a working artifact, not just knowledge.

✓

Covers LLM CI/CD patterns alongside classical ML

The LLM Inference case study applies the CI/CD framework to a generative AI workload — image build for large model containers, cold-start testing, and canary releases using quality probes instead of traditional metrics. ML CI/CD in 2025 means handling both.

✓

Security, observability, and cost — without separate courses

Supply chain security basics (SBOM, image signing, least-privilege tokens), observability hooks (pipeline metrics, SLO linkage), and cost-aware CI/CD (cache strategy, right-sizing runners, fail-fast thresholds) are all included as modules — with explicit handoffs to the dedicated specialization courses for deeper coverage.

❌ Generic DevOps CI/CD courses

—Treat model deployment like any software artifact
—No data quality checks or training reproducibility
—No model evaluation gates or registry promotion
—Rollback means “revert the code” — not “restore the previous model”

✓ CI/CD for MLOps

→Three ML-specific CI types built from the ground up
→Data contracts, smoke training, and eval reports as first-class pipeline steps
→Registry promotion gates with metric deltas and approver checks
→Model-layer rollback with auto-rollback on SLO breach

Learning outcomes

By the end of this course, you’ll be able to:

Six CI/CD capabilities that give your ML team a reliable, automated path from code change to production — for classical ML and LLM workloads.

⚙️ ML Pipeline CI
✓
Build complete CI pipelines for ML — covering data contracts, smoke training, reproducibility checks, and model packaging in one automated flow
Replace manual deployment rituals with a single pipeline that validates data, runs a fast training check, packages the model, and gates promotion — triggered on every pull request.
🚀 Safe Rollouts
✓
Implement shadow traffic, canary releases, and blue/green deployments — with rollback criteria defined at the model layer, not just the infrastructure layer
Run a full shadow → canary → promote flow for the Customer Churn model, with auto-rollback on SLO breach and a fast revert path when things go wrong.
✅ Quality Gates
✓
Set up ML-specific test strategies and promotion gates — code, data, model, and non-functional checks — that block a bad model before it reaches production
Configure the four test categories (code, data, model, non-functional) as pipeline gates, with metric delta thresholds and eval report requirements at each promotion stage.
📦 Registry & Deployment
✓
Configure model registry promotion gates and deployment pipelines for both batch scoring and online serving — with health checks and secret management
Build the registry-to-deployment handoff — promoting from Staging to Production only when metric gates pass — and configure both a batch CronJob and an online FastAPI/KServe endpoint from the same pipeline.
🔒 Security & Observability
✓
Add supply chain security basics and observability hooks to your pipelines without needing separate tooling — SBOM, image signing, pipeline metrics, and SLO linkage
Know what belongs in the CI/CD pipeline versus the dedicated Security and Observability courses — and implement the baseline from each domain inside the pipeline itself.
📐 Reusable Templates
✓
Create reusable CI/CD workflows and a golden pipeline template — so every new ML model in your organisation gets the same automated path from day one
Build a parameterized golden pipeline that covers the full train → package → register → deploy sequence and can be applied to any new ML project in your organisation without rebuilding from scratch.

Each outcome maps directly to a course module — see the full curriculum below.

