Introduction
MLOps Certified Professional (MLOCP) is a hands-on certification for professionals who want to learn how to build, deploy, monitor, and manage machine learning systems in production. It is hosted on DevOpsSchool and is designed to connect machine learning with DevOps, automation, and reliability practices. This certification is especially useful for DevOps engineers, cloud engineers, data engineers, SREs, and ML professionals who want to move beyond model building and into real-world ML operations. In practical terms, it helps you understand the full lifecycle of ML systems, including deployment, versioning, monitoring, governance, and scaling.
What it is:
MLOps Certified Professional (MLOCP) is a practical certification for professionals who want to learn how to combine machine learning, automation, CI/CD, observability, and governance into one operational workflow. It helps bridge the gap between data science and production engineering.
Who should take it:
This certification is a good fit for DevOps engineers, data engineers, cloud engineers, SREs, ML engineers, platform engineers, and technical managers who want to support ML systems in production. It is also useful for learners who are building a career in AI operations, data operations, or ML platform engineering.
Certification Overview
The MLOps Certified Professional (MLOCP) program is delivered via MLOps Certified Professional (MLOCP) and hosted on DevOpsSchool. In practical terms, that means the learning path is organized around real-world MLOps practices rather than only theory, with a focus on implementation, deployment, and operational excellence.
The certification typically progresses from foundational concepts to applied workflow design, automation, deployment, monitoring, and lifecycle management. Assessment is usually structured around conceptual understanding and practical readiness, so the learner is evaluated on whether they can apply MLOps principles in production-like scenarios.
Skills you'll gain
Designing MLOps workflows for model training, deployment, and monitoring.
Building CI/CD pipelines for machine learning projects.
Managing model versioning, reproducibility, and rollback strategies.
Setting up observability for ML systems, including drift and performance monitoring.
Working with containers, orchestration, and cloud-native ML deployment patterns.
Improving collaboration between data science, DevOps, and platform teams.
Applying governance, automation, and lifecycle management in production ML.
Real-world projects you should be able to do after it
Deploy a machine learning model into a production environment with automated release flow.
Build a pipeline that retrains and redeploys models based on new data.
Monitor model performance, latency, and data drift in production.
Create reproducible ML environments using containers and infrastructure automation.
Set up alerting for model degradation and operational failures.
Design an end-to-end MLOps workflow for a business use case.
Common mistakes
Treating MLOps as only model deployment, instead of full lifecycle management.
Ignoring data quality, versioning, and experiment tracking.
Skipping monitoring after deployment.
Using manual release steps instead of automation.
Focusing only on tools and not on process design.
Not aligning ML workflows with DevOps and security practices.
Best next certification after this
The best next certification depends on your career direction. If you want to go deeper in ML lifecycle delivery, choose an advanced MLOps or AI platform certification. If you want to broaden into infrastructure, choose SRE or Kubernetes-focused certification. If you want governance and business alignment, FinOps or cloud architecture certification can be a strong next step.
Certification Table
Track | Level | Who it’s for | Prerequisites | Skills Covered | Recommended Order | |
|---|---|---|---|---|---|---|
MLOps Certified Professional (MLOCP) | Intermediate | DevOps, ML, cloud, and platform professionals | Basic cloud, Linux, Git, containers, and ML awareness | MLOps lifecycle, CI/CD, deployment, monitoring, automation | 1 | |
DevOps for ML | Foundation to Intermediate | DevOps engineers entering AI/ML | CI/CD and container basics | Pipeline automation, release engineering, model deployment | 2 | |
ML Platform Engineering | Intermediate | Platform and cloud engineers | Kubernetes and cloud fundamentals | Scalable ML platforms, orchestration, governance | 3 | |
AI Operations | Intermediate | Ops, SRE, and automation teams | Monitoring and automation concepts | Model observability, incident response, drift handling | 4 | |
DataOps for ML | Intermediate | Data engineers and analytics teams | Data pipeline knowledge | Data quality, lineage, reproducibility | 5 | |
MLOps Governance | Advanced | Managers, architects, and compliance teams | Experience in operations or governance | Risk, policy, auditability, controls | 6 |
Choose your path
1. DevOps path
Start with MLOCP if you already know CI/CD, containers, and cloud delivery. Then move to Kubernetes and platform automation so you can support ML workloads end to end.
