Artificial Intelligence has revolutionized how businesses operate, from predictive analytics to intelligent automation. But the major challenge isn’t building machine learning models—it’s operationalizing them effectively. That’s where MLOps (Machine Learning Operations) comes in. It combines DevOps methodologies and machine learning frameworks to ensure scalable, reliable, and automated ML model deployment and monitoring.
The MLOps Foundation Certification by DevOpsSchool is the perfect starting point for professionals aspiring to strengthen their skills in MLOps frameworks, automation pipelines, and model governance. Designed and mentored by Rajesh Kumar , a globally recognized leader in DevOps, MLOps, SRE, AIOps, and Cloud, this certification delivers hands-on learning and career-focused expertise in machine learning operations.
Understanding MLOps and Its Importance
MLOps bridges the gap between data scientists who design models and operations teams that deploy and maintain them. It aims to automate the entire machine learning lifecycle—from development and testing to deployment and retraining—using infrastructure automation and CI/CD practices.
Key MLOps benefits include:
- Faster Deployment Cycles: Automated pipelines for model integration and deployment.
- Reproducibility: Consistency in model versions across environments.
- Scalability: Efficient management of large-scale model deployments using containerization.
- Collaboration: Seamless communication between data, DevOps, and business teams.
With over a 40% growth in MLOps job roles in 2025, according to ProjectPro Insights, certified professionals are in high demand across industries adopting AI-driven decision-making systems.
Overview of the MLOps Foundation Certification
The MLOps Foundation Certification Course offered by DevOpsSchool empowers professionals to efficiently apply MLOps principles, ensuring models are deployed, monitored, retrained, and governed effectively.
This program provides practical, tool-centric sessions that simulate real-world scenarios, from model versioning and automation to continuous monitoring in production.
| Parameter | Details |
|---|---|
| Mode of Training | Online, Self-paced, or Corporate (Instructor-led) |
| Duration | 5 Days (approx.) |
| Mentor | Rajesh Kumar |
| Course Focus | Model Deployment, CI/CD for ML, Model Governance, Automation |
| Hands-On Components | Cloud Labs, Practice Projects, Mock Exams |
| Support | Lifetime LMS Access, Forum Discussions, Career Mentorship |
| Certification | Industry-recognized credential from DevOpsSchool and DevOpsCertification.co |
What You’ll Learn
The course curriculum is structured to equip participants with both foundational knowledge and applied expertise across the MLOps framework.
Module 1: Introduction to MLOps
- Overview of MLOps and its integration with DevOps and machine learning.
- Understanding the lifecycle from model design to production deployment.
Module 2: Automating ML Pipelines
- Designing CI/CD automation using tools like Jenkins and GitHub Actions.
- Building, testing, and deploying models with minimal manual intervention.
Module 3: Data & Model Versioning
- Version Tracking for datasets and model parameters.
- Using MLflow and Kubeflow for experiment management and reproducibility.
Module 4: Containerization & Orchestration
- Creating Docker images for ML models.
- Orchestrating production workloads with Kubernetes and Helm Charts.
Module 5: Model Monitoring & Performance Optimization
- Building real-time performance dashboards with Prometheus and Grafana.
- Managing model drift and automating retraining processes.
Module 6: Governance, Security & Compliance
- Establishing secure ML pipelines with GDPR and compliance tracking.
- Implementing model audit logs and governance strategies.
Key Benefits of the MLOps Foundation Certification
Earning a MLOps Foundation Certification provides industry-approved validation of your capability to operationalize ML systems efficiently.
| Benefits | Description |
|---|---|
| Career Progression | Certified professionals gain access to high-paying roles such as MLOps Engineer, ML Platform Architect, or Cloud ML Engineer. |
| Skill Advancement | Master automation, monitoring, and version control across ML pipelines. |
| Increased Productivity | Improve team collaboration and model deployment time by 50%. |
| Risk Reduction | Implement governance and monitoring controls to prevent model failures. |
| Networking Opportunities | Join DevOpsSchool’s global community of AI practitioners and mentors. |
Professionals report 30%-40% faster ML deployment cycles after adopting MLOps practices learned through this course.
Who Should Take This Course
- Machine Learning Engineers aiming to productionize and manage models at scale.
- Data Scientists looking to automate and streamline their workflows.
- DevOps Engineers who want to extend their capabilities into ML systems.
- Cloud Engineers and Architects focusing on scalable AI deployments.
- IT Managers and Technical Leads responsible for ensuring ML system reliability.
Core Tools and Technologies Covered
| Category | Tools Included |
|---|---|
| Containerization | Docker, Kubernetes |
| Pipeline Management | Jenkins, Airflow, CI/CD tools |
| Monitoring & Logging | Prometheus, Grafana |
| Version Control | MLflow, Git, GitHub |
| Infrastructure Automation | Terraform, CloudFormation |
| Cloud Platforms | AWS, Azure, GCP |
This multi-tool coverage ensures learners gain a cross-platform understanding essential for real-world system integration.
Course Methodology and Delivery
DevOpsSchool ensures a blended, interactive learning approach combining theory, practice, and assessment aligned to real-industry use cases.
- Instructor-led Live Sessions: Led by seasoned trainers under Rajesh Kumar’s supervision.
- Hands-on Labs in AWS: Live labs and exercises for deploying and managing ML pipelines.
- Mock Exams & Interview Kits: To prepare you effectively for certification exams and job interviews.
- LMS with Lifetime Access: Includes slides, notes, tutorials, and recorded sessions, accessible 24×7.
Comparison: Traditional ML vs MLOps-Driven Workflow
| Aspect | Traditional ML Workflow | MLOps-Based Workflow |
|---|---|---|
| Model Deployment | Manual and time-intensive | Automated with CI/CD |
| Collaboration | Isolated teams | DevOps-integrated teams |
| Monitoring | Limited model oversight | Continuous performance feedback |
| Scalability | Restricted to smaller models | Scalable cloud-native systems |
| Reusability | Minimal reusability | Full version control and modularization |
| Governance | Ad-hoc auditing | Continuous compliance and traceability |
Organizations that adopt MLOps frameworks achieve up to 60% improvement in production efficiency.
Why Choose DevOpsSchool for MLOps Training
- Expert-Led by Rajesh Kumar: Learn directly from a master trainer with 20+ years of expertise.
- Comprehensive Curriculum: Designed from extensive industry research and case studies.
- Hands-On Experience: Real-time projects using cloud platforms and modern toolchains.
- Lifetime Technical Support: Assistance through the DevOpsSchool community and LMS.
- Industry Recognition: Certification accredited by DevOpsCertification.co.
Visit DevOpsSchool to explore other complementary courses in DevOps, DataOps, and AIOps for future-ready professionals.
Certification Outcome
Upon completion, participants receive a globally recognized MLOps Foundation Certification, validating their ability to manage the end-to-end ML lifecycle, implement CI/CD models, ensure performance scalability, and maintain governance in production environments.
Graduates often secure roles such as:
- MLOps Engineer
- DataOps Specialist
- Cloud AI Engineer
- ML Platform Administrator
Contact Information
To get started or request a demo session, reach out:
- Email: contact@DevOpsSchool.com
- Phone & WhatsApp (India): +91 99057 40781
- Phone & WhatsApp (USA): +1 (469) 756-6329