MLOps Roadmap 2026
The definitive path to operationalizing AI systems
A comprehensive MLOps roadmap for ML engineers, data scientists, and DevOps professionals. Master the 2026 operational stack: from Reproducible Pipelines and Model Registries to LLMOps, Observability, and LLM-as-a-Judge evaluation. Move beyond notebooks to build reliable, scalable, and production-grade AI infrastructure.
Quick answer
What is the right MLOps roadmap for 2026?
Start with the ML lifecycle and foundational engineering skills (Python, Linux, Git). Move into data versioning, experimentation tracking, and training pipelines. Then master containers, model serving, and workflow orchestration. Add CI/CD and testing for automation. Finally, focus on monitoring, observability, drift detection, and governance. Don't jump to Kubernetes or complex platforms before you can reliably serve a single model behind an API.
Sources and methodology · This roadmap is reviewed when production practices, tools or platform patterns materially change.
Starting knowledge
For ML engineers
Estimated path
10-16 weeks part-time
Roadmap outcome
Start with the ML lifecycle and foundational engineering skills (Python, Linux, Git)
Level
Intermediate
Structure
4 phases · 10 stages
Builds
4 project builds
Version
v2.0
Where should you start?
You likely already know
- Python
- ML lifecycle
- Experimentation
Review briefly
- Linux basics
- Git workflow
Recommended entry
03Data Versioning and ValidationCore Roadmap
The MLOps Roadmap
Follow one common roadmap first. Build the foundations for production ML, learn pipelines and serving the right way, and move toward reliable, observable, and governed AI infrastructure.
- Phase 01Foundation
- Phase 02Build
- Phase 03Ship
- Phase 04Operate
Roadmap overview
Ten stages with what to learn, what to build, the exit criterion for each, and an estimated time.
| Stage | What to learn | What to build | Exit criterion | Time |
|---|---|---|---|---|
| 01 ML Lifecycle and Foundations | Understand the full ML lifecycle and the operational mindset required for production AI systems. | A documented ML lifecycle map for a real project, showing data, training, evaluation, deployment, and monitoring stages. | Write a one-page ML lifecycle document for a real project that identifies all stages, handoffs, and operational risks. | 1 week |
| 02 Python, Linux, and Git | Build the foundational engineering skills required for MLOps workflows and infrastructure. | A Python automation script that processes data, trains a model, and logs results, all under Git version control. | Build a reproducible training script with Git-tracked code, pinned dependencies, and a single-command execution that produces identical results on two runs. | 1–2 weeks |
| 03 Data Versioning and Validation | Learn how to version data, validate quality, and ensure reproducible training inputs. | A data pipeline that versions datasets, runs schema validation, and rejects invalid data automatically. | Build a data pipeline that versions a dataset with DVC or similar, runs 5+ schema and quality checks, and automatically rejects inputs that fail validation. | 1–2 weeks |
| 04 Experimentation and Training Pipelines | Learn how to track experiments, manage model registries, and build reproducible training pipelines. | A training pipeline with experiment tracking, model registry integration, and automated hyperparameter logging. | Build a training pipeline that logs 5+ metrics and parameters to an experiment tracker, registers the model in a model registry, and can reproduce the same result from a registry entry. | 2 weeks |
| 05 Containers and Packaging | Learn how to package models and applications into reproducible, deployable containers. | A Docker container that packages a trained model and serves it behind a simple API. | Build a Docker image that packages a trained model with all dependencies, serves predictions via an API, and produces identical results locally and in a clean environment. | 1–2 weeks |
| 06 Model Serving Patterns | Learn how to serve models in production through batch, online, and API serving patterns. | A model serving API that handles both batch and online prediction requests with proper error handling. | Deploy a model behind a REST API that serves online predictions with sub-200ms latency and handles 100+ concurrent requests without errors. | 2 weeks |
| 07 Workflow Orchestration and Platforms | Learn how to orchestrate ML workflows and understand container orchestration and cloud ML platforms. | An orchestrated ML pipeline that runs data processing, training, and deployment stages automatically. | Build an orchestrated pipeline with 3+ stages (data, train, deploy) that runs automatically on a schedule and sends alerts on failure. | 1–2 weeks |
| 08 CI/CD and Testing | Learn how to automate testing and deployment for ML systems through CI/CD pipelines. | A CI/CD pipeline that runs tests, builds a model container, and deploys it automatically on code changes. | Build a CI/CD pipeline that runs 5+ tests, builds a Docker image, and deploys the model to a staging environment automatically on every Git push. | 1–2 weeks |
| 09 Monitoring, Observability, and Drift | Learn how to monitor ML systems in production, detect drift, and respond to incidents. | A monitoring dashboard that tracks model performance, data drift, and system health with alerting. | Deploy a monitoring system that tracks 5+ metrics (latency, prediction distribution, data drift, error rate, throughput) and triggers alerts on anomaly detection. | 2 weeks |
| 10 Governance, Retraining, and Capstone | Learn governance, access control, retraining strategies, and bring everything together in a capstone project. | A complete end-to-end MLOps capstone: data pipeline, training, model registry, serving, monitoring, and automated retraining. | Build and deploy a complete MLOps system with versioned data, automated training, model registry, containerized serving, monitoring with drift detection, and an automated retraining trigger. | 2–3 weeks |
Phase 01
Foundation
The ML lifecycle and the engineering primitives that make it operational.