2. DevSecOps path
Use MLOCP to learn production ML delivery, then add security, secrets management, policy-as-code, and supply chain protection. This path is ideal if your focus is secure deployment and compliance.
3. SRE path
Take MLOCP to understand model reliability, monitoring, and incident handling. Then go deeper into observability, SLIs/SLOs, error budgets, and production resilience.
4. AIOps/MLOps path
This is the most direct path for automation around ML systems. Learn MLOCP first, then expand into AI-driven operations, model lifecycle tooling, and automated remediation.
5. DataOps path
Begin with MLOCP to connect data pipelines with ML deployment. Then build expertise in data quality, lineage, orchestration, and reproducible analytics workflows.
6. FinOps path
Take MLOCP to understand ML infrastructure needs, then add cost optimization, workload sizing, and cloud financial governance. This is useful when ML workloads need cost control at scale.
Role → Recommended certifications
Role | Recommended certifications |
|---|---|
DevOps Engineer | MLOCP, Kubernetes certification, DevSecOps certification |
SRE | MLOCP, Observability certification, SRE certification |
Platform Engineer | MLOCP, Kubernetes certification, Cloud engineering certification |
Cloud Engineer | MLOCP, AWS/Azure/GCP architecture certification, Terraform certification |
Security Engineer | MLOCP, DevSecOps certification, Cloud security certification |
Data Engineer | MLOCP, DataOps certification, cloud data engineering certification |
FinOps Practitioner | MLOCP, FinOps certification, cloud cost optimization certification |
Engineering Manager | MLOCP, cloud strategy certification, leadership or architecture certification |
Training institutions
If you want help with training and certification prep for MLOps Certified Professional (MLOCP), several focused learning brands are commonly associated with this ecosystem. DevOpsSchool is the core provider, while Cotocus, Scmgalaxy, BestDevOps, Devsecopsschool, Sreschool, Aiopsschool, Dataopsschool, and Finopsschool are positioned around related learning tracks and role-based specialization.
These institutions are useful because they map MLOps to adjacent domains like DevOps, SRE, DevSecOps, AIOps, DataOps, and FinOps. For learners who want practical career alignment, this can help you choose a training path based on your current role and next target role. The best option depends on whether you want broader platform knowledge, deeper operational focus, or more specialized certification preparation.
Next certifications to take
Same track: Advanced MLOps, ML platform engineering, or cloud-native ML deployment certification.
Cross-track: Kubernetes, SRE, or DevSecOps certification.
Leadership: Cloud architecture, platform strategy, or engineering management certification.
FAQs
What is MLOps Certified Professional (MLOCP)?
MLOCP is a certification focused on operationalizing machine learning in production using automation, monitoring, and lifecycle management.Who should pursue this certification?
DevOps professionals, data engineers, cloud engineers, SREs, ML engineers, and platform teams should consider it.Is this certification beginner-friendly?
It is better suited to learners with basic cloud, Linux, Git, and container knowledge, though motivated beginners can prepare with the right foundation.What skills does MLOCP build?
It builds skills in CI/CD, deployment automation, model monitoring, reproducibility, and MLOps governance.Does MLOCP help with real projects?
Yes, it prepares you to build and manage production ML pipelines, monitoring systems, and deployment workflows.How is MLOps different from DevOps?
DevOps focuses broadly on software delivery, while MLOps applies similar automation and reliability principles to machine learning systems.What tools are usually involved?
Common tools include container platforms, CI/CD tools, orchestration systems, model tracking tools, and monitoring stacks.What career roles can benefit from it?
Roles like DevOps engineer, ML engineer, SRE, platform engineer, and cloud engineer can benefit directly.What should I learn after MLOCP?
You can move into Kubernetes, SRE, DevSecOps, DataOps, or advanced MLOps depending on your career path.Is MLOCP useful for managers?
Yes, especially for managers who want to understand ML delivery, team workflows, governance, and platform planning.
Why choose DevOpsSchool?
DevOpsSchool is a strong choice for MLOps training because it connects certification learning with practical industry-oriented delivery. For learners who already work in DevOps, cloud, or technical content creation, it offers a structured path that aligns well with modern production engineering, certification readiness, and job-focused skill building.
Conclusion
MLOps Certified Professional (MLOCP) is a valuable certification for anyone who wants to work at the intersection of machine learning, automation, and operations. If your goal is to build production-ready AI/ML systems and grow into cloud-native or platform-oriented roles, this certification can be a strong step forward.