Artificial Intelligence (AI) and Machine Learning (ML) have reshaped the global technology landscape—but innovation doesn’t stop with building models. To truly succeed, organizations must operationalize ML models, ensuring scalability, security, and continuous improvement. That’s where MLOps (Machine Learning Operations) steps in.
For professionals aiming to bridge the gap between data science and DevOps, the MLOps Certified Professional Course from DevOpsSchool is a game-changing opportunity. Designed and mentored by Rajesh Kumar — a globally recognized DevOps, MLOps, and Cloud strategist — this program transforms participants into skilled MLOps engineers equipped to deploy, monitor, and manage machine learning models at scale.
Why MLOps? The Future of Machine Learning Operations
MLOps, or Machine Learning Operations, combines the strengths of DevOps, Data Engineering, and AI lifecycle management to bring models from experimentation to production efficiently.
According to industry reports, the MLOps market is forecasted to grow at a 39% CAGR by 2030 as enterprises increasingly implement ML models in real-world production environments.
Key Reasons Why MLOps is In-Demand:
- Facilitates collaboration between ML engineers, DevOps, and data teams.
- Automates the end-to-end ML lifecycle — from training to deployment.
- Reduces model downtime through continuous monitoring and retraining.
- Ensures scalability, governance, and compliance for enterprise-grade AI systems.
- Enables reproducibility and traceability of models for better audits and transparency.
In short, MLOps is no longer optional — it’s essential for advancing AI-driven products.
Course Overview: MLOps Certified Professional by DevOpsSchool
The MLOps Certified Professional Course by DevOpsSchool stands as one of the most comprehensive certifications available today. This program is crafted for data scientists, DevOps engineers, ML architects, and AI practitioners who want to automate and scale ML pipelines using tools like Docker, Kubernetes, Jenkins, Kubeflow, and MLflow.
Learning Objectives
Participants learn to:
- Automate ML model training, testing, and deployments using CI/CD pipelines.
- Manage version control for data, models, and parameters.
- Implement scalable ML workflows using Kubernetes and Docker.
- Monitor model performance and detect data drift in real time.
- Ensure security, compliance, and governance in ML production systems.
Course Duration and Delivery
| Learning Type | Duration | Format |
|---|---|---|
| Online Live Classes | 35 Hours | Instructor-led |
| Self-Paced Option | 35 Hours | Video Learning |
| Corporate Training | 2–3 Days | On Request |
| One-on-One Mentorship | Custom | Interactive Sessions |
Key Modules Covered in the Training
The course offers an end-to-end understanding of the MLOps lifecycle, ensuring participants are job-ready upon completion.
| Module | Topics Covered |
|---|---|
| Introduction to MLOps | Roles, Benefits, and Workflow of MLOps |
| Linux Fundamentals for MLOps | Command-line operations, scripting, automation |
| AWS for MLOps | Model deployment, S3 buckets, IAM roles, SageMaker integration |
| Docker and Kubernetes | Containerization, Kubeflow, Helm charts, orchestration |
| CI/CD for ML Models | Jenkins, GitHub Actions, and ArgoCD for automated ML pipelines |
| Monitoring with Prometheus and Grafana | Model tracking and performance metrics dashboards |
| MLflow and Kubeflow | Experiment tracking and model reproducibility |
| Python for API Deployment | Flask integration with MySQL for real-world ML serving |
| Terraform for IaC | Deploying cloud resources automatically |
| Airflow for ML Pipelines | Workflow scheduling and automation of retraining tasks |
Participants also gain hands-on exposure to model versioning, CI pipeline configurations, and distributed ML model management.
Why DevOpsSchool for MLOps Certification?
While multiple MLOps certifications exist, DevOpsSchool’s program excels through depth, mentorship, and community support.
DevOpsSchool is among the most trusted global platforms for professional DevOps, SRE, Cloud, and Automation training. Its MLOps program is benchmarked against 10,000+ industry job descriptions and 20+ years of technical experience, ensuring learners acquire in-demand skills.