ML Lifecycle and Foundations
Understand the full ML lifecycle and the operational mindset required for production AI systems.
Core concepts
ML Lifecycle Basics
Data, training, evaluation, deployment, monitoring, and retraining stages
Batch vs. Online ML
Batch prediction, real-time serving, streaming, and when to use each pattern
Production ML Mindset
Reproducibility, reliability, observability, and the shift from research to production
Why it matters
Without understanding the ML lifecycle, you'll optimize the wrong things. MLOps is about the entire lifecycle, not just deployment.
How this fits into the system
- ML Lifecycle Basics
- Batch vs. Online ML
- Production ML Mindset
Build 1
A documented ML lifecycle map for a real project, showing data, training, evaluation, deployment, and monitoring stages.
Input
Understand the full ML lifecycle and the operational mindset required for production AI systems.
System
Success condition
Write a one-page ML lifecycle document for a real project that identifies all stages, handoffs, and operational risks.
Common mistake
Treating MLOps as traditional DevOps without understanding data and model lifecycle differences.
Ready to continue?
You should now be able to:
- Write a one-page ML lifecycle document for a real project that identifies all stages, handoffs, and operational risks.
Reality check
Reality Check: MLOps is not 'DevOps plus Jupyter.' It is a different discipline with different failure modes. Data drift, model degradation, and reproducibility issues don't exist in traditional software. Treat them differently.
Python, Linux, and Git
Build the foundational engineering skills required for MLOps workflows and infrastructure.
How this fits into the system
- Python Automation
- Linux Basics
- Git Workflow
- Dependency Reproducibility
What to build
A Python automation script that processes data, trains a model, and logs results, all under Git version control.
Build 1
A Python automation script that processes data, trains a model, and logs results, all under Git version control.
Input
Build the foundational engineering skills required for MLOps workflows and infrastructure.
System
Success condition
Build a reproducible training script with Git-tracked code, pinned dependencies, and a single-command execution that produces identical results on two runs.
Core concepts
Python Automation
Scripting, CLI tools, configuration management, and task automation
Linux Basics
Command line, file systems, permissions, processes, and shell scripting
Git Workflow
Version control, branching, code review, and CI-triggering patterns
Dependency Reproducibility
Virtual environments, requirement pinning, and reproducible builds
Why it matters
MLOps depends on automation, scripting, and version control. Without these fundamentals, you cannot build reproducible pipelines.
Ready to continue?
You should now be able to:
- Build a reproducible training script with Git-tracked code, pinned dependencies, and a single-command execution that produces identical results on two runs.
Still unclear? Review: ML Lifecycle and Foundations →
Reality check
Reality Check: 'It works on my machine' is not MLOps. If your training is not reproducible—same code, same data, same dependencies, same result—you don't have a pipeline; you have a lottery.
Phase 02
Build
Make training reproducible: versioned data, tracked experiments, pipelined runs.
Data Versioning and Validation
Learn how to version data, validate quality, and ensure reproducible training inputs.
How this fits into the system
- Data Ingestion Patterns
- Data Versioning
- Schema and Quality Checks
- Feature Store Awareness
What to build
A data pipeline that versions datasets, runs schema validation, and rejects invalid data automatically.
