In an era where Artificial Intelligence (AI) drives decision-making and predictive intelligence shapes digital ecosystems, it’s not enough to develop machine learning models. Enterprises across the world are seeking professionals who can deploy, monitor, and manage these models efficiently at scale. This is where MLOps (Machine Learning Operations) becomes indispensable.
To help professionals gain hands-on expertise in this domain, DevOpsSchool has introduced the globally recognized MLOps Foundation Certification Course — designed and mentored by Rajesh Kumar, one of the world’s foremost experts in DevOps, SRE, AIOps, and MLOps. This course empowers you to bridge the critical gap between machine learning and operations, preparing you to become an industry-ready MLOps engineer.
Understanding MLOps: The Core of Modern AI Systems
MLOps, short for Machine Learning Operations, is the application of DevOps principles to the ML lifecycle — automating and optimizing everything from data preparation and model building to deployment and monitoring.
Why MLOps matters:
- Automates ML workflows for efficiency and reliability.
- Bridges collaboration between data scientists, engineers, and operations teams.
- Ensures reproducibility, scalability, and consistent model performance.
- Reduces time-to-market for AI-driven solutions.
- Streamlines compliance, governance, and continuous improvement mechanisms.
MLOps professionals play a key role in enabling AI adoption within organizations by operationalizing machine learning at scale and ensuring continuous model optimization.
About the MLOps Foundation Certification
The MLOps Foundation Certification offered by DevOpsSchool is a structured learning journey that introduces you to key principles, tools, and processes used to automate ML model management.
This 5-day, hands-on certification course blends live instructor-led sessions, practical labs, and real-world projects, providing you with a balance of technical depth and applied learning.
Key Objectives of the Certification:
- Understand the principles and lifecycle of MLOps.
- Develop reproducible, automated ML pipelines.
- Manage model deployment, versioning, and monitoring at scale.
- Implement CI/CD workflows tailored for machine learning systems.
- Strengthen collaboration between data science and IT operations teams.
It’s a highly recommended certification for data scientists, ML engineers, DevOps professionals, and AI aspirants seeking career advancement in the fast-evolving machine learning ecosystem.
What You’ll Learn in the MLOps Foundation Program
DevOpsSchool ensures a practical, well-rounded syllabus designed to help you deploy, manage, and monitor ML models in real-world environments.
| Module | What You’ll Learn |
|---|---|
| Introduction to MLOps | Understand MLOps principles, lifecycle, and its integration with traditional DevOps. |
| Automating ML Pipelines | Build continuous integration and deployment (CI/CD) for ML workflows using Jenkins and GitOps tools. |
| Data Engineering for MLOps | Design robust data pipelines using Python, TensorFlow, and Scikit-learn. |
| Version Control for Models | Implement data and model versioning for reproducibility using Git and MLflow. |
| Model Deployment Strategies | Deploy models using Kubernetes, Docker, and Terraform (blue-green, canary, shadow deployments). |
| Monitoring and Governance | Detect model drift, ensure compliance, and monitor performance with Prometheus and Grafana. |
| Infrastructure as Code (IaC) | Automate provisioning of ML infrastructure with Terraform and AWS tools. |
| Governance and Security | Apply security, auditing, and access control best practices across your ML setup. |
Professionals complete practical labs using industry-standard tools like Kubeflow, MLflow, Terraform, Jenkins, Docker, Prometheus, and Grafana.
Why Choose DevOpsSchool for MLOps Foundation Certification?
DevOpsSchool stands as a global leader in DevOps and AI-operations training, with a track record of hundreds of successful batches worldwide. Here’s why learners choose DevOpsSchool for their MLOps journey :
| Feature | DevOpsSchool Advantage | Other Providers |
|---|---|---|
| Trainer Expertise | Mentored by Rajesh Kumar (20+ years in DevOps, AI, SRE, and MLOps) | Generic trainers with limited field experience |
| Course Format | Live instructor-led sessions + cloud-based hands-on labs | Mostly pre-recorded lessons |
| Curriculum Design | Research-based, covering full ML lifecycle automation | Focuses on isolated ML topics |
| Career Support | Lifetime access to learning materials, job updates, mock interview kits | Limited duration support |
| Certification Value | Accredited by DevOpsCertification.co (Industry-recognized) | Non-accredited or local certificates |
| Community Access | Active forums, mentoring, and DevOps global community | No community interaction |
| Pricing and Flexibility | Group discounts and re-scheduling options | Fixed-cost, rigid programs |
The DevOpsSchool Learning Experience
Learning at DevOpsSchool goes beyond lectures. It emphasizes real-life application, collaboration, and continuous improvement. Each learner gets full access to:
- Lifetime LMS Access (24/7 availability of recorded classes, notes, slides, and test materials).
