MLOps is becoming an important skill for software engineers, DevOps professionals, cloud engineers, data engineers, ML engineers, and technical managers.The MLOps Certified Professional (MLOCP) certification by DevOpsSchool is designed to help professionals understand how machine learning models are developed, deployed, monitored, and managed in real production environments.Instead of focusing only on model building, MLOps connects machine learning with DevOps, cloud, CI/CD, containers, infrastructure, security, and monitoring.
MLOCP Certification Overview
- Certification: MLOps Certified Professional (MLOCP)
- Track: AIOps / MLOps
- Level: Professional
- Provider: DevOpsSchool
- Who it’s for: Software Engineers, DevOps Engineers, ML Engineers, Data Engineers, Cloud Engineers, SREs, Architects, and Managers
- Prerequisites: Basic Linux, Git, Python, cloud, and machine learning knowledge can be helpful
- Skills covered: MLOps lifecycle, CI/CD, Docker, Kubernetes, cloud, model deployment, monitoring, security, and automation
- Recommended order: Linux → Git → Python → Docker → Cloud → Kubernetes → CI/CD → MLOps
- Official Link: MLOps Certified Professional (MLOCP)
- Provider: DevOpsSchool
What Is MLOps Certified Professional?
MLOps Certified Professional is a practical certification focused on managing the complete machine learning lifecycle.
It helps professionals learn how to move machine learning models from development environments into stable, secure, scalable, and monitored production systems.
Who Should Take It?
MLOCP can be useful for:
- Software Engineers
- Machine Learning Engineers
- DevOps Engineers
- Data Engineers
- Cloud Engineers
- Site Reliability Engineers
- Platform Engineers
- Technical Leads
- Architects
- Engineering Managers
It is especially useful for professionals who want to combine software engineering, cloud, automation, and machine learning skills.
Skills You’ll Gain
After completing MLOCP training, learners should understand:
- Machine learning lifecycle management
- Model deployment
- Docker and containers
- Kubernetes
- CI/CD pipelines
- Cloud infrastructure
- Infrastructure as Code
- Git and version control
- Model monitoring
- Experiment tracking
- Model versioning
- Security practices
- Observability
- Automated retraining
- Production troubleshooting
Real-World Projects You Should Be Able to Do
After learning MLOps, you should be able to work on projects such as:
- Deploying an ML model as an API
- Containerizing ML applications with Docker
- Deploying ML workloads on Kubernetes
- Creating CI/CD pipelines for ML applications
- Tracking experiments and model versions
- Monitoring model performance
- Automating model deployment
- Creating retraining pipelines
- Managing cloud infrastructure using automation
- Building complete production-ready ML workflows
MLOCP Preparation Plan
7–14 Days
Best for experienced professionals.
Focus on Linux, Git, Python, Docker, Kubernetes, CI/CD, model deployment, monitoring, and MLOps concepts.
30 Days
Best for working engineers.
Spend the first week on Linux, Git, and Python. Use the second week for Docker, cloud, and Kubernetes. Learn CI/CD and model lifecycle management in the third week. Use the final week for monitoring, security, and a practical project.
60 Days
Best for beginners or professionals moving from traditional software roles.
Build your foundation first, then gradually learn containers, Kubernetes, cloud, CI/CD, model deployment, infrastructure automation, monitoring, and complete MLOps pipelines.
Common Mistakes
Avoid these common mistakes while preparing:
- Learning tools without understanding MLOps concepts
- Focusing only on machine learning models
- Ignoring Linux and Git fundamentals
- Avoiding hands-on projects
- Trying to learn every cloud platform at once
- Ignoring monitoring and observability
- Memorizing commands instead of understanding workflows
- Ignoring data and model versioning
Best Next Certification
After MLOCP, professionals can continue learning based on their career goals.
Possible next areas include:
- AIOps
- DevOps
- DevSecOps
- SRE
- DataOps
- FinOps
AIOps can be a natural next step for professionals who want to explore intelligent automation and AI-powered IT operations.
Choose Your Path
DevOps
Choose DevOps if you want to strengthen CI/CD, automation, infrastructure, containers, and cloud skills.
DevSecOps
Choose DevSecOps if you want to focus on security automation, application security, container security, and secure ML systems.
SRE
Choose SRE if you are interested in system reliability, monitoring, incident management, performance, and availability.
AIOps/MLOps
Choose this path if your main goal is machine learning operations, AI platforms, automation, and production ML systems.
DataOps
Choose DataOps if you work with data pipelines, data quality, data engineering, and analytics platforms.
FinOps
Choose FinOps if you are responsible for cloud costs, resource optimization, budgeting, and infrastructure efficiency.
Institutions Supporting MLOps Training and Certification
Several technology training organizations can support professionals in building related skills.
DevOpsSchool provides the MLOps Certified Professional certification discussed in this guide and focuses on practical DevOps, cloud, automation, and MLOps learning.
Cotocus, Scmgalaxy, and BestDevOps can also be explored for technical learning and professional development related to DevOps, cloud, automation, and modern engineering practices.
devsecopsschool and sreschool are useful for professionals interested in security engineering and site reliability concepts that complement MLOps.
aiopsschool, dataopsschool, and finopsschool can help learners explore specialized areas such as AIOps, DataOps, and FinOps.
Before selecting any program, learners should compare the curriculum, practical labs, trainer expertise, project work, and certification objectives.
Conclusion
The MLOps Certified Professional (MLOCP) certification can help software engineers and technology professionals understand how machine learning systems are deployed and operated in real-world environments.
MLOps is not only about machine learning. It combines software engineering, DevOps, cloud, containers, automation, security, infrastructure, and monitoring.Professionals who develop strong MLOps skills can contribute to building reliable, scalable, and production-ready AI and machine learning platforms.