Core concepts
Data Ingestion Patterns
Batch ingestion, streaming, data sources, and ETL patterns for ML
Data Versioning
DVC, dataset registries, and reproducible data tracking
Schema and Quality Checks
Schema validation, data profiling, anomaly detection, and quality gates
Feature Store Awareness
Feature stores, online vs. offline features, and feature reuse patterns
Debug this
Treating data as an afterthought and focusing only on model code.
What would you inspect first?
- [ Inputs ]
- [ Config ]
- [ Pipeline ]
- [ Environment ]
Reveal reasoning
Start by reproducing the failure with the smallest change. Most data versioning and validation failures come from a mismatch between how the component was built and how it runs in the wider system, not from the core logic itself.
Architecture decision
Git vs DVC for Data
| Option A | Option B | |
|---|---|---|
| Stores | code | large datasets |
| Diff | lines | checksums / partitions |
| Storage backend | git objects | object store (S3/GCS) |
| Best for | source + config | training datasets + artifacts |
Start with: Use Git for code/config and DVC (or similar) for datasets — never commit large data to Git.
Build 1
A data pipeline that versions datasets, runs schema validation, and rejects invalid data automatically.
Input
Learn how to version data, validate quality, and ensure reproducible training inputs.
System
Success condition
Build a data pipeline that versions a dataset with DVC or similar, runs 5+ schema and quality checks, and automatically rejects inputs that fail validation.
Ready to continue?
You should now be able to:
- Build a data pipeline that versions a dataset with DVC or similar, runs 5+ schema and quality checks, and automatically rejects inputs that fail validation.
Still unclear? Review: Python, Linux, and Git →
Reality check
Reality Check: Data is the #1 source of production ML failures. If you're not versioning and validating your data, you're debugging in the dark. Spend more time on data than on models.
Experimentation and Training Pipelines
Learn how to track experiments, manage model registries, and build reproducible training pipelines.
Core concepts
Experiment Tracking
MLflow, Weights & Biases, tracking metrics, parameters, artifacts, and model comparisons
Model Registry
Model versioning, staging, approval workflows, and registry management
Training Pipeline Design
Pipeline stages, modular training code, and reproducible execution
Hyperparameter Automation
Automated tuning, search strategies, and pipeline-integrated optimization
Why it matters
Without experiment tracking and reproducible pipelines, you cannot compare models reliably or reproduce production results.
How this fits into the system
- Experiment Tracking
- Model Registry
- Training Pipeline Design
- Hyperparameter Automation
Build 1
A training pipeline with experiment tracking, model registry integration, and automated hyperparameter logging.
Input
Learn how to track experiments, manage model registries, and build reproducible training pipelines.
System
Success condition
Build a training pipeline that logs 5+ metrics and parameters to an experiment tracker, registers the model in a model registry, and can reproduce the same result from a registry entry.
Tools you'll encounter
- Experiment Tracking
- MLflowWeights & Biases
- Workflow Orchestration
- DagsterAirflowPrefect
- Model Registry
- MLflow RegistryW&B Models
Learn the engineering concept first. Tools are implementation choices.
Common mistake
Using notebooks as training pipelines without experiment tracking or model registry integration.
Ready to continue?
You should now be able to:
- Build a training pipeline that logs 5+ metrics and parameters to an experiment tracker, registers the model in a model registry, and can reproduce the same result from a registry entry.
Still unclear? Review: Data Versioning and Validation →
Reality check
Reality Check: A notebook is not a pipeline. If your training process requires manual steps, clicking 'Run All,' or copying files, it's not reproducible. Pipelines are code, not notebooks.
Phase 03
Ship
Package, expose and orchestrate models reliably behind real interfaces.
Containers and Packaging
Learn how to package models and applications into reproducible, deployable containers.
How this fits into the system
- Docker Basics
- Artifact Management
- Runtime Configuration
What to build
A Docker container that packages a trained model and serves it behind a simple API.
Build 1
A Docker container that packages a trained model and serves it behind a simple API.
Input
Learn how to package models and applications into reproducible, deployable containers.
System
Success condition
Build a Docker image that packages a trained model with all dependencies, serves predictions via an API, and produces identical results locally and in a clean environment.
Core concepts
Docker Basics
Images, containers, Dockerfiles, layer caching, and multi-stage builds
Artifact Management
Model artifacts, registry storage, and packaging for deployment
Runtime Configuration
Environment variables, config files, and runtime parameter management
Tools you'll encounter
- Container Runtime
- DockerPodman
- Artifact Storage
- S3GCSArtifact Registry
Learn the engineering concept first. Tools are implementation choices.