- Hands-on lab practice kits in an AWS cloud environment.
- Step-by-step web tutorials built by industry experts.
- Technical support and participation in global professional forums.
- Lifetime mentorship from instructors like Rajesh Kumar and his network of global trainers.
Training Delivery Options
| Mode | Description | Duration |
|---|---|---|
| Instructor-Led Online Classes | Live sessions with cloud-based lab exercises | 5 Days |
| Self-Paced Video Learning | Recorded sessions via LMS | ~35 Hours |
| Corporate Training | Customized program for teams (online/classroom) | 2–3 Days |
| One-to-One Mentorship | Personalized learning plan and career coaching | Flexible |
Course Benefits and Career Outcomes
Earning an MLOps Foundation Certification from DevOpsSchool positions professionals for leadership roles in the AI/ML industry.
Top Benefits:
- Enhanced Employability: Get recognized as a certified MLOps practitioner in enterprise AI teams.
- High-Paying Roles: Certified professionals qualify for roles like MLOps Engineer, DataOps Specialist, DevSecOps Analyst, and ML System Architect.
- Master Automation: Learn CI/CD pipelines, versioning, monitoring, and infrastructure automation end-to-end.
- Global Recognition: Gain a certification respected within the DevOps and AI industries worldwide.
Salary Snapshot: Global MLOps Engineer Roles
| Region | Entry-level | Mid-career | Senior-level |
|---|---|---|---|
| USA | $100,000–$125,000 | $130,000–$155,000 | $170,000+ |
| India | ₹8–10 LPA | ₹15–25 LPA | ₹28–35 LPA |
| Europe | €80,000+ | €100,000+ | €130,000+ |
Global companies like Google, AWS, Microsoft, and IBM are actively hiring certified MLOps professionals who can operationalize AI pipelines across hybrid cloud ecosystems.
Mentorship by Rajesh Kumar
A unique aspect of this program is mentorship by Rajesh Kumar — a pioneer in DevOps, Cloud Transformation, SRE, and MLOps education.
He has over 20 years of industry experience delivering training, consulting, and digital transformation solutions globally. Under his guidance, learners develop a deep technical and strategic understanding of MLOps, preparing them for enterprise-scale challenges.
His mentoring ensures a humanized, real-industry learning experience — not just technical instruction, but career-focused development.
Comparison: Traditional ML Workflow vs. MLOps-Driven Workflow
| Aspect | Traditional ML Workflow | MLOps Workflow |
|---|---|---|
| Team Collaboration | Isolated (Data Science vs. Ops) | Integrated cross-functional teams |
| Deployment Process | Manual, error-prone | Automated CI/CD pipelines |
| Model Updates | Periodic retraining | Continuous training automation |
| Scalability | Limited to single environments | Scalable via containers (Docker, Kubernetes) |
| Monitoring | Minimal post-deployment checks | Continuous feedback and drift monitoring |
| Governance | Inconsistent compliance tracking | Centralized governance and audit-ready logs |
Certification and Recognition
After successful completion of this program, participants earn the DevOps Certified Professional (DCP) credential accredited by DevOpsCertification.co.
This verifies your proficiency in applying MLOps practices for:
- Automated ML pipeline deployment.
- Model governance and versioning.
- Infrastructure management using IaC tools.
- Effective monitoring and alert-driven optimization.
Backed by DevOpsSchool’s reputation, this certification enhances employability across industries that rely on ML infrastructure — finance, healthcare, e-commerce, cybersecurity, and cloud computing.
Enroll Today: Your Path to MLOps Mastery
Becoming MLOps certified means standing at the forefront of AI-driven innovation. Whether you’re a data scientist, DevOps professional, or new to machine learning, this course prepares you to operationalize AI at scale.
Start your journey today with DevOpsSchool’s globally recognized certification:
MLOps Foundation Certification Course
For details, inquiries, or enrollment:
- Email: contact@DevOpsSchool.com
- Phone & WhatsApp (India): +91 99057 40781
- Phone & WhatsApp (USA): +1 (469) 756-6329
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