Machine Learning Operations Concepts and Best Practices

 

Introduction

MLOps is becoming an important skill for software engineers, DevOps professionals, cloud engineers, ML engineers, and technical managers.The MLOps Certified Professional (MLOCP) certification from DevOpsSchool focuses on how machine learning models are built, deployed, monitored, automated, and managed in production environments.

What Is MLOCP?

MLOCP is a professional certification designed to help learners understand the complete MLOps lifecycle.It connects machine learning with DevOps, cloud, automation, containers, CI/CD, monitoring, testing, and infrastructure management.

Who Should Take It?

MLOCP is suitable for:

• Software Engineers
• DevOps Engineers
• Machine Learning Engineers
• Data Engineers
• Cloud Engineers
• SRE Professionals
• Platform Engineers
• Technical Leads
• Engineering Managers

Prerequisites

Basic knowledge of the following is useful:

• Linux
• Python
• Git
• Cloud computing
• DevOps concepts
• Basic machine learning

You do not need to be an expert in every area before starting.

Skills You’ll Gain

During MLOCP preparation, learners can develop skills in:

• MLOps lifecycle management
• Git and CI/CD
• Docker
• Kubernetes
• Terraform
• Cloud platforms
• MLflow
• Model training and testing
• Model deployment
• Model monitoring
• Observability
• Security and governance
• Production troubleshooting

Real-World Projects You Should Be Able to Do

After completing MLOCP training, you should aim to build projects such as:

• Deploying an ML model using Docker
• Creating an ML CI/CD pipeline
• Deploying models on Kubernetes
• Tracking experiments using MLflow
• Managing model versions
• Provisioning infrastructure using Terraform
• Monitoring model performance
• Building an end-to-end MLOps pipeline

Preparation Plan

7–14 Days

Suitable for experienced professionals.

Focus on MLOps concepts, Docker, Kubernetes, CI/CD, Terraform, MLflow, model deployment, and monitoring.

30 Days

Suitable for working engineers.

Spend the first two weeks on Linux, Python, Git, Docker, Kubernetes, cloud, and CI/CD. Use the remaining time for MLflow, model deployment, monitoring, and hands-on projects.

60 Days

Best for beginners.

Start with Linux, Python, Git, DevOps, and cloud fundamentals. Then learn containers, Kubernetes, Terraform, ML lifecycle management, deployment, and observability.

Common Mistakes

Avoid these common mistakes:

• Learning tools without understanding the MLOps lifecycle
• Focusing only on model training
• Ignoring Docker and Kubernetes
• Skipping hands-on projects
• Memorizing commands without understanding concepts
• Ignoring monitoring and observability
• Ignoring security and governance

Choose Your Path

After MLOCP, you can select a specialization based on your career goals.

DevOps

Focus on automation, CI/CD, containers, cloud, and infrastructure.

DevSecOps

Focus on secure pipelines, vulnerability scanning, access control, and security automation.

SRE

Focus on reliability, monitoring, incident management, SLOs, and observability.

AIOps/MLOps

Continue deeper into ML platforms, model monitoring, AI operations, and production AI systems.

DataOps

Focus on data pipelines, data quality, governance, lineage, and automation.

FinOps

Focus on cloud cost optimization, resource utilization, GPU cost management, and financial governance.

Best Next Certification

After MLOCP, professionals can consider certifications in:

• AIOps
• DevOps
• DevSecOps
• SRE
• DataOps
• FinOps

The best choice depends on your current role and future career direction.

Training and Certification Support Institutions

Some institutions that may help learners with MLOps, DevOps, and related technology training include:

DevOpsSchool – Provides MLOps Certified Professional training and certification with practical learning and hands-on concepts.

Cotocus – Offers technology consulting and professional learning support around modern engineering practices.

Scmgalaxy – Provides resources and training related to DevOps, SCM, automation, and software engineering.

BestDevOps – Focuses on DevOps tools, cloud, containers, automation, and related professional skills.

DevSecOpsSchool – Useful for professionals interested in security-focused DevOps and secure delivery practices.

SRESchool – Focuses on site reliability engineering, monitoring, observability, and production reliability.

AIOpsSchool – Useful for professionals exploring artificial intelligence in IT operations and automation.

DataOpsSchool – Focuses on data operations, data pipelines, governance, and automation.

FinOpsSchool – Useful for engineers and managers interested in cloud cost management and FinOps practices.

Conclusion

The MLOps Certified Professional (MLOCP) certification can be a useful learning path for professionals who want to understand how machine learning systems are deployed and managed in production.Instead of focusing only on ML models, MLOps combines software engineering, DevOps, cloud, containers, automation, monitoring, and governance.For software engineers and technical professionals planning to work with production AI and machine learning platforms, MLOCP can provide a structured way to build practical MLOps knowledge.