Expected depth
- Docker
- OPERATE
- Artifact Management
- BUILD
- Kubernetes
- EXPLAIN
Why it matters
Containers are the foundation of reproducible deployment. Without proper packaging, your model works in dev but fails in production.
Ready to continue?
You should now be able to:
- Build a Docker image that packages a trained model with all dependencies, serves predictions via an API, and produces identical results locally and in a clean environment.
Still unclear? Review: Experimentation and Training Pipelines →
Reality check
Reality Check: A 5GB Docker image with 'everything installed' is not packaging; it's laziness. Learn to build lean, reproducible images. Your deployment speed and security depend on it.
Model Serving Patterns
Learn how to serve models in production through batch, online, and API serving patterns.
How this fits into the system
- Batch and Online Serving
- API Serving Patterns
- Release Strategies
- Performance Tuning
What to build
A model serving API that handles both batch and online prediction requests with proper error handling.
Core concepts
Batch and Online Serving
Batch prediction, real-time serving, and when to use each pattern
API Serving Patterns
REST APIs, gRPC, model servers (BentoML, Triton), and serving frameworks
Release Strategies
Canary, blue-green, shadow deployments, and gradual rollout patterns
Performance Tuning
Latency optimization, batching, caching, and resource management
Debug this
Using naive model.predict() in a web framework without handling concurrency, latency, or versioning.
What would you inspect first?
- [ Inputs ]
- [ Config ]
- [ Pipeline ]
- [ Environment ]
Reveal reasoning
Start by reproducing the failure with the smallest change. Most model serving patterns failures come from a mismatch between how the component was built and how it runs in the wider system, not from the core logic itself.
Architecture decision
Online vs Batch Inference
| Option A | Option B | |
|---|---|---|
| Latency | ms / sec | min / hour |
| Traffic | requests | scheduled |
| Infrastructure | always-on | jobs |
| Best for | live apps | bulk scoring |
| Operational cost | higher | lower |
Start with: Learn batch and basic API serving before adding complex inference infrastructure.
Build 1
A model serving API that handles both batch and online prediction requests with proper error handling.
Input
Learn how to serve models in production through batch, online, and API serving patterns.
System
Success condition
Deploy a model behind a REST API that serves online predictions with sub-200ms latency and handles 100+ concurrent requests without errors.
Tools you'll encounter
- Serving
- FastAPIBentoMLTriton
- Infrastructure
- DockerKubernetes
Learn the engineering concept first. Tools are implementation choices.
Expected depth
- FastAPI Serving
- BUILD
- Batch Inference
- BUILD
- Triton / BentoML
- AWARE
- Latency Debugging
- DEBUG
Ready to continue?
You should now be able to:
- Deploy a model behind a REST API that serves online predictions with sub-200ms latency and handles 100+ concurrent requests without errors.
Still unclear? Review: Containers and Packaging →
Reality check
Reality Check: Serving a model is not the same as calling model.predict(). You need to handle concurrency, latency, errors, versioning, and rollback. If you skip these, your 'served model' will crash on the first real traffic spike.
Workflow Orchestration and Platforms
Learn how to orchestrate ML workflows and understand container orchestration and cloud ML platforms.
Core concepts
Orchestration Basics
Airflow, Prefect, Dagster, DAGs, scheduling, and pipeline dependencies
Kubernetes Awareness
K8s concepts, deployments, services, and ML-specific patterns
Cloud ML Platform Awareness
AWS SageMaker, GCP Vertex AI, Azure ML, and managed ML platforms
Why it matters
Orchestration is what turns manual pipelines into automated, reliable systems. Without it, every pipeline run is a manual operation.
How this fits into the system
- Orchestration Basics
- Kubernetes Awareness
- Cloud ML Platform Awareness
Build 1
An orchestrated ML pipeline that runs data processing, training, and deployment stages automatically.
Input
Learn how to orchestrate ML workflows and understand container orchestration and cloud ML platforms.
System
Success condition
Build an orchestrated pipeline with 3+ stages (data, train, deploy) that runs automatically on a schedule and sends alerts on failure.
Common mistake
Starting with Kubernetes before mastering basic workflow orchestration.
Ready to continue?