Course Features and Benefits
| Features | DevOpsSchool | Other Platforms |
|---|---|---|
| Curriculum Depth | Covers full ML lifecycle with 25+ tools | Focused on limited areas |
| Mentorship | Rajesh Kumar’s 20+ years of experience | Generic trainers |
| Hands-on Projects | Real-time deployment and CI/CD pipelines | Theoretical only |
| Career Support | Mock Interviews & Resume Prep | Minimal support |
| Lifetime LMS Access | Yes — 24/7 recordings, notes, dumps | 6–12 months limited |
| Community Support | Active DevOps Forum & Job Updates | None |
| Certification | Globally recognized DCP (DevOps Certified Professional) | Non-accredited certificates |
Rajesh Kumar’s involvement ensures that each participant benefits from industry-proven practices and strategic architectural insights for deploying and managing models at scale.
Hands-On Projects: Learn by Doing
Learning MLOps concepts without real-world application is incomplete. DevOpsSchool ensures its learners build practical expertise through guided projects:
- CI/CD Pipeline for ML Model Deployment
Automate building, testing, and deploying an ML model to production using Jenkins, Docker, and Kubernetes. - Model Monitoring with Prometheus and Grafana
Create an observability stack to track key ML metrics such as latency, accuracy, and data drift. - Serving ML Models via Flask API
Develop RESTful endpoints to manage model predictions using Flask integrated with MySQL. - Infrastructure Automation with Terraform
Automate provisioning of AWS services (EC2, S3, IAM) using Terraform scripts. - End-to-End MLOps Pipeline with Kubeflow and MLflow
Orchestrate model training, versioning, and retraining workflow using these tools.
These projects simulate enterprise environments — ensuring learners can demonstrate tangible outcomes during job interviews.
Mentorship by Rajesh Kumar: A Global Influencer in DevOps & MLOps
The program is guided by Rajesh Kumar, a veteran DevOps and Cloud Transformation leader, with extensive expertise in DevSecOps, SRE, AIOps, and MLOps. His mentorship brings real-world understanding that bridges the gap between theoretical knowledge and enterprise-scale implementation.
Rajesh’s influence extends globally — with successful mentorship provided to thousands of IT professionals across India, the USA, and Europe. Under his direction, learners grasp not just tools, but strategic frameworks and best practices to apply MLOps in production.
Career Outcomes and Global Opportunities
Earning an MLOps certification opens up diverse and high-paying roles across industries. After completing the course, professionals can pursue the following roles:
- MLOps Engineer
- Machine Learning Infrastructure Architect
- DevSecOps Specialist in AI Systems
- Data Operations Engineer
- AI/ML Systems Administrator
Average Salary Trends (Global)
| Role | Early Career | Mid-Level | Experienced |
|---|---|---|---|
| MLOps Engineer (USA) | $111,000/year | $135,000/year | $150,000+/year |
| MLOps Engineer (India) | ₹8–15 LPA | ₹18–25 LPA | ₹30+ LPA |
These figures align with growing enterprise investments in AI, data governance, and scalable MLOps frameworks.
Certification and Recognition
Upon completion, participants earn DevOps Certified Professional (DCP) certification from DevOpsCertification.co, accredited and respected across the global IT workforce.
This validates your ability to:
- Build production-ready ML pipelines.
- Implement CI/CD for machine learning.
- Deploy and manage models using best-in-class cloud and container tools.
- Monitor performance and retrain models efficiently.
This certification is highly valued among recruiters hiring for roles combining DevOps and AI domains.
Post-Training Career Support and Community
DevOpsSchool ensures you don’t just complete the course — you start your career transformation.
Key Post-Training Benefits:
- Access to Interview Preparation Kits and mock sessions.
- Resume and LinkedIn optimization.
- Notifications of job openings through DevOpsSchool’s internal portal.
- Continuous community discussions via forums for problem-solving and collaboration.
How to Enroll
With flexible schedules and multiple modes of learning, the course fits perfectly into your professional life.
To enroll or get more details, visit:
MLOps Certified Professional Course by DevOpsSchool
Contact DevOpsSchool
- Email: contact@DevOpsSchool.com
- Phone (India): +91 99057 40781
- Phone (USA): +1 (469) 756-6329