Introduction: The Growing Need for MLOps and DevOps Skills
Artificial Intelligence (AI) and Machine Learning (ML) are transforming industries globally. While building ML models is critical, deploying them efficiently and managing them reliably requires MLOps integrated with DevOps practices.
MLOps ensures ML models are production-ready, continuously monitored, and scalable, while DevOps principles provide automation, CI/CD pipelines, and robust deployment strategies.
The MLOps Certified Professional Course by DevOpsSchool combines these disciplines, preparing professionals to handle real-world AI and ML operations effectively.
What Is MLOps and Its Connection to DevOps?
MLOps is the practice of applying DevOps principles to Machine Learning workflows. It ensures that ML models are not only developed but also deployed, monitored, and scaled efficiently.
Core Objectives
- Implement CI/CD pipelines for ML models using DevOps practices
- Automate model testing, deployment, and retraining
- Enable collaboration between data scientists, ML engineers, and operations teams
- Monitor ML models in production for reliability and performance
MLOps vs Traditional ML Workflow
| Feature | Traditional ML | MLOps + DevOps |
|---|---|---|
| Deployment | Manual and slow | Automated CI/CD pipelines |
| Monitoring | Minimal | Real-time monitoring & alerting |
| Collaboration | Siloed teams | Cross-functional DevOps & DataOps collaboration |
| Versioning | Manual | Automated tracking & reproducibility |
| Scalability | Limited | Cloud-native, scalable, and efficient |
Integrating DevOps with MLOps ensures faster delivery, higher reliability, and smoother scaling of AI systems.
About the MLOps Certified Professional Course
The MLOps Certified Professional Course offers a comprehensive, hands-on approach combining MLOps and DevOps methodologies. Learners gain skills to:
- Deploy and manage ML models efficiently
- Build CI/CD pipelines for ML workflows
- Automate version control, logging, and retraining
- Work with tools like MLflow, Kubeflow, Airflow, Docker, Jenkins, and cloud platforms like AWS, Azure, and GCP
The program includes live instructor-led training, real-world projects, and globally recognized certification.
Meet the Mentor – Rajesh Kumar
The course is led by Rajesh Kumar, a globally recognized expert in DevOps, MLOps, Cloud, and AI with over 20 years of industry experience.
Why Learn From Rajesh Kumar
- Expert in DevOps, MLOps, SRE, DataOps, AIOps, Kubernetes, and Cloud solutions
- Mentored 100,000+ professionals and 500+ corporate teams globally
- Implemented AI and ML solutions in enterprise production environments
- Recognized as a thought leader in modern DevOps and MLOps practices
“MLOps is the intersection of Machine Learning and DevOps. Our training emphasizes practical skills, real-world pipelines, and production readiness.”
— Rajesh Kumar, Mentor at DevOpsSchool
With his mentorship, learners gain deep technical knowledge and hands-on DevOps-integrated MLOps experience, making them ready for high-impact roles.
Course Modules Overview
| Module | Topics Covered |
|---|---|
| Introduction to MLOps & DevOps | MLOps lifecycle, architecture, CI/CD principles |
| Environment Setup | Python, Docker, Kubernetes, Jenkins, Airflow |
| ML Pipeline Automation | Build and automate ML pipelines, version control, experiment tracking |
| Model Deployment | CI/CD for ML models using MLflow and Kubeflow |
| Monitoring & Governance | Detect model drift, maintain logging, ensure compliance |
| Hands-on Projects | Real-world case studies across finance, healthcare, and e-commerce |
Who Should Enroll?
Ideal for professionals aiming to integrate DevOps practices with ML workflows:
- DevOps Engineers transitioning into MLOps
- Machine Learning Engineers
- Data Scientists and Analysts
- Cloud Engineers and Infrastructure Specialists
- Software Developers interested in AI operations
The course is designed for both beginners and experienced professionals aiming to become industry-ready MLOps practitioners.
Career Advantages of the MLOps + DevOps Certification
Integrating MLOps and DevOps skills opens high-demand career opportunities globally.
Potential Roles
- MLOps Engineer
- DevOps for ML Engineer
- ML Engineer
- DataOps Engineer
- AI Infrastructure Engineer
Salary Overview
| Role | India (INR) | Global (USD) |
|---|---|---|
| MLOps Engineer | ₹10–20 LPA | $100,000 – $150,000 |
| ML Engineer | ₹9–22 LPA | $95,000 – $140,000 |
| DevOps Engineer (ML pipelines) | ₹8–18 LPA | $90,000 – $130,000 |
With hands-on DevOps-integrated MLOps experience, certified professionals are highly sought after in AI, ML, and Cloud projects worldwide.
Why Choose DevOpsSchool?
- ✅ Mentorship by Rajesh Kumar – globally recognized DevOps and MLOps expert
- ✅ Hands-On Projects – real ML pipeline deployment with DevOps integration
- ✅ Flexible Learning – online live and weekend batches
- ✅ Globally Recognized Certification – validate your skills and expertise
- ✅ Career Support – resume, interview, and global opportunity guidance
Testimonials
“The MLOps + DevOps training helped me automate ML pipelines efficiently. Rajesh’s mentorship made the learning practical and applicable.”
— Ankit S., Data Engineer
“This course accelerated my career transition from DevOps to MLOps. The integration of CI/CD with ML workflows is a game-changer.”
— Priya R., ML Engineer
Enroll Today and Advance Your AI Career
The MLOps Certified Professional Course empowers you to master MLOps with DevOps integration, gain real-world experience, and advance your career in AI and ML operations.
Contact DevOpsSchool
- Email: contact@DevOpsSchool.com
- Phone & WhatsApp (India): +91 99057 40781
- Phone & WhatsApp (USA): +1 (469) 756-6329
- Website: www.DevOpsSchool.com