You should now be able to:
- Build an orchestrated pipeline with 3+ stages (data, train, deploy) that runs automatically on a schedule and sends alerts on failure.
Still unclear? Review: Model Serving Patterns →
Reality check
Reality Check: Don't start with Kubernetes. Start with a simple orchestrator like Airflow or Prefect. You don't need K8s until you're serving multiple models at scale. Premature orchestration complexity kills projects.
CI/CD and Testing
Learn how to automate testing and deployment for ML systems through CI/CD pipelines.
How this fits into the system
- Test Strategy for ML
- CI/CD Foundations
- Infrastructure as Code
What to build
A CI/CD pipeline that runs tests, builds a model container, and deploys it automatically on code changes.
Build 1
A CI/CD pipeline that runs tests, builds a model container, and deploys it automatically on code changes.
Input
Learn how to automate testing and deployment for ML systems through CI/CD pipelines.
System
Success condition
Build a CI/CD pipeline that runs 5+ tests, builds a Docker image, and deploys the model to a staging environment automatically on every Git push.
Core concepts
Test Strategy for ML
Data tests, model tests, integration tests, and behavioral testing for ML
CI/CD Foundations
GitHub Actions, GitLab CI, pipeline stages, and automated deployment
Infrastructure as Code
Terraform, CloudFormation, and reproducible infrastructure provisioning
Why it matters
Without CI/CD, every deployment is manual and error-prone. Without testing, you ship broken models to production.
Ready to continue?
You should now be able to:
- Build a CI/CD pipeline that runs 5+ tests, builds a Docker image, and deploys the model to a staging environment automatically on every Git push.
Still unclear? Review: Workflow Orchestration and Platforms →
Reality check
Reality Check: ML testing is not just unit tests. You need data tests, model tests, integration tests, and deployment tests. If you only test code and not model behavior, you'll ship models that pass CI but fail in production.
Phase 04
Operate
Observe, govern, detect drift and retrain — the production loop.
Monitoring, Observability, and Drift
Learn how to monitor ML systems in production, detect drift, and respond to incidents.
How this fits into the system
- Service Observability
- Model Monitoring
- Incident Response
- Business Metric Alignment
What to build
A monitoring dashboard that tracks model performance, data drift, and system health with alerting.
Core concepts
Service Observability
Logs, metrics, tracing, dashboards, and system health monitoring (Prometheus, Grafana, OpenTelemetry)
Model Monitoring
Prediction drift, data drift, performance decay, and model quality tracking
Incident Response
Alerting, on-call, rollback, and incident management for ML systems
Business Metric Alignment
Connecting model metrics to business KPIs and impact measurement
Debug this
Deploying models without monitoring, drift detection, or alerting.
What would you inspect first?
- [ Inputs ]
- [ Config ]
- [ Pipeline ]
- [ Environment ]
Reveal reasoning
Start by reproducing the failure with the smallest change. Most monitoring, observability, and drift failures come from a mismatch between how the component was built and how it runs in the wider system, not from the core logic itself.
Build 1
A monitoring dashboard that tracks model performance, data drift, and system health with alerting.
Input
Learn how to monitor ML systems in production, detect drift, and respond to incidents.
System
Success condition
Deploy a monitoring system that tracks 5+ metrics (latency, prediction distribution, data drift, error rate, throughput) and triggers alerts on anomaly detection.
Tools you'll encounter
- Metrics & Logs
- PrometheusGrafanaOpenTelemetry
- Drift Detection
- EvidentlyArizeWhyLabs
Learn the engineering concept first. Tools are implementation choices.
Expected depth
- Metrics & Logs
- OPERATE
- Drift Detection
- DEBUG
- Evidently
- BUILD
Ready to continue?
You should now be able to:
- Deploy a monitoring system that tracks 5+ metrics (latency, prediction distribution, data drift, error rate, throughput) and triggers alerts on anomaly detection.
Still unclear? Review: CI/CD and Testing →
Reality check
Reality Check: Unmonitored models drift silently. By the time users complain, the damage is done. If you don't have drift detection, you're not running MLOps; you're running hope-driven operations.
Governance, Retraining, and Capstone
Learn governance, access control, retraining strategies, and bring everything together in a capstone project.
