Artificial Intelligence and Machine Learning are the cornerstones of modern innovation, helping businesses make smarter, faster, and more data-driven decisions. However, the biggest challenge today isn’t model creation—it’s deploying, managing, and monitoring ML models efficiently at scale. This is where MLOps (Machine Learning Operations) becomes indispensable.

To help professionals master this crucial discipline, DevOpsSchool has designed the MLOps Certified Professional Course—a comprehensive learning experience that builds job-ready expertise across ML deployment, CI/CD for models, observability, and collaboration between data scientists, ML engineers, and DevOps teams.

Understanding MLOps: The Bridge Between AI and Operations

MLOps (Machine Learning Operations) is an engineering practice that unifies Machine Learning, Software Development, and DevOps to streamline the entire ML lifecycle—from model training and deployment to monitoring and retraining.

By 2025, Gartner reports that 70% of enterprises will operationalize AI using robust MLOps frameworks to ensure model reliability, compliance, and automation.​

Core Goals of MLOps:

• Automate and standardize ML workflows
• Enable continuous integration and delivery (CI/CD) for ML pipelines
• Manage model versions and data drift
• Ensure transparency, reproducibility, and governance
• Monitor model performance in real-time

MLOps is no longer optional—it’s the foundation for scalable AI success.

Why Choose DevOpsSchool’s MLOps Certified Professional Program?

DevOpsSchool is a global leader in DevOps, Cloud, and AI domain training, with decades of expertise in building top-tier professionals. Its MLOps Certified Professional Course stands out for its hands-on, tool-focused curriculum and real-world projects guided by industry experts.

Key Learning Outcomes

Participants gain full-spectrum knowledge in the MLOps ecosystem, from automating ML workflows to managing real-time production systems.

1. MLOps Fundamentals and Lifecycle

• Introduction to MLOps principles, pipelines, and architecture
• Understanding collaboration between ML and DevOps teams
• Automating model deployment and retraining cycles

2. Core Tools and Technologies

• Docker & Kubernetes: for containerizing and scaling ML models
• MLflow & Kubeflow: for experiment tracking and model management
• Jenkins & ArgoCD: for continuous integration and continuous delivery
• Prometheus & Grafana: for production-level ML monitoring

3. Cloud Integration for MLOps

• Deploying models with AWS SageMaker, Azure ML, or Google AI Platform
• Managing scalable infrastructures using Terraform and AWS Lambda

4. Real-Time Project Work

• Practical lab sessions and scenario-based projects
• Version control for models and datasets with Git & GitHub
• Monitoring live ML models using Prometheus + Grafana dashboards

Unique Advantages of DevOpsSchool’s MLOps Training

Mentorship by Rajesh Kumar – The Global DevOps Thought Leader

The course is mentored by Rajesh Kumar, a globally recognized DevOps, DevSecOps, SRE, DataOps, AIOps, MLOps, and Cloud expert with more than 20 years of experience.

Under his guidance, participants gain practical exposure to industry-grade environments, automation strategies, and MLOps scaling techniques. His mentorship ensures that learners evolve from theoretical understanding to real-world competency.

Course Curriculum Overview

Real-World Projects and Practical Assignments

Each participant works on at least one real-time enterprise project, implementing a complete MLOps pipeline—from model versioning and Cloud deployment to monitoring via Grafana.

Some common project themes:

• Real-time fraud detection model pipeline
• Predictive maintenance using AWS SageMaker
• Automated ML model retraining using Kubeflow and Jenkins

Who Should Enroll?

This MLOps course is ideal for:

• Machine Learning Engineers
• Data Scientists transitioning to operations roles
• DevOps Engineers entering the AI ecosystem
• Cloud and Automation Engineers
• Professionals aspiring to become MLOps Engineers or AI Infrastructure Specialists

Certification and Career Prospects

Upon completion, participants earn the DevOps Certified Professional (DCP) accreditation, demonstrating their ability to design, deploy, and manage machine learning systems.

Common career roles after certification:

• MLOps Engineer
• ML Infrastructure Engineer
• AI/ML Consultant
• DataOps Architect
• DevSecOps Engineer with AI specialization

According to GSDC and industry sources, MLOps-certified professionals enjoy 40% higher job placement rates and salaries reaching $120,000+ annually in the U.S..​

Flexible Batches and Global Timings

These timing options make it convenient for learners from different time zones to join live sessions.

Contact Information

For enrollment and inquiries:

• Email: contact@DevOpsSchool.com
• Phone & WhatsApp (India): +91 99057 40781
• Phone & WhatsApp (USA): +1 (469) 756-6329