Introduction
MLOps is becoming an important skill for software engineers, DevOps professionals, ML engineers, data engineers, and technical managers.Building a machine learning model is only the beginning. Companies also need to deploy models, automate testing, manage versions, monitor performance, handle infrastructure, and retrain models when required.The MLOps Certified Professional (MLOCP) certification by DevOpsSchool is designed to help professionals understand these practical MLOps concepts.
MLOCP at a Glance
Track: MLOps / AIOps / DevOps
Level: Professional
Who it’s for: Software Engineers, DevOps Engineers, ML Engineers, SREs, Data Engineers, Managers
Prerequisites: Basic Linux, Git, Python, cloud, and DevOps knowledge
Skills covered: CI/CD, Docker, Kubernetes, MLflow, Terraform, cloud, model deployment, monitoring, governance
Recommended order: Linux → Git → Python → Docker → CI/CD → Kubernetes → MLflow → Deployment → Monitoring
What Is MLOps Certified Professional?
MLOCP is a professional certification focused on managing the complete machine learning lifecycle.It teaches how to move machine learning models from development and experimentation into reliable production environments.
Who Should Take It?
MLOCP can be useful for:
Software Engineers
Machine Learning Engineers
DevOps Engineers
Cloud Engineers
SRE Professionals
Data Engineers
Platform Engineers
Technical Leads
Engineering Managers
Professionals moving into MLOps or AIOps
Skills You’ll Gain
After learning MLOps concepts, you should understand:
Machine learning lifecycle management
Git and version control
CI/CD for ML projects
Docker containers
Kubernetes
Cloud platforms
Infrastructure as Code
Terraform
MLflow
Model versioning
Experiment tracking
Model deployment
Automated testing
Monitoring and observability
Model drift
Security and governance
Real-World Projects You Should Be Able to Build
After completing your MLOps learning, you should aim to create:
Automated ML training pipelines
CI/CD pipelines for ML applications
Docker-based ML services
Kubernetes model deployments
MLflow experiment tracking systems
Model registry workflows
Automated model testing
Model monitoring dashboards
Drift detection systems
Automated deployment and rollback workflows
These practical projects can also strengthen your portfolio.
Preparation Plan
7–14 Days
Best for experienced professionals.
Focus on:
Linux
Git
Python
Docker
Kubernetes basics
CI/CD
MLflow
Model deployment
Build one small end-to-end project.
30 Days
Suitable for working professionals.
Week 1: Linux, Git, Python
Week 2: Docker, cloud, CI/CD
Week 3: Kubernetes and Terraform
Week 4: MLflow, deployment and monitoring
60 Days
Best for beginners or career switchers.
Start with Linux, Python and Git. Then learn Docker, cloud, CI/CD, Kubernetes and Terraform.
Finally, focus on MLflow, model deployment, monitoring, testing, model governance and practical projects.
Common Mistakes
Avoid these common mistakes:
Learning tools without understanding MLOps concepts
Ignoring Linux and Git basics
Working only with Jupyter notebooks
Ignoring model monitoring
Ignoring data quality
Using only manual deployment processes
Memorising tools without building projects
Trying to master every technology at once
Focus on understanding the complete workflow.
Best Next Certification After MLOCP
Your next certification should depend on your career goal.
AIOps: For AI-driven IT operations
SRE: For reliability and production operations
DevSecOps: For security-focused engineering
DataOps: For data pipelines and data platforms
FinOps: For cloud cost optimisation
Cloud/Kubernetes: For infrastructure-focused roles
Choose Your Path
DevOps
Linux → Git → CI/CD → Docker → Kubernetes → Terraform → MLOps
Best for software engineers and DevOps professionals.
DevSecOps
DevOps → Security testing → Container security → Kubernetes security → MLOps
Best for security and DevOps professionals.
SRE
Linux → Cloud → Kubernetes → Monitoring → SRE → MLOps
Best for production and reliability engineers.
AIOps/MLOps
Python → Machine Learning → Docker → CI/CD → Kubernetes → MLflow → MLOps → AIOps
Best for ML engineers and AI platform professionals.
DataOps
Python → SQL → Data pipelines → Data quality → Automation → MLOps
Best for data engineers.
FinOps
Cloud → Cost monitoring → Resource optimisation → FinOps → MLOps infrastructure optimisation
Best for cloud architects, managers, and platform teams.
Training and Certification Support Institutions
DevOpsSchool
DevOpsSchool is the official provider of the MLOps Certified Professional certification. It focuses on practical learning, tools, projects, and MLOps concepts.
Cotocus
Cotocus can support professionals looking for practical technology training related to DevOps, cloud, automation, and MLOps.
Scmgalaxy
Scmgalaxy provides learning resources around software configuration management, DevOps, cloud, and related technologies.
BestDevOps
BestDevOps can help learners strengthen DevOps fundamentals before moving toward advanced MLOps concepts.
devsecopsschool
Suitable for professionals interested in combining security, DevOps, and modern software delivery practices.
sreschool
Useful for learners interested in reliability engineering, monitoring, automation, and production operations.
aiopsschool
Relevant for professionals interested in AIOps, automation, observability, and AI-driven operations.
dataopsschool
Suitable for learners working with data pipelines, DataOps, data quality, and automation.
finopsschool
Useful for professionals interested in cloud cost management, resource optimisation, and FinOps practices.
Conclusion
The MLOps Certified Professional (MLOCP) certification can help engineers and managers understand how machine learning models are developed, deployed, monitored, and maintained in real production environments.It is especially useful for professionals working in software engineering, DevOps, cloud, machine learning, SRE, and data engineering.The best way to prepare is to combine certification learning with practical projects. Learn Linux, Git, Docker, CI/CD, Kubernetes, MLflow, monitoring, and model deployment step by step.