Core concepts
Retraining Strategy
When to retrain, automated retraining triggers, and model lifecycle management
Governance and Access
Access control, audit trails, model approval workflows, and compliance
Documentation and Runbooks
System documentation, operational runbooks, and on-call procedures
Capstone System
End-to-end MLOps project integrating all stages into a production system
Why it matters
Governance and retraining are what make ML systems sustainable long-term. Without them, your systems become unmaintainable and non-compliant.
How this fits into the system
- Retraining Strategy
- Governance and Access
- Documentation and Runbooks
- Capstone System
Build 1
A complete end-to-end MLOps capstone: data pipeline, training, model registry, serving, monitoring, and automated retraining.
Input
Learn governance, access control, retraining strategies, and bring everything together in a capstone project.
System
Success condition
Build and deploy a complete MLOps system with versioned data, automated training, model registry, containerized serving, monitoring with drift detection, and an automated retraining trigger.
Common mistake
Building a capstone without monitoring, governance, or retraining components.
Ready to continue?
You should now be able to:
- Build and deploy a complete MLOps system with versioned data, automated training, model registry, containerized serving, monitoring with drift detection, and an automated retraining trigger.
Still unclear? Review: Monitoring, Observability, and Drift →
Reality check
Reality Check: A capstone is not a demo. It's a system. If your capstone doesn't include monitoring, governance, and retraining, you've built a science project, not an MLOps system.
Build progression
- 01
REPRODUCE
Reproducible Training Pipeline
Version data + code + config into a reproducible run.
- 02
PACKAGE
Containerized Model API
Wrap the model artifact in a container behind a FastAPI endpoint.
- 03
AUTOMATE
Registry + CI/CD
Promote artifacts through a registry with automated tests.
- 04
OPERATE
Monitoring + Drift + Retraining
Observe, detect drift and trigger retraining.
- 05Capstone
CAPSTONE
Production MLOps System
The full loop: data → train → register → serve → monitor → retrain.
Production MLOps system
Every node links back to its roadmap stage so you can jump straight to the relevant learning.
Read left to right: each node links back to its roadmap stage for the relevant learning.
How this roadmap changes by background
The full roadmap stays the same — your focus shifts based on what you already know.
Data Scientist
Already has: Python · ML
Focus more on:
Versioning → Pipelines → Serving → Monitoring
- • Data Versioning and Validation
- • Experimentation and Training Pipelines
- • Model Serving Patterns
- • Monitoring, Observability, and Drift
DevOps / SRE
Already has: Containers · Kubernetes
Focus more on:
ML Lifecycle → Artifacts → Serving → Model Monitoring
- • ML Lifecycle and Foundations
- • Containers and Packaging
- • Model Serving Patterns
- • Monitoring, Observability, and Drift
Backend Engineer
Already has: APIs · Systems
Focus more on:
ML Lifecycle → Training → Model Serving → ML Observability
- • ML Lifecycle and Foundations
- • Experimentation and Training Pipelines
- • Model Serving Patterns
- • Monitoring, Observability, and Drift
From notebook to production system
The roadmap rewrites a manual, unreproducible workflow into an automated, observable, governed system.
Where you start
- Notebook
- model.pkl
- manual prediction
Where you end
- Versioned Data
- Training Pipeline
- Experiment Tracking
- Model Registry
- CI/CD
- Serving
- Monitoring
- Retraining
Roadmap review
- Version
- v2.0
- Last reviewed
- July 13, 2026
- Reviewed by
- SCAI Technical Training Team
- Scope
- The definitive path to operationalizing AI systems
- Does not primarily cover
- LLMOps / AgentOps (covered in their own roadmaps)
- Update policy
- Reviewed when production practices, tools or platform patterns materially change.
Changelog
- July 13, 2026Refreshed stage content, added system map and role paths.
Continue With Structured Learning
Turn This MLOps Roadmap Into a Reviewed Production Portfolio
The MLOps Course is the closest structured match for this roadmap. It adds live implementation, instructor code reviews, production projects and architecture discussions on top of the same progression.
- Build the core project from this roadmap with instructor review
- Debug production failure modes hands-on with guided feedback
- Produce a reviewed portfolio artifact by the end of the track
Fees, schedules and enrolment details live on the course page. No placement, salary or outcome is guaranteed.
Build Along the Way
What you can build on this MLOps roadmap
Use the roadmap as a practical build path. Every major stage should produce something useful and visible.
- Build 01
Reproducible Training Pipeline
Build a training pipeline with experiment tracking, data versioning, and reproducible execution.
