In 2025, managing and deploying machine learning models efficiently is no longer a luxury—it’s a necessity. As organizations rely increasingly on ML-driven decision systems, MLOps (Machine Learning Operations) has emerged as the backbone of scalable AI implementation. It bridges the gap between data science and IT operations, enabling teams to automate, monitor, and enhance machine learning deployment pipelines at scale.
If you’re aiming to accelerate your career at the intersection of AI, automation, and cloud engineering, the MLOps Certified Professional Course by DevOpsSchool is the industry-recognized path to mastery.
Mentored by Rajesh Kumar—one of the most respected leaders in DevOps, MLOps, and Cloud Engineering—this comprehensive course turns aspiring data engineers and developers into deployment experts who can take ML models from prototype to production.
What is MLOps and Why It Matters
MLOps integrates Machine Learning (ML) and DevOps (Development + Operations) methodologies to streamline the lifecycle of ML models—from data preparation and model training to deployment, versioning, and monitoring in production.
In modern data-driven enterprises, data scientists often face challenges like inconsistent deployment environments, unreliable pipelines, and model drift. MLOps solves this by providing:
- Automation in model training, testing, and deployment.
- Continuous integration (CI) and continuous delivery (CD) for ML pipelines.
- Governance, reproducibility, and traceability for data and models.
- Monitoring for accuracy, drift, and performance in real time.
- Collaboration between data scientists, DevOps engineers, and product teams.
As noted in DevOpsSchool’s MLOps Foundation insights, this discipline ensures that machine learning systems in production remain reliable, scalable, and adaptive.
Why Choose DevOpsSchool’s MLOps Certified Professional Program
While numerous platforms offer MLOps training, DevOpsSchool stands apart by delivering end-to-end project-based learning, expert mentorship, and enterprise-grade infrastructure practice that prepares professionals for real-world scenarios.
Key Highlights
- 35+ hours of intensive, interactive live online training.
- Hands-on labs with cloud-based execution (AWS & Kubernetes-driven).
- Lifetime access to LMS recordings, course notes, and materials.
- Real-time projects covering model deployment, version control, CI/CD, and monitoring.
- Mock interviews and career guidance to ensure job readiness.
- Globally flexible batch timings suitable across IST, PST, EST, CET, and JST zones.
- Mentorship by Rajesh Kumar, who personally guides students through advanced real-world MLOps use cases.
Curriculum Overview: From Foundations to Automation
The MLOps Certified Professional curriculum blends theory with implementation to ensure mastery of the complete ML lifecycle.
| Module | Key Topics | Tools Covered | Learning Outcomes |
|---|---|---|---|
| MLOps Lifecycle & Best Practices | Model versioning, data pipelines, automation principles | Git, Jenkins | Understand end-to-end MLOps pipelines |
| Cloud & Infrastructure Setup | AWS setup, VM configuration, serverless deployment | AWS EC2, S3, Lambda | Build and deploy models in scalable environments |
| Containerization & Orchestration | Docker, Kubernetes, Helm, Terraform integration | Docker, K8s, Helm | Automate model lifecycle management |
| Continuous Integration & Deployment | CI/CD pipelines for ML | ArgoCD, GitHub Actions, Jenkins | Automate deployment workflows |
| Monitoring & Governance | Log analytics, drift detection, security management | Prometheus, Grafana | Monitor performance and ensure compliance |
| Experiment Tracking & Versioning | MLflow, DVC, Model Registry | MLflow, DVC | Manage experiments and model iterations |
| Advanced Topics | AI observability, ethical AI, reproducibility | Kubeflow, Airflow | Implement scalable MLOps in production |
Through this personalized learning journey, participants create, package, and deploy ML models using Docker and Kubernetes while maintaining CI/CD automation best practices.
