In the ever-evolving realm of artificial intelligence, the efficient management of machine learning models has become a cornerstone for innovation. MLOps (Machine Learning Operations) bridges the gap between data science and operations, ensuring seamless workflows from model development to deployment and monitoring. To help professionals gain mastery of these principles, DevOpsSchool brings you the MLOps Foundation Certification Course—an international-level training designed and mentored by Rajesh Kumar, a globally recognized expert with over 20 years of experience in DevOps, MLOps, DataOps, AIOps, and Cloud Systems.
Whether you’re a data scientist looking to operationalize ML or a DevOps professional seeking to expand your expertise, this course empowers you with the knowledge to build reliable, automated, and scalable ML systems.
What is MLOps Foundation Certification?
The MLOps Foundation Certification is an industry-recognized credential aimed at professionals who want to establish a strong foundation in Machine Learning Operations. It introduces key principles, tools, and frameworks that enable teams to develop, deploy, monitor, and govern machine learning models efficiently at scale.
MLOps applies DevOps methodologies—such as continuous integration (CI), continuous delivery (CD), and infrastructure automation—to the ML lifecycle, ensuring AI systems perform consistently and remain reproducible across environments.
Why MLOps Certification is Essential in Today’s AI-Driven World
As organizations rapidly expand their AI initiatives, MLOps professionals have become indispensable. The global demand for skilled MLOps engineers is projected to grow by over 39% by 2030, making this a key career growth domain.
Key reasons to consider this certification include:
- Bridging the AI Deployment Gap: Most ML models fail post-deployment due to scalability and monitoring challenges; MLOps fixes this gap.
- Enhancing Model Reliability: Automate retraining and eliminate performance drift.
- Compliance and Governance: Maintain model transparency and regulatory compliance.
- High-Paying Career Path: MLOps engineers earn average salaries above $130,000 globally.
- Collaboration and Automation: Brings teams together—data scientists, engineers, and DevOps professionals—all working in harmony.
Why Choose DevOpsSchool for MLOps Foundation Certification
DevOpsSchool, a leader in DevOps and Cloud certifications worldwide, combines technical depth with a hands-on, mentor-led learning strategy. Each concept is tied to real-world applications to ensure learners can implement solutions immediately after training.
| Feature | DevOpsSchool | Others |
|---|---|---|
| Lifetime LMS & Technical Support | ✅ | ❌ |
| Hands-on AWS Labs | ✅ | ❌ |
| Instructor-led Live Training | ✅ | ❌ |
| Practical Case Studies | ✅ | ❌ |
| Mock Interviews & Certification Prep | ✅ | ❌ |
| Global Accreditation Recognition | ✅ | ❌ |
The program follows DevOpsSchool’s unique “Learn–Apply–Master” model, ensuring every learner transitions from theoretical knowledge to production-ready implementation.
Course Curriculum – Build Machine Learning Operational Mastery
The MLOps Foundation Certification is structured to build a solid understanding of fundamental principles while introducing learners to the complete MLOps lifecycle.
Core Learning Modules
- Introduction to MLOps
- Understanding the MLOps lifecycle, goals, and principles
- Differences between traditional ML and MLOps workflows
- Data, Model & Code Versioning
- Tracking datasets, experiments, and model versions
- Using Git, MLflow, and DVC for version control
- Automation & CI/CD for ML
- Building and automating ML pipelines using Jenkins and GitHub Actions
- Implementing blue-green, canary, and shadow deployment strategies
- Model Deployment Techniques
- Deploying ML models with Docker and Kubernetes
- Orchestrating microservices using Helm charts
- Monitoring, Drift Detection, and Governance
- Identifying data drift and model decay
- Monitoring model health with Prometheus and Grafana
- Implementing compliance and explainability measures
- Collaboration and Workflow Management
- Integrating data science and DevOps workflows
- Managing documentation using Confluence and Jira
Hands-On Labs and Real-World Projects
The course includes interactive AWS-based cloud labs that mimic enterprise-grade environments. Participants work on projects covering:
- End-to-end ML pipeline automation
- Continuous model integration and delivery (CI/CD) pipelines
- Model retraining and drift management
- Deployment and live monitoring using Grafana dashboards
This experiential setup ensures learners not only understand how to apply MLOps—but also why it matters for scaling AI operations.
Key Learning Outcomes
Upon completion, certified professionals will be able to:
- Design scalable and automated ML pipelines
- Deploy and monitor ML models in hybrid environments
- Manage data and model versioning effectively
- Implement continuous training (CT) and validation loops
- Collaborate effectively between data, DevOps, and IT teams
Through these outcomes, learners become competent in transforming experimental AI models into production-level machine learning systems.
Who Should Enroll?
The MLOps Foundation Certification is ideal for:
- Data Scientists keen on mastering model deployment
- DevOps Engineers integrating ML into existing CI/CD workflows
- AI/ML Practitioners seeking operational expertise
- Cloud Professionals & Engineers pursuing data-driven automation
- IT Managers & Architects aiming for end-to-end governance frameworks
The course suits both beginners and intermediate professionals, offering flexible formats—online interactive, self-paced, and corporate training options.
Mentorship by Rajesh Kumar – Elevating Expertise
The course is seasoned with the mentorship of Rajesh Kumar—a thought leader in DevOps, Cloud, and Automation training. With two decades of global experience, Rajesh focuses on hands-on problem-solving and industry alignment, ensuring learners gain highly practical, deployable skills.
Through Rajesh’s mentorship, students benefit from exposure to DevOps, DevSecOps, SRE, AIOps, MLOps, and Kubernetes ecosystems, blending holistic understanding with mission-critical execution.
Training Methodology and Course Duration
- Duration: 5 Days (Flexible Online/Corporate modes)
- Format: Instructor-Led Live or Self-Paced Online
- Training Breakdown:ActivityWeightageUnderstanding the Problems5%Concept Discussion10%Demo25%Lab & Exercises50%Assessments & Projects10%
Each session includes guided exercises, Q&A discussions, and mock tests to prepare participants for the final certification exam.
Global Recognition and Career Impact
MLOps Foundation Certification from DevOpsSchool is globally recognized, adding credibility to your professional profile across industries. Certified professionals gain access to high-demand opportunities such as:
- MLOps Engineer
- AI Infrastructure Engineer
- Machine Learning Operations Specialist
- Data Engineering Lead
- Cloud Ops Architect
Organizations across healthcare, finance, and tech sectors increasingly rely on MLOps professionals to operationalize machine learning models and ensure scalability with confidence.
What Students Say
Abhinav Gupta, Pune:
“Rajesh’s sessions were practical and insightful. His real-world MLOps deployment scenarios made concepts crystal clear.”
Indrayani, India:
“Brilliantly structured training. The combination of tools like MLflow, Docker, and Kubernetes gave us immense industry exposure!”
Vinayakumar, Bangalore:
“Excellent delivery, structured labs, and engaging mentorship—highly recommended for professionals in AI or Cloud Operations.”
Enroll Now – Build the Foundation for Scalable AI Excellence
Take the next step in your AI career with DevOpsSchool’s MLOps Foundation Certification. Learn from industry experts, gain hands-on experience, and acquire globally validated credentials that make you stand out in a competitive job market.
Contact DevOpsSchool:
- Email: contact@DevOpsSchool.com
- Phone & WhatsApp (India): +91 99057 40781
- Phone & WhatsApp (USA): +1 (469) 756-6329