View full curriculum ↓

What you’ll learn

Course Curriculum

15 Content Modules
3 Case Studies
1 Capstone Project
GH Actions Primary CI Tool
#5 Specialization
🗺️01
Introduction 6 lessonsProject setup & repository structure
+
  • 📹 Course Introduction
  • 📹 About Your Instructor
  • 📹 Course Structure
  • 📹 Customer Churn Prediction Service — API + Batch Pipeline Project
  • 📹 Monorepo vs. Multi-repo for MLOps — Code, Data, and Infra Folders
  • 📹 GitHub Repositories
🔀02
CI Basics for ML Projects 4 lessonsBranching · hooks · PR checks · caching
+
  • 📹 Branching Model and Naming — Feature / Bugfix / Release
  • 📹 Pre-commit Hooks — Format, Lint, Security Checks
  • 📹 Fast PR Checks — Unit Tests, Style, Type Hints
  • 📹 Caching in CI — pip Cache, Docker Layer Cache
🗄️03
Data CI — Contracts & Quality on Every Change 4 lessonsSchema validation · quality checks · PR artifacts
+
  • 📹 Data Contracts and Schemas — What Must Not Change
  • 📹 Sample-Based Checks — Freshness, Nulls, Ranges, Categories
  • 📹 Contract Tests for Features — Breaking vs. Non-Breaking Changes
  • 📹 Generating Quick Data Reports as PR Artifacts
🔬04
Training CI — Fast, Deterministic, Reviewable 4 lessonsSmoke training · reproducibility · eval artifacts
+
  • 📹 Smoke Training on a Small Slice with Fixed Seed
  • 📹 Reproducibility Check — Same Code/Data → Same Metrics
  • 📹 Offline Eval Report — AUC/PR, Calibration — as Pipeline Artifact
  • 📹 Time and Cost Limits for CI Training Jobs
📦05
Model Packaging CI 4 lessonsSignatures · formats · containers · scanning
+
  • 📹 Model Signatures — I/O Schema and Validation
  • 📹 Packaging Options — pyfunc / ONNX / TorchScript
  • 📹 Container Build — Multi-Stage, Slim Runtime
  • 📹 Image Scan and Size Budgets
🔖06
Model Registry & Promotion Gates 4 lessonsStages · gates · model cards · release notes
+
  • 📹 Register Model with Links to Code, Data, and Metrics
  • 📹 Stages — Dev → Staging → Production
  • 📹 Promotion Gates — Metric Deltas, Eval Report, Approver Check
  • 📹 Model Card and Release Notes Generated from CI Data
🚢07
Deployment Pipelines 4 lessonsBatch · online serving · health checks · secrets
+
  • 📹 Batch Scoring Deploy — Job/CronJob Template + Schedule
  • 📹 Online Serving Deploy — KServe / Seldon / BentoML or FastAPI + Ingress
  • 📹 Health Checks — Readiness/Liveness Probes and Timeouts
  • 📹 Secrets and Configs — Env Vars, ConfigMap/Secret Basics
🕶️08
Safe Rollouts — Shadow, Canary, Blue/Green 4 lessonsShadow traffic · canary · revert path · load test
+
  • 📹 Shadow Traffic — Compare Outputs Without User Impact
  • 📹 Canary Releases — %-Traffic Split, Auto-Rollback on SLO Breach
  • 📹 Blue/Green Switch and Fast Revert Path
  • 📹 Load Test Step in Pipeline — K6 / Locust Job
🧪09
Test Strategy & Quality Gates for ML CI/CD 4 lessonsCode · data · model · non-functional checks
+
  • 📹 Code Tests — Unit, Integration, Contract Tests
  • 📹 Data Tests — Schema, Drift Pre-Checks, Sample Integrity
  • 📹 Model Tests — Performance Smoke, Fairness Spot-Check
  • 📹 Non-Functional Checks — Latency, Memory, Cold-Start
🌍10
Environments & Configuration 4 lessonsDev/staging/prod · versioning · feature flags · secrets
+
  • 📹 Dev / Staging / Prod Flows — One Pipeline, Different Params
  • 📹 Versioning and Semantic Releases — Tags, Changelogs
  • 📹 Feature Flags and Config-as-Data for Experiments
  • 📹 Secrets Rotation and Minimal Access
🖥️11
Runners, Compute & Artifacts 4 lessonsHosted vs self-hosted · GPU runners · artifact stores
+
  • 📹 Hosted vs. Self-Hosted Runners — When You Need GPU Runners
  • 📹 Artifact Stores and Retention — Logs, Models, Reports
  • 📹 Parallel Jobs and Matrices — Python Versions, OS
  • 📹 Queue Limits, Concurrency, and Cancellation Policies
📡12
Observability Hooks for Pipelines 4 lessonsPipeline metrics · audit logs · SLO linkage
+
  • 📹 Emit Pipeline Metrics — Durations, Pass/Fail, Queue Time
  • 📹 Store Logs and Artifacts for Audits and Root Cause Analysis
  • 📹 Link Deploy Step to Service SLOs — Error Rate, Latency
  • 🔍 Hand-off to MLOps Observability course for deep dashboards (Course #7)
🔒13
Supply Chain Security 4 lessonsSBOM · image signing · least-privilege tokens
+
  • 📹 SBOM Generation and Dependency Scanning
  • 📹 Sign Images and Verify on Deploy
  • 📹 Least-Privilege Tokens for CI/CD
  • 🔍 Hand-off to MLOps Security & Compliance course for audits (Course #8)
📐14
Reuse & Templates 4 lessonsShared libraries · golden pipeline · composition
+
  • 📹 Reusable Workflows and Shared CI Libraries
  • 📹 Pipeline Composition — Train → Package → Register → Deploy
  • 📹 Parameterized Jobs per Repo — API, Batch, Feature Generation
  • 📹 Golden Pipeline Template for New Models
💰15
Cost-Aware CI/CD 4 lessonsCache strategy · runner sizing · fail-fast
+
  • 📹 Cache Strategy and Job Timeouts
  • 📹 Right-Size Runners and Schedules
  • 📹 Fail-Fast on Thresholds — Avoid Noisy Retriggers
  • 🔍 Hand-off to FinOps course for full cost guardrails
📋16
Case Studies 3 studiesChurn · Time Series · LLM Inference
+
  • 📋 Churn Service — End-to-End PR → Canary → Promote
  • 📋 Time Series Forecasting — Scheduled Retrain + Batch Deploy
  • 📋 LLM Inference — Image Build, Cold-Start Test, Canary on Quality Probes
🏁
Capstone Project — Golden Pipeline for Your Own Model
Apply the complete CI/CD pipeline to your own ML project — or extend the Customer Churn Service pipeline with a new feature. You leave the course with a working golden pipeline template, reviewed by Alex.
Included