Tools You’ll Master
Students gain expertise across a wide range of industry-standard tools:
- CI/CD and Pipeline Tools: Jenkins, ArgoCD, MLflow, Airflow, Terraform
- Containerization & Orchestration: Docker, Kubernetes, Helm, Kubeflow
- Monitoring & Visualization: Prometheus, Grafana
- Version Control & Documentation: Git, GitHub, Confluence, Jira
- Cloud Platforms: AWS EC2, S3, Lambda, SageMaker
- Frameworks & Languages: TensorFlow, PyTorch, Flask, Python
This ensures graduates leave not only with familiarity but hands-on mastery—a critical distinction when employers assess certification programs.
Real-World Skills You’ll Gain
By the end of this course, you’ll be able to:
- Build end-to-end MLOps pipelines integrating model training, versioning, and CI/CD.
- Automate data preprocessing, training, and testing tasks.
- Deploy containerized models using Docker and Kubernetes.
- Set up real-time monitoring dashboards for model performance drift.
- Utilize MLflow and Airflow for experiment tracking and workflow automation.
- Implement DevSecOps practices to ensure data security and compliance in ML pipelines.
- Manage ML lifecycles using IaC (Infrastructure-as-Code) tools like Terraform.
What Sets DevOpsSchool Apart
| Feature | DevOpsSchool | Typical Training Platforms |
|---|---|---|
| Instructor-Led Learning | Live interaction with certified experts | Pre-recorded sessions |
| Hands-On Projects | Yes—cloud-native real-world projects | Sometimes limited |
| Lifetime LMS Access | Included | Often time-restricted |
| Career Guidance | Personalized mock interviews & resume help | Minimal |
| 24×7 Technical Support | Lifetime via forums & community | Limited duration |
| Expert Mentor | Rajesh Kumar | Generic trainers |
With a deep emphasis on practical learning and technical mentorship, DevOpsSchool has become the go-to portal for MLOps mastery worldwide.
Certification & Career Outcomes
Upon completion, you’ll receive the DevOpsSchool Certified MLOps Professional certification, accredited by DevOpsCertification.co. This credential validates your ability to manage full-scale ML pipelines, making you job-ready for roles such as:
- MLOps Engineer
- Machine Learning Engineer
- Data Engineer
- Platform Engineer (AI/ML)
- DevSecOps Automation Specialist
Average salaries for MLOps engineers globally range between $110,000–$145,000 per year, depending on experience and geographic region.
Graduates also gain access to lifelong career forums, job postings, and technical communities supporting continuous learning and growth.
Group Discounts & Flexible Batches
DevOpsSchool offers multiple formats for professionals worldwide:
| Format | Duration | Mode | Availability |
|---|---|---|---|
| Self-Learning | 35 hours | Recorded Video | Anytime |
| Live Online Sessions | 35 hours | Instructor-led | Weekly Batches |
| One-on-One Coaching | Custom | Virtual | Flexible |
| Corporate Training | 2–3 Days | Online or On-site | On Demand |
Guided by Thought Leader Rajesh Kumar
The course is authored and led by Rajesh Kumar, an internationally recognized mentor in DevOps, MLOps, SRE, Cloud, and Kubernetes. With over 20 years of experience, Rajesh has trained professionals at top tech companies, enabling thousands to achieve global certifications and career transformations.
Under his mentorship, the MLOps Certified Professional Program continuously evolves to match industry trends, ensuring learners stay ahead of the curve.
Why Invest in MLOps Certification Now
In 2025, MLOps is more than a technical skill—it’s an operational necessity.
- AI adoption has doubled across industries, but 60% of ML models never reach production due to operational complexity.
- Certified MLOps professionals bridge that gap, commanding premium roles in DataOps, AIOps, and CloudOps ecosystems.
- With its unique blend of theory and practice, DevOpsSchool’s certification positions you as a strategic asset to employers—from startups innovating with AI to global enterprises deploying intelligent systems at scale.
Start Your MLOps Journey Today
If your goal is to lead AI-driven projects and optimize ML deployment pipelines, the MLOps Certified Professional Course is your ideal pathway.
Join thousands of global professionals honing future-ready AI and DevOps expertise with DevOpsSchool.
Contact DevOpsSchool
- Email: contact@DevOpsSchool.com
- Phone (India): +91 99057 40781
- Phone (USA): +1 (469) 756-6329
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