In the era where machine learning (ML) drives digital transformation, the ability to turn models into scalable, production-ready systems is what sets true data professionals apart. That’s where MLOps (Machine Learning Operations) comes in.
To bridge the crucial gap between data science and production engineering, DevOpsSchool introduces its globally trusted program — the MLOps Certified Professional Course. This transformative course is designed by seasoned industry experts and mentored by Rajesh Kumar, a globally recognized thought leader in DevOps, DevSecOps, SRE, DataOps, AIOps, and MLOps.
This program enables learners to design, automate, and monitor the entire lifecycle of machine learning models using the latest tools and cloud platforms.
Understanding MLOps: The Backbone of AI Deployment
MLOps, short for Machine Learning Operations, is the practice of integrating ML modeling, DevOps automation, and data engineering to ensure stable and efficient delivery of machine learning models into production.
It brings together:
- Automation through CI/CD pipelines for ML.
- Versioning for datasets and models.
- Scalability using orchestration tools like Kubernetes.
- Continuous monitoring via Prometheus and Grafana.
- Experiment tracking and reproducibility via MLflow and Kubeflow.
The goal of MLOps is simple — efficient collaboration between data scientists, engineers, and operations teams to deploy reliable, secure, and adaptive ML systems.
Why Learn MLOps in 2025?
According to industry analyses, over 70% of AI projects fail to make it to production due to operational complexity. As organizations increasingly rely on artificial intelligence, demand for qualified MLOps engineers is soaring.
Professionals skilled in MLOps are becoming indispensable for maintaining continuous ML workflows — from data ingestion to version control, deployment, and scaling on cloud infrastructure.
Key reasons to learn MLOps now:
- Gain a rare, high-demand skillset that bridges data science and cloud DevOps.
- Future-proof your career in AI-driven industries.
- Learn cloud-native, containerized infrastructure management.
- Secure high-paying roles in AI deployment and governance.
Inside DevOpsSchool’s MLOps Certified Professional Course
The MLOps Certified Professional Training offered by DevOpsSchool is among the world’s most comprehensive programs that combine DevOps principles, ML engineering, and real-world project experience.
Key Course Highlights
| Feature | DevOpsSchool Advantage | Other Providers |
|---|---|---|
| Course Format | Live Interactive + Self Learning | Usually Pre-recorded |
| Duration | ~35 Hours | 20–25 Hours |
| Access | Lifetime LMS + Technical Support | Limited |
| Real-time Project | Yes | Not Always Included |
| Tools Covered | Docker, Kubernetes, Jenkins, MLflow, Kubeflow, AWS | Basic or Partial |
| Certification | DevOps Certified Professional (DCP) | Generic Certification |
| Governance | Mentored by Rajesh Kumar | Varies |
| Job Prep | Resume Guidance + Mock Interviews | Minimal Support |
What You’ll Learn: MLOps End-to-End Lifecycle
This program takes learners through the entire operational pipeline of machine learning — from data collection to deployment.
Core Learning Modules
- Introduction to MLOps
- Understanding lifecycle phases: training, deployment, and monitoring.
- The role of automation, reproducibility, and model governance.
- Automation with CI/CD and Bash Scripting
- Create automated pipelines for ML using Jenkins, ArgoCD, and Shell scripts.
- Implement testing automation using Pytest and CI/CD pipelines.
- Containerization and Infrastructure Management
- Build containers with Docker and orchestrate them using Kubernetes.
- Manage infrastructure at scale through Terraform and IaC.
- Cloud Deployment with AWS
- Configure EC2, S3, and SageMaker for large-scale ML model hosting.
- Apply autoscaling, serverless computing, and IAM-based access control.
- Experiment Tracking and CI Integration
- Use MLflow and Kubeflow Pipelines for model tracking, deployment, and versioning.
- Integrate ML experiments into enterprise CI/CD for continuous updates.
- Monitoring and Observability
- Track performance metrics with Prometheus and visualize results through Grafana.
- Automate alerting for model drift, performance degradation, and failures.
Why DevOpsSchool Stands Apart
DevOpsSchool has earned global recognition as a leader in DevOps, Cloud, and AI/ML training, thanks to its dedicated mentorship-driven learning model.
What Sets DevOpsSchool Apart:
- Expert-led sessions by Rajesh Kumar, a 20+ years veteran in DevOps transformation.
- Hands-on labs hosted on AWS cloud for real project experience.
- Interview Preparation Kit with 250+ real-world MLOps questions.
- Community & Forum Support where experts help resolve queries.
- LMS with Lifetime Access to recordings, notes, and certification dumps.
Real-World Tools and Frameworks Covered
| Category | Tools |
|---|---|
| Version Control | Git, GitHub |
| Orchestration | Kubernetes, Helm |
| Automation | Jenkins, ArgoCD, Terraform |
| ML Lifecycle | MLflow, Kubeflow |
| Monitoring | Prometheus, Grafana |
| Cloud Services | AWS EC2, S3, SageMaker |
| Development | Flask, Python, MySQL |
| Workflow Scheduling | Apache Airflow |
Participants will integrate these technologies in real-time projects, building and deploying models that simulate enterprise AI architecture.
Career Impact of MLOps Certification
Completing the MLOps Certified Professional program paves the way for diverse roles across AI, Cloud, and Engineering ecosystems.
Popular Job Roles:
- MLOps Engineer
- Machine Learning Engineer
- DataOps Engineer
- AI Infrastructure Architect
- Cloud DevOps Engineer
Salary Outlook (based on experience):
- Entry-level Engineer: $110,000/year
- Mid-Level Engineer: $135,000/year
- Senior MLOps Engineer: $147,000+/year
This certification positions you at the intersection of AI, DevOps, and Cloud, enabling global career advancement in technology innovation.
Additional Benefits
- Lifetime Technical Support & Access
- Mock Interviews and Resume Guidance
- 100+ Lab Assignments and 1 Real Industry Project
- LMS Access to Recorded Sessions, Dumps, and Notes
- Exclusive Forum for Career Discussion & Job Updates
Learn From the Best – Rajesh Kumar
The program is guided by Rajesh Kumar, one of the world’s leading DevOps and Cloud transformation mentors.
His hands-on training empowers learners to manage ML models in enterprise systems using advanced MLOps methodologies.
With his expertise across Kubernetes, Terraform, Jenkins, and AI Automation, Rajesh ensures every participant graduates with hands-on proficiency and career readiness.
How to Enroll
Transform your career with DevOpsSchool’s MLOps Certified Professional Program today.
Visit: MLOps Certified Professional Course
Learn more: DevOpsSchool.com
For enrollment and support, contact:
- Email: contact@DevOpsSchool.com
- Phone (India): +91 99057 40781
- Phone (USA): +1 (469) 756-6329
Final Thoughts
In a world where AI is reshaping industries, MLOps is the key to operationalizing intelligence.
With DevOpsSchool’s cutting-edge program, you don’t just learn — you build, deploy, and master the art of continuous ML at scale.
This certification gives you the foundation and confidence to become a leader in Machine Learning Engineering and DevOps integration.