The MLOps Certified Professional Course by DevOpsSchool is a specialized program designed for data scientists, ML engineers, and DevOps professionals aiming to master the end-to-end operationalization of machine learning models. This in-depth training bridges the gap between model creation and enterprise-level deployment, ensuring learners gain real-world, hands-on experience in managing ML workflows efficiently.
Why MLOps Matters in Today’s AI Ecosystem
Modern organizations rely on machine learning (ML) models for automation, decision intelligence, and predictive analytics. However, moving models from development to production is often challenging due to data drift, lack of automation, and poor integration practices.
This is where MLOps (Machine Learning Operations) emerges as the key to success. It combines DevOps practices with machine learning lifecycle management, focusing on continuous integration, deployment, and monitoring of ML models. The result is faster iteration, scalability, and collaboration—allowing organizations to build robust, production-ready ML pipelines.
Comprehensive Course Overview
The MLOps Certified Professional Course equips learners with the competencies required to manage ML pipelines—from data collection and model training to monitoring and retraining. Designed by industry expert Rajesh Kumar, the program stresses practical learning through guided projects, AWS labs, and real-world scenarios.
Course Modules Covered
| Module | Key Topics | Skills Gained |
|---|---|---|
| Introduction to MLOps | Understanding lifecycle, collaboration, automation | Foundational MLOps principles |
| Linux for MLOps | File systems, commands, permissions | System-level automation |
| AWS for MLOps | EC2, S3, SageMaker, IAM, Lambda | Scalable ML deployment |
| Docker & Kubernetes | Containerization, Helm charts, scaling | Cloud-native ML workflows |
| Git & GitHub | Version control for models and data | Code and model governance |
| Terraform (IaC) | Infrastructure as Code | Automated cloud provisioning |
| Jira & Confluence | Agile workflow management | Collaborative project documentation |
| Prometheus & Grafana | Model and system monitoring | Performance visualization |
| MLflow & Kubeflow | Model tracking, versioning, serving | End-to-end ML lifecycle management |
| Flask API Development | RESTful services for ML deployment | Application integration |
| Apache Airflow | Workflow automation | Scalable data pipeline execution |
Learners also master Testing (with Pytest & scikit-learn), Model Validation, and CI/CD practices with ArgoCD, ensuring complete production-ready pipelines.
Tools & Technologies You’ll Gain Expertise In
Participants will apply concepts using some of the most in-demand tools in MLOps today:
- Containerization: Docker, Kubernetes
- Orchestration & Deployment: Helm, ArgoCD
- Monitoring: Prometheus, Grafana
- Experiment Management: MLflow, Kubeflow
- Workflow Automation: Apache Airflow
- Cloud Services: AWS EC2, S3, SageMaker, Lambda
- Infrastructure Management: Terraform, IaC principles
This toolchain mastery ensures learners can seamlessly build, deploy, monitor, and maintain machine learning models in any cloud or on-premise environment.
Hands-On Approach with Real-Time Projects
Every participant in this program applies knowledge through one real-world, scenario-based project, guided by mentors. The training simulates enterprise-level environments with live demonstrations on AWS Labs via GoToMeeting. No complex local setup is required, enabling participants to focus on applied problem-solving and automation.
You also receive:
- Lifetime LMS Access to course materials, assignments, and recordings.
- Lifetime Technical Support from DevOpsSchool’s expert community.
- Mock Interviews and Interview Preparation Kits to solidify your career readiness.
What Makes DevOpsSchool Unique
| Feature | DevOpsSchool | Other Platforms |
|---|---|---|
| Lifetime Technical Support | Included | Limited |
| Lifetime LMS Access | Yes | Often Restricted |
| Real-time Projects | Yes (AWS Lab) | Partial |
| Global Expert Trainer | Rajesh Kumar | Varies |
| Group Discounts | Available | Rare |
| Exam Preparation & Dumps | Included | Add-on Only |
DevOpsSchool stands apart as a global leader in DevOps and MLOps education, with a proven track record in corporate training and consulting. Learners gain not just certification, but a holistic transformation in technical skills, confidence, and professional readiness.
Meet Your Mentor – Rajesh Kumar
Every batch is guided by Rajesh Kumar, a globally recognized DevOps and MLOps trainer with over 20 years of experience. Rajesh’s expertise spans multiple DevOps disciplines, including DevSecOps, DataOps, AIOps, Cloud, and SRE. His teaching style integrates theory with deep hands-on exposure—ensuring every learner gains real production-level proficiency.
Flexible Learning Options
| Mode | Duration | Type |
|---|---|---|
| Self-Learning (Video-based) | ~35 hours | Flexible schedule |
| Live Online Batch | ~35 hours | Interactive sessions |
| One-to-One Training | ~35 hours | Personalized learning |
| Corporate/Enterprise Mode | 2–3 days | Online or classroom setting |
Across time zones—India, USA, Europe, and East Asia—DevOpsSchool offers accessible schedules, making learning convenient for professionals globally.
Career Outcomes and Salary Potential
According to recent statistics, the average salary of a Machine Learning Engineer in the USA ranges from $111,000 to $147,000 per year, depending on experience and proficiency in MLOps practices. Completing this certification adds significant career leverage in roles such as:
- MLOps Engineer
- Machine Learning Engineer
- Data Science DevOps Specialist
- Automation and CI/CD Engineer
- Cloud ML Architect
This program not only enhances technical capability but also equips learners to meet the growing enterprise demand for MLOps professionals.
Why Choose DevOpsSchool for MLOps Certification
- Globally recognized certification for career acceleration.
- Led by Rajesh Kumar – a top-tier trainer with 20+ years of expertise.
- Comprehensive coverage across ML, DevOps, and Cloud ecosystems.
- Hands-on training, real-time projects, and lifetime mentorship.
- Community-driven learning with access to the DevOpsSchool forum for peer support.
Get Certified in MLOps Today
Take the next step in your AI and DevOps career with the MLOps Certified Professional Program by DevOpsSchool. Whether you are an ML engineer looking to upscale or a DevOps professional entering AI integration, this course provides a complete roadmap to deploy machine learning models at scale with confidence.
Contact DevOpsSchool for Enrollment:
Email: contact@DevOpsSchool.com
Phone & WhatsApp (India): +91 7004215841
Phone & WhatsApp (USA): +1 (469) 756-6329