Certified Advanced MLOps Engineer (C-MLOpsE)

Certified Advanced MLOps Engineer (C-MLOpsE) Certification Program by Tonex

Modern AI succeeds when models ship reliably, adapt fast, and stay secure. This program prepares you to build and operate production-grade ML systems at scale. You will design resilient pipelines, automate CI/CD, and harden runtime operations. Learn how to track performance, detect drift, and respond to incidents with clear SLOs. Master versioning, lineage, and governance to keep changes auditable and safe.

Cybersecurity is woven throughout. You will secure the ML supply chain, protect data, and enforce access controls. You will implement model signing, SBOM, and monitoring that catches threats early. The result is trustworthy ML that meets compliance goals and business objectives. Graduates lead platforms that are fast, cost-aware, and dependable.

Learning Objectives:

• Build resilient, versioned ML pipelines.
• Implement CI/CD and continuous training.
• Monitor models, data, and systems with SLOs.
• Detect and remediate drift safely.
• Secure the model supply chain and data.
• Govern ML with auditability and compliance.

Audience:

• MLOps and ML Engineers
• Data Engineers and Scientists
• DevOps and SRE Professionals
• Software Architects and Tech Leads
• Product and Platform Owners
• Cybersecurity Professionals

Program Modules:

Module 1: Production ML Pipelines & DataOps

• Design end-to-end pipelines.
• Manage feature stores.
• Orchestrate workflows.
• Enforce data contracts.
• Track lineage and versions.
• Ensure idempotent runs.

Module 2: CI/CD for ML Systems

• Adopt GitOps workflows.
• Test data and models.
• Package and containerize.
• Enable continuous training.
• Automate safe deployments.
• Plan rollbacks and gates.

Module 3: Monitoring & Observability

• Unify metrics, logs, traces.
• Validate data quality.
• Track model performance.
• Define SLOs and budgets.
• Create incident runbooks.
• Triage alerts effectively.

Module 4: Drift, Robustness & Reliability

• Detect data and label drift.
• Manage concept drift.
• Run A/B and shadow tests.
• Spot outliers and attacks.
• Design graceful degradation.
• Stress and resilience test.

Module 5: Security, Governance & Compliance

• Secure supply chain and SBOM.
• Manage secrets and access.
• Protect privacy and PII.
• Maintain audit trails.
• Enforce fairness controls.
• Align with regulations.

Module 6: Platform Operations & Cost

• Use Infrastructure as Code.
• Apply container/serverless patterns.
• Tune performance at scale.
• Implement FinOps controls.
• Support multi-cloud and edge.
• Improve team workflows.

Exam Domains:

1. ML Platform Architecture & Reliability
2. Data Lifecycle Governance & Quality Assurance
3. Secure Model Supply Chain & Compliance Management
4. Production Monitoring, SLOs & Incident Response
5. Performance Engineering, Scalability & Cost Optimization
6. Organizational Readiness, Risk & Change Leadership

Course Delivery:
The course is delivered through lectures, interactive discussions, case studies, and guided exercises led by experts in Certified Advanced MLOps Engineer (C-MLOpsE). Participants gain access to curated online resources, readings, and tool walkthroughs for practical reinforcement.

Assessment and Certification:
