In the world of Artificial Intelligence and Data Science, creating machine learning (ML) models is only half the journey. The real challenge lies in deploying, monitoring, and maintaining these models efficiently and at scale. This is precisely where MLOps (Machine Learning Operations) comes in — an approach that blends machine learning and DevOps to streamline the production lifecycle of ML models.
The MLOps Foundation Certification by DevOpsSchool provides professionals and organizations with the skills to automate, manage, and operationalize ML models in real-world environments. Guided by Rajesh Kumar — a global thought leader in DevOps, DevSecOps, DataOps, and MLOps — this certification empowers learners to implement scalable and reliable AI pipelines while ensuring compliance and governance.
What is MLOps and Why It Matters?
MLOps (short for Machine Learning Operations) combines the principles of DevOps and AI Engineering to automate and manage the entire ML lifecycle — from data preparation and model training to deployment and continuous monitoring. In simple terms, MLOps ensures that machine learning systems remain accurate, scalable, and secure in production environments.
Organizations use MLOps to:
- Automate ML workflows and reduce deployment time
- Maintain version control for models and data
- Monitor model drift to ensure performance
- Improve collaboration between Data Science and IT teams
- Achieve compliance, governance, and reproducibility
With AI adoption skyrocketing, enterprises are actively hiring MLOps-certified professionals to strengthen their data pipelines and deliver faster, more reliable intelligence.
Why Choose DevOpsSchool for MLOps Foundation Certification?
DevOpsSchool stands as a global authority in DevOps, Cloud, and Automation training. Boasting over 18 years of industry experience and guidance from Rajesh Kumar, the institute has trained and certified over 100,000 professionals across the world.
Here’s why DevOpsSchool’s MLOps Foundation Certification is the preferred choice for professionals and enterprises:
- Expert-Led Training: Instructor-led live sessions by certified professionals with 10–15 years of MLOps expertise.
- Hands-on Practical Labs: Real-world scenarios on AWS, Kubernetes, Docker, and MLflow.
- Structured Curriculum: Covers model versioning, deployment, CI/CD automation, and ML monitoring.
- Career Support: Mock interviews, job alerts, and resume assistance for all learners.
- Lifetime Access: Recordings, slides, quizzes, and reading materials via DevOpsSchool’s LMS portal.
- Global Recognition: Industry-wide acceptance through DevOpsCertification.co accreditation.
Course Overview – MLOps Foundation Certification Program
| Aspect | Details |
|---|---|
| Certification Name | MLOps Foundation Certification |
| Training Duration | 8–12 Hours (online) / 3 Days (Corporate) |
| Mode of Learning | Instructor-Led |
| Trainer | Rajesh Kumar |
| Prerequisites | Basic understanding of ML and DevOps |
| Practical Environment | AWS Cloud and Kubernetes Labs |
| Certification Partner | DevOpsSchool & DevOpsCertification.co |
The course combines conceptual learning, hands-on labs, and assessment projects, ensuring participants not only understand MLOps theory but can also implement solutions professionally.
Key Learning Modules of the MLOps Foundation Certification
This certification is designed to provide a comprehensive understanding of the MLOps lifecycle and tools essential to deploy machine learning models effectively.
Module 1: Introduction to MLOps Fundamentals
- Understanding MLOps methodology and lifecycle
- Differences between MLOps, DevOps, and DataOps
- Common challenges in productionizing ML models
Module 2: Building End-to-End ML Pipelines
- Setting up CI/CD pipelines for machine learning models
- Implementing data ingestion, transformation, and model training automation
- Tools: Jenkins, MLflow, Airflow, Terraform
Module 3: Model Deployment and Versioning
- Managing ML models across environments
- Using Docker and Kubernetes for containerized model deployment
- Version tracking with Git and MLflow
Module 4: Monitoring, Governance & Compliance
- Continuous model monitoring for performance and drift
- Using Prometheus and Grafana for real-time tracking
- Managing compliance, auditing, and governance standards
Module 5: Hands-On Project & Certification Exam
- Participants build a live ML project using cloud-based tools.
- Final assessment includes quizzes, mock tests, and project evaluation.
Course Features and Benefits
| Feature | DevOpsSchool | Other Providers |
|---|---|---|
| Lifetime LMS Access | ✔️ | ❌ |
| Hands-on Labs on Cloud | ✔️ | Limited |
| Real-Time Industry Scenarios | ✔️ | ❌ |
| Interview Kits & Dumps | ✔️ | ❌ |
| Mentor Support (Rajesh Kumar) | ✔️ | ❌ |
| Career Guidance & Job Alerts | ✔️ | Partial |
Who Should Take This Certification?
The MLOps Foundation Certification is ideal for:
- Data Scientists – Automate and monitor ML workflows
- Machine Learning Engineers – Deploy models at scale
- DevOps Professionals – Transition into Data/MLOps roles
- AI Engineers – Build reproducible ML production frameworks
- Software Developers – Integrate ML systems into production
Career Advantages of MLOps Certification
With the MLOps Foundation Certification, professionals can access a wealth of career opportunities across industries like finance, healthcare, e-commerce, and cybersecurity.
Average Salaries (as per industry data):
- MLOps Engineer: $130,000/year (USA)
- DataOps Engineer: $125,000/year
- AI/ML Specialist: $140,000/year
This certification also acts as a stepping stone toward advanced designations like:
- MLOps Certified Professional
- DataOps Certified Engineer
- AI/ML Architect
Why Organizations Should Upskill Teams with MLOps
For enterprises, adopting MLOps practices is crucial in:
- Reducing Model Deployment Times by 60–70%
- Improving Model Accuracy via continuous training and monitoring
- Scaling AI Operations across departments
- Lowering Downtime Risks with automated incident detection
By enrolling teams in the DevOpsSchool’s corporate MLOps Foundation Program, organizations can strengthen collaboration between DevOps, data science, and cloud engineering teams.
Testimonials from Certified Professionals
Abhinav Gupta, Pune:
“Rajesh Kumar’s approach simplified complex ML concepts. The labs and live environment exercises gave me real confidence.”
Indrayani, India:
“Rich in hands-on labs and full of practical insights. This course was exactly what I needed to step into the world of MLOps.”
Vinayakumar, Bangalore:
“The mentorship, LMS support, and mock interview sessions were highly impactful. I’m now managing ML operations effectively at work.”
Training Methodology
DevOpsSchool employs a blended-learning approach, combining theoretical instruction with cloud-based hands-on labs.
Training Flow Includes:
- Conceptual Learning (10%)
- Demos (25%)
- Practical Lab Exercises (50%)
- Assessments & Projects (10%)
- Discussions & Real-world Case Studies (5%)
Get Certified in MLOps – Join the DevOpsSchool Community
The MLOps Foundation Certification equips you with the tools, frameworks, and confidence to run next-gen ML operations effectively. Under the leadership of Rajesh Kumar, this program prepares you for the AI-driven transformation shaping future industries.
Start your journey toward becoming a certified MLOps professional today!
Contact Information
For queries or registration, reach out to:
- Email: contact@DevOpsSchool.com
- Phone & WhatsApp (India): +91 99057 40781
- Phone & WhatsApp (USA): +1 (469) 756-6329