Is this course right for you?

Built for engineers who want to stop deploying ML models by hand

You don’t need to be a data scientist. You need to understand CI/CD — and you need to know how to extend it to handle the parts that are unique to ML systems: data, training, evaluation, and model-layer rollback.

⚙️
DevOps & MLOps Engineers
Own the ML pipeline, not just the infrastructure
You set up CI/CD for software teams. Now your organisation is doing ML and the same tools don’t quite fit. This course extends what you already know — branching models, pipeline stages, artifact management — into the ML-specific layer: data contracts, smoke training, model packaging, and registry promotion.
→ By the end you’ll have a complete, working ML CI/CD pipeline that your data science team can ship through reliably.
💻
Software Engineers supporting ML teams
Build the automation your ML team is asking for
You’re tasked with “making the model deployment more reliable” without a clear spec. This course gives you the complete picture: what a data scientist needs from a pipeline, what the ML model needs in terms of gates and validation, and how to wire it all together in a maintainable way.
→ The golden pipeline template and reusable workflows modules directly produce the artifacts your team will use in production.
🖥️
Platform & Infrastructure Engineers
Design ML delivery infrastructure that scales
Runner configuration, artifact storage, cost-aware scheduling, supply chain security, and observability hooks — the infrastructure decisions behind ML CI/CD are platform engineering decisions. This course gives you the ML context to make those decisions correctly.
→ The Runners, Compute & Artifacts and Supply Chain Security modules are directly applicable to the infrastructure you design for ML teams.
📋
Technical Leads & Engineering Managers
Set the standard for how your team delivers ML
If you lead a team that ships ML models, you need to define what “done” means at every stage — promotion criteria, test requirements, rollback criteria, and cost guardrails. This course gives you the vocabulary and the framework to set those standards clearly.
→ The Test Strategy and Promotion Gates modules map directly to the engineering standards you’ll write for your team.

Prerequisites — what you need before starting:

✓ You need
✓ Solid CI/CD experience — GitHub Actions, GitLab CI, or equivalent
✓ Python for MLOps (Course #2) — or equivalent Python skills
✓ MLOps Fundamentals (Course #1) — field overview strongly recommended
✓ Docker basics — building and running containers
✗ You do NOT need
✗ Experience training or building ML models
✗ Data science or statistics background
✗ Prior MLflow or model registry experience
✗ Kubernetes expertise — basic container concepts are enough

MLOps Specialization path

This is Course #1 of the full
MLOps Specialization

Each course builds on the previous one. Start here to get your field overview – then go as deep as your career requires.

1
Available now
MLOps Fundamentals
Full-field observability: AI/ML landscape, model lifecycle, CI/CD, infrastructure, monitoring, security, cost, and career paths – with real examples and a GitHub project.
Enroll → View course
2
Available now
Python for MLOps
Practical Python skills for MLOps contexts – scripting pipelines, working with APIs, handling data, and automating ML workflows. No data science prerequisites
Enroll → View course
3
Available now
Machine Learning Algorithms for MLOps
The most widely used ML algorithms explained for engineers who operate them, not develop them – behavior in production, resource footprint, and deployment characteristics for each model type.
Enroll → View course
4
Coming soon
Machine Learning Model Lifecycle
From experiment tracking to production monitoring – MLflow, model registry, serving patterns, drift detection, retraining, and rollback runbooks. 9 hands-on labs on the Customer Churn Prediction project.
🔔 Notify me
5
▶ This course · Coming Soon
CI/CD for MLOps Coming Soon
Build ML pipelines from scratch: data CI, training CI, model packaging, container builds, registry promotion, deployment pipelines, safe rollouts, and LLM CI/CD – validated against 1k+ real job descriptions.
Join Waitlist – Early Access →
6
Coming soon
Data for MLOps
Data pipelines, quality contracts, versioning, lineage tracking, and feature stores – the data infrastructure layer that makes ML models reliable in production.
🔔 Notify me
7
Coming soon
MLOps Observability & Monitoring
Production monitoring for ML systems: model performance dashboards, drift detection, alerting strategies, SLOs for ML services, and incident response for model failures.
🔔 Notify me
8
Coming soon
MLOps Security & Compliance
Supply chain security for ML systems, model governance, access control, compliance frameworks, and SecOps practices applied to ML infrastructure and pipelines.
🔔 Notify me
🎓
Specialization goal
MLOps Specialization Certificate
Nubes Academy is developing university accreditation partnerships. The specialization is designed to qualify as a recognized credential – not just another online certificate.

Start with MLOps Fundamentals and decide your next step based on your career goals.

Start with Course #1 – Enroll Now

Coming soon

Be first when CI/CD for MLOps launches

Join the waitlist to secure subscriber pricing and get notified the moment the course goes live. Everything planned, confirmed from the curriculum draft:

  • ⚙️
    15 content modules — complete CI/CD pipeline stack From branching models and data contracts through to cost-aware pipelines and reusable templates.
  • 📋
    3 case studies — Churn, Time Series, LLM Inference End-to-end scenarios applying the pipeline to different ML deployment types including a generative AI workload.
  • 🏁
    Capstone project — golden pipeline template Apply the full pipeline to your own ML project and leave with a reusable template ready for production.
  • 🏆
    Certificate of Completion Step 5 of the MLOps Specialization credential path.
CI/CD for MLOps · Course #5
Price: coming soon
💡 Waitlist members receive subscriber pricing — a discount off the public launch price. No commitment required.
Join the Waitlist — Free
No commitment · Just first access and subscriber pricing
  • ✓ 15 content modules confirmed
  • ✓ 3 case studies (incl. LLM CI/CD)
  • ✓ Capstone + golden pipeline template
  • ✓ Certificate of Completion
  • ✓ Customer Churn running project
Machine Learning Model Lifecycle — Certificate of Completion

Course credential

Certificate of Completion

Complete the course and earn a Certificate of Completion in CI/CD for MLOps — shareable directly to your LinkedIn profile and CV. CI/CD automation for ML is one of the most in-demand skills in MLOps job descriptions, and a verifiable credential in this domain carries real weight in technical hiring.

This certificate is Step 5 of the MLOps Specialization. Nubes Academy is developing university accreditation partnerships for the full specialization — the credentials you build today are designed to grow into something recognized beyond a standard online course certificate.

🎓 Part of the MLOps Specialization path Complete all courses in the specialization to qualify for the full Specialization Certificate — designed toward university accreditation.
Alex Pototskyy - Founder, Nubes Academy

Your instructor

About Your Instructor

Alex is an Independent Consultant with 20+ years of experience across Software development, DevOps, Architecture, and technical leadership. He developed and delivered Cloud & DevOps curriculum at Master’s Degree level at a university, and continues to work as an active practitioner alongside building Nubes Academy.

What makes Alex’s approach different: his courses are built from data, not assumptions. The MLOps Fundamentals curriculum was designed by analysing what MLOps engineers actually do in production, not by following a generic textbook outline. The result is a course that covers what your future employer expects, explained in a way that makes sense if you already think like an engineer.

If you have a background in IT, and you want to understand where ML fits into the systems you already work with – this course was built for exactly where you are.

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Got questions?

Frequently asked questions

The course is currently in development. Join the waitlist to be notified as soon as it’s available — waitlist members receive subscriber pricing at launch. We don’t publish a date until the course passes our quality review. Subscribe to the newsletter and you’ll hear first.
Most of it. CI/CD for software assumes code is the only artifact that moves through a pipeline. ML adds three new artifact types — data, trained models, and model packages — each with their own validation requirements. The CI Basics module (Module 2) is the most familiar. From Module 3 onwards — Data CI, Training CI, Model Packaging CI — the material is specific to ML and won’t overlap with standard DevOps CI/CD knowledge.
GitHub Actions is the primary CI tool used in examples and the hands-on project, with GitLab CI referenced as an alternative. ArgoCD is mentioned in the context of deployment orchestration. The pipeline concepts and patterns are tool-agnostic — if you use a different CI tool at work, the module structure (stages, triggers, artifacts, promotion gates) transfers directly. MLflow is used for the model registry and packaging steps throughout.
These courses are designed to be sequential, not overlapping. Course #4 (ML Model Lifecycle) teaches what the model registry is, how promotion gates work conceptually, and how shadow/canary deployments apply to models. Course #5 (this course) teaches how to automate all of that inside a CI/CD pipeline — triggering the data checks, running the smoke training, publishing to the registry, and executing the deployment strategy, automatically, on every change. Course #4 is the “what and why”; Course #5 is the “how to automate it.”
Yes — as a dedicated case study (Module 16, Case Study 3). The LLM Inference case study applies the CI/CD framework to a generative AI workload specifically: building and scanning large model containers, testing cold-start budgets, and running canary releases using quality probes instead of traditional accuracy metrics. The course treats LLM CI/CD as a natural extension of the classical ML pipeline patterns taught in earlier modules, not a separate track.
The Reuse & Templates module (Module 14) and the Capstone produce a parameterized, reusable pipeline that covers the complete train → package → register → deploy sequence. It’s a GitHub Actions workflow (with GitLab CI equivalent) that can be applied to any new ML model in your organisation with minimal configuration. This is the primary take-home artifact from the course — something you can actually use in production, not just reference material.
Yes — you’ll receive a Certificate of Completion in CI/CD for MLOps shareable on LinkedIn and your CV. This is Step 5 of the MLOps Specialization. Nubes Academy is developing university accreditation partnerships — the full specialization certificate is designed to qualify as a recognized credential beyond a standard online course.
Yes. Team licenses for 5–20 engineers will be available, with a manager dashboard and kickoff session. This course is particularly effective for DevOps/platform teams upskilling together — when the whole team understands ML CI/CD patterns, the pipeline you build gets applied to every model consistently. Contact [email protected] to be included in early team pricing discussions.

Still have questions? Email us directly — we reply within 1–2 business days.

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