- Build 02
Containerized Model API
Package a trained model into a Docker container and serve it behind a REST API with proper error handling.
- Build 03
Automated CI/CD Deployment
Build a CI/CD pipeline that runs tests, builds containers, and deploys models automatically on code changes.
- Build 04
Full MLOps Capstone System
Ship a complete MLOps system with monitoring, drift detection, governance, and automated retraining.
Next Step
Pick your path and start building
Now choose how you want to apply your MLOps skills and move into a structured learning path.
Start with MLOps Course
Master production ML pipelines, model serving, monitoring, and AI infrastructure through a structured program.
Start MLOps Course →ML PathDeepen ML engineering foundations
Go deeper into ML algorithms, model architecture, and ML engineering before focusing on operations and infrastructure.
Explore ML Engineer Roadmap →AIOps FocusFocus on AI operations
Learn how AI systems run in production through LLMOps, observability, monitoring, and reliability practices for AI applications.
Explore AIOps Path →Start with MLOps Course for production ML systems. Move to ML Engineer Roadmap for deeper ML foundations or AIOps for AI operations.
Related learning
- Continue to the ML Engineer roadmapFor the model-development foundation MLOps operationalizes.
- Continue to the AIOps roadmapTo broaden into production AI operations across LLMs and agents.
- Continue to the LLMOps roadmapTo specialize in LLM application operations.
- Compare MLOps and LLMOps and AIOpsWhere each operations track starts and ends.
- Compare MLOps and DevOpsWhat changes when data and models enter the pipeline.
- Compare the MLOps and AIOps coursesCourse-level decision between ML operations and broad AI operations.
FAQ
MLOps Roadmap — Frequently Asked Questions
Clear answers to the most common questions engineers ask before moving into MLOps.
Who is this MLOps roadmap for?
This roadmap is designed for ML engineers, data scientists, backend engineers, and DevOps professionals who want a practical path into production ML operations and AI infrastructure.
Do I need ML experience to learn MLOps?
You need a practical understanding of ML basics, not deep ML expertise. This roadmap focuses on the operational side—pipelines, serving, monitoring, and governance—rather than model theory.
What should I learn first in MLOps?
Start with the ML lifecycle, then Python, Linux, and Git. Move into data versioning, experimentation tracking, training pipelines, containers, serving, orchestration, CI/CD, and finally monitoring and governance.
Do I need Kubernetes for MLOps?
No, not initially. Start with simple orchestration tools like Airflow or Prefect. You only need Kubernetes when you're serving multiple models at scale. Premature K8s complexity kills projects.
MLOps Engineer vs Data Scientist: Who earns more in 2026?
In 2026, MLOps engineers often command a premium because they bridge the gap between model creation and production value. While Data Scientists build models, MLOps engineers ensure those models actually generate revenue by running reliably in production. Both roles are valuable, but MLOps is the rarer skill.
What is the difference between MLOps and DevOps?
MLOps extends DevOps with ML-specific challenges: data versioning, model drift, reproducibility, experiment tracking, and model lifecycle management. MLOps is not just 'DevOps plus Jupyter.'
What is LLMOps and how does it relate to MLOps?
LLMOps is MLOps specialized for large language models. It adds LLM-specific concerns like prompt versioning, LLM-as-a-Judge evaluation, token cost monitoring, and model provider management.
How long does it take to follow this MLOps roadmap?
A realistic part-time estimate is 10 to 16 weeks if you learn in the right order and build projects consistently.
What kind of projects should I build while following this roadmap?
Start with a reproducible training pipeline, then build a containerized model API, an automated CI/CD deployment, and finally a full MLOps capstone with monitoring, drift detection, and governance.
Should I learn MLOps before or after ML engineering?
It depends on your background. If you're a DevOps or backend engineer, learn MLOps alongside ML basics. If you're a data scientist, learn ML engineering first, then MLOps to operationalize your models.
What is the most important skill in MLOps?
Monitoring and drift detection. Models degrade silently in production, and without monitoring you'll discover failures from user complaints. Monitoring is what separates MLOps from ad-hoc deployment.
Is MLOps a good career move in 2026?
Absolutely. As every company moves from 'AI experiments' to 'AI in production,' the demand for engineers who can deploy, monitor, and scale models has exploded. MLOps is one of the highest-paying and most stable roles in the modern AI stack.