As Artificial Intelligence continues to transform industries, the ability to successfully deploy and manage machine learning (ML) models in production has become a mission-critical skill. While data scientists excel at building models, the gap between model development and production deployment remains one of the biggest challenges in AI-driven organizations. This is where MLOps (Machine Learning Operations) steps in — and mastering it begins with the MLOps Foundation Certification at DevOpsSchool.
Designed and governed by Rajesh Kumar—a global authority with over two decades of expertise in DevOps, DevSecOps, MLOps, and Cloud Computing—this certification program gives professionals a comprehensive foundation in automating, scaling, and maintaining machine learning workflows effectively.
What is MLOps and Why It Matters
MLOps (Machine Learning Operations) is the fusion of machine learning, DevOps, and data engineering principles. It ensures that machine learning models move reliably from conception to production while maintaining reproducibility, accountability, and automation throughout the lifecycle.
In simpler terms, MLOps is the “DevOps for AI”—enabling consistent delivery of ML models with automated pipelines, version control, and monitoring mechanisms.
Key Benefits of Implementing MLOps:
- Streamlines and automates ML deployment workflows.
- Detects and mitigates model drift early.
- Enhances collaboration between data scientists, developers, and operations teams.
- Reduces operational costs and downtime.
- Increases speed-to-market for AI solutions.
Organizations adopting MLOps witness 30–50% faster model deployment cycles, making it a must-have discipline in every modern enterprise.
Overview of the MLOps Foundation Certification
The MLOps Foundation Certification by DevOpsSchool provides professionals the skills to operationalize machine learning models across the entire development pipeline—from data management to continuous delivery and monitoring.
This foundational course is ideal for professionals aiming to bridge the gap between ML experimentation and production pipelines.
Key Training Features
| Feature | DevOpsSchool Advantage | Others |
|---|---|---|
| Course Duration | 8–12 Hours of Online Instructor-led Training | Basic Recorded Sessions |
| Flexibility | Live, Self-paced & Corporate Learning Options | Limited Modes |
| Access | Lifetime LMS + Technical Support | Restricted Duration |
| Certification | Industry-recognized & Project-based Certification | Generic Certificate |
| Mentor | Rajesh Kumar, 20+ Yrs Experience | Varies |
| Learning Resources | Lifetime Notes, PDFs, Dumps, Interview-Kit | Limited Documentation |
| Labs & Demos | 50% of Course time dedicated to Real Labs | Minimal Hands-on Learning |
Course Objectives and Learning Outcomes
The MLOps Foundation Certification helps participants grasp the core mechanisms of automating ML workflows and implementing governance across AI systems.
By completing this program, you will:
- Understand the concepts, architecture, and lifecycle of MLOps.
- Learn to implement CI/CD for ML models.
- Automate model training, deployment, and monitoring.
- Gain expertise in model versioning and governance.
- Ensure reproducibility, compliance, and ethical AI operations.
- Build foundational confidence to manage collaborative ML pipelines in real business environments.
Course Curriculum: What You’ll Learn
This course combines theoretical principles with real-time, instructor-led practical labs, ensuring learners acquire both depth and hands-on experience.
Module Breakdown:
- Introduction to MLOps
- Core principles of MLOps and its business impact.
- Understanding ML operational challenges in production.
- Machine Learning Lifecycle
- Data curation, transformation, and feature engineering.
- Model training, evaluation, validation, and versioning.
- CI/CD Automation for ML
- Build end-to-end ML pipelines using Jenkins and GitHub Actions.
- Incorporate IaC (Infrastructure as Code) with Terraform.
- Containerization and Orchestration
- Use Docker and Kubernetes for scalable deployment.
- Implement flexible deployment models (Blue-Green, Canary).
- Monitoring and Model Governance
- Detect drift using Prometheus and Grafana.
- Define governance frameworks for compliant model deployment.
- Hands-on Cloud Implementation
- Deploy on AWS, Azure, or GCP environments.
- Build and scale CI/CD workflows within cloud-native ecosystems.
Hands-On Approach and Real-World Projects
Approximately 50% of the course is dedicated to hands-on exercises and lab simulations, giving participants practical experience with essential MLOps tools.
Practical Sessions Involve:
- Building and deploying models using Docker and Kubernetes.
- Automating model pipelines using Jenkins and ArgoCD.
- Version-controlling datasets and models using DVC and Git.
- Integrating monitoring and alerts using Prometheus and Grafana.
- Managing model lifecycle with MLflow and Kubeflow.
Participants will also receive mock exams, assessments, and real-world case studies to simulate enterprise-level challenges.
Why Choose DevOpsSchool for MLOps Foundation Certification
DevOpsSchool is one of the most trusted global platforms for professional DevOps and Cloud certifications. With a focus on instructor-led mentoring, practical exposure, and lifelong access, it ensures your learning has lasting value.
Here’s what sets DevOpsSchool apart:
- Lifetime LMS access with recordings and resources.
- Expert faculty with 15+ years of enterprise experience.
- AWS-based lab setups for real project simulation.
- Interview preparation kits and community job updates.
- Industry networking via DevOps forums and learning communities.
All sessions are conducted via GoToMeeting, and learners can attend live batches or view recordings at their convenience.
Who Should Take This Course?
This certification is ideal for:
- Data Scientists seeking production-level deployment skills.
- Machine Learning Engineers aiming to expand into pipeline automation.
- DevOps Professionals integrating AI workflows.
- Cloud Engineers managing scalable ML infrastructure.
- Software Developers and IT Managers designing AI systems for enterprise use.
By earning this credential, participants future-proof their careers and align themselves with high-demand MLOps roles globally.
Industry Relevance and Career Growth
Professionals certified in MLOps gain access to some of the most promising roles in the tech industry.
Common Job Roles Include:
- MLOps Engineer
- Machine Learning Engineer
- DataOps Engineer
- AI Platform Architect
- Cloud DevOps Engineer
Salary Insights (Global Averages):
| Experience Level | Average Annual Salary |
|---|---|
| Entry-level | USD $100,000 |
| Mid-level | USD $130,000 |
| Senior Level | USD $150,000+ |
The certification helps professionals move beyond experimentation into scalable ML applications, driving real-world AI adoption.
Learn from Rajesh Kumar – The Global DevOps Visionary
The certification is mentored by Rajesh Kumar—an internationally acclaimed trainer with over two decades of expertise in DevOps, MLOps, Cloud, SRE, and AIOps.
His sessions are renowned for their real-world simulations, simplified learning, and career transformation success stories. Rajesh has trained over 100,000 professionals worldwide, including engineers at leading tech enterprises.
Under his guidance, every participant learns to think, build, and scale like an enterprise-grade MLOps practitioner.
How to Enroll
Kickstart your MLOps journey with DevOpsSchool today.
Visit: MLOps Foundation Certification
Explore: Devopsschool
Get in touch:
Email: contact@DevOpsSchool.com
Phone (India): +91 99057 40781
Phone (USA): +1 (469) 756-6329
Final Thoughts
The MLOps Foundation Certification from DevOpsSchool isn’t just a course — it’s the stepping stone to mastering the automation, monitoring, and scalability of machine learning systems. Whether you’re a data scientist or a DevOps engineer, this certification empowers you to bring AI innovation seamlessly into production—efficiently, ethically, and at scale.
Now is the time to bridge the gap between ML research and real-world application with the power of MLOps—and become an AI operations pioneer.