Bridging the Gap Between Data Science and DevOps
Machine Learning (ML) is changing industries at lightning speed—but there’s one big challenge: most ML models never make it into production. Why? Because traditional data science workflows often lack scalability, automation, and reliability.
This is where MLOps (Machine Learning Operations) comes in—a revolutionary approach that combines the best of Machine Learning, DevOps, and Data Engineering to streamline model deployment, monitoring, and management at scale.
If you’re ready to master this fast-growing field, the MLOps Certified Professional (MLOCP) course by DevOpsSchool is your perfect starting point. With expert mentorship, hands-on projects, and a globally recognized certification, this course helps you gain the skills top employers are actively seeking in 2025 and beyond.
MLOps Certified Professional (MLOCP)
The MLOps Certified Professional (MLOCP) course by DevOpsSchool is a comprehensive, instructor-led training designed to make you proficient in end-to-end MLOps workflows. From setting up ML pipelines to automating deployment and managing models in production, this course covers everything you need to become an MLOps practitioner.
Whether you’re a data scientist, DevOps engineer, or ML enthusiast, this training provides a balanced blend of theory, tools, and hands-on implementation—ensuring you don’t just understand MLOps, but can apply it in real-world projects.
Key Highlights
- Mode: Online, Classroom, or Corporate Training
- Trainer: Rajesh Kumar (20+ years of DevOps & AI/ML experience)
- Duration: 35+ hours of live sessions + lifetime access
- Tools Covered: Docker, Kubernetes, MLflow, TensorFlow, Jenkins, Kubeflow, GitOps, and more
- Certification: Globally recognized MLOps Certified Professional (MLOCP) credential
Course Comparison Table
| Feature | DevOpsSchool MLOCP | Generic Online ML Course |
|---|---|---|
| Instructor Expertise | Rajesh Kumar (20+ years) | Often generic trainers |
| Hands-On Labs | Yes – real MLOps pipelines | Limited demos only |
| Tool Coverage | Extensive (Docker, MLflow, Kubeflow) | Basic overview |
| Project Work | 2+ industry-level projects | None or theoretical |
| Certification Support | Yes (MLOCP exam prep) | Not provided |
| Corporate Batch Option | Available | Rarely available |
This comparison highlights what makes DevOpsSchool stand out—real-world expertise, practical learning, and outcome-driven training.
Who Can Enroll in This Course
The MLOps Certified Professional (MLOCP) program is designed for a wide audience—whether you’re an ML beginner, a DevOps professional, or an organization wanting to scale ML operations.
Ideal for:
- Data Scientists who want to automate model deployment and monitoring.
- DevOps Engineers integrating ML workflows into CI/CD pipelines.
- Machine Learning Engineers aiming to improve scalability and reliability.
- Software Developers exploring AI integration.
- Data Engineers handling data pipelines for ML lifecycle.
- Tech Teams & Enterprises building production-grade ML systems.
If you’re passionate about AI, automation, and DevOps, this course is your key to mastering the future of intelligent systems.
Learning Outcomes: What You’ll Achieve
Upon completing the MLOps Certified Professional (MLOCP) course, you’ll be able to confidently design, deploy, and manage scalable machine learning systems in production environments.
You will learn to:
- Build end-to-end MLOps pipelines integrating CI/CD workflows.
- Use tools like MLflow, Kubeflow, Jenkins, and Docker for automation.
- Manage model versioning, retraining, and monitoring efficiently.
- Deploy machine learning models on Kubernetes and cloud platforms.
- Implement continuous training (CT) and continuous deployment (CD) for ML models.
- Apply best practices for model governance, scalability, and reproducibility.
MLOps Certification Roadmap & Course Modules
| Module | Topic | Tools/Concepts Covered |
|---|---|---|
| 1 | Introduction to MLOps | AI/ML fundamentals, lifecycle overview |
| 2 | Environment Setup | Docker, Python, Git, ML frameworks |
| 3 | Model Development & Versioning | MLflow, DVC, GitOps |
| 4 | CI/CD for Machine Learning | Jenkins, GitHub Actions, ArgoCD |
| 5 | Model Deployment | Kubernetes, Kubeflow, AWS Sagemaker |
| 6 | Monitoring & Governance | Prometheus, Grafana, Model Drift Detection |
| 7 | Project Work & Certification | Real-world pipeline build and deployment |
Each module is structured with hands-on labs, quizzes, and real-life case studies to ensure you get practical exposure to production-grade MLOps.
Why Choose DevOpsSchool for MLOps Training
When it comes to DevOps and emerging tech training, DevOpsSchool is a name trusted by thousands of professionals worldwide. Here’s why:
1. Expert Trainer – Rajesh Kumar
This program is led by Rajesh Kumar, a globally respected DevOps and AI/ML trainer with 20+ years of experience mentoring professionals from top enterprises. His insights bridge the gap between academic ML theory and real-world DevOps practice.
2. Hands-On, Real-World Training
DevOpsSchool emphasizes learning by doing. You’ll build pipelines, automate workflows, deploy ML models, and monitor them in real time using industry-grade tools.
3. Industry-Recognized Certification
The MLOps Certified Professional (MLOCP) credential from DevOpsSchool holds strong recognition in the DevOps and AI communities. It validates your ability to operationalize ML models effectively.
4. Lifetime Learning Access
Once you enroll, you’ll have lifetime access to resources, videos, and updates—so you can keep learning as MLOps tools evolve.
5. Flexible Learning Options
Choose from self-paced, live online, or corporate group sessions—DevOpsSchool adapts to your learning preferences and professional schedule.
Career Benefits & Real-World Value
The demand for MLOps professionals is exploding. With AI adoption accelerating across every sector, companies now need experts who can bridge the gap between ML development and production systems.
According to LinkedIn and Indeed reports, MLOps roles have grown by over 300% in the past three years—and salaries are climbing fast.
Here’s what you can expect after certification:
- In-Demand Roles: MLOps Engineer, ML Architect, AI Operations Lead, DevOps for AI Specialist
- High Earning Potential: Certified professionals earn 30–40% more on average.
- Career Versatility: Opportunities across AI startups, tech giants, finance, healthcare, and automation sectors.
- Practical Edge: Master both the ML lifecycle and DevOps automation, making you a hybrid expert.
- Global Recognition: Gain credibility as a certified MLOps professional under an established global brand.
With DevOpsSchool and Rajesh Kumar, you’ll not only learn MLOps—you’ll experience it in action through guided projects and mentorship.
Conclusion: Transform Your ML Career with MLOps
The future of AI lies not just in building models, but in successfully operationalizing them at scale. Whether you’re a data scientist tired of deployment bottlenecks or a DevOps engineer eager to expand into AI, the MLOps Certified Professional (MLOCP) program is your bridge to success.
Join DevOpsSchool, one of the world’s leading platforms for DevOps, Cloud, and MLOps training, and learn directly from industry veteran Rajesh Kumar—a trainer trusted by thousands of professionals globally.
It’s time to level up your AI and DevOps career—enroll today and become a certified MLOps professional.
👉 Enroll Now: MLOps Certified Professional (MLOCP) by DevOpsSchool
📩 Contact Us:
✉️ contact@DevOpsSchool.com
📞 +91 99057 40781 (India)
📞 +1 (469) 756-6329 (USA)
🌐 Website: DevOpsSchool
👨🏫 Trainer: Rajesh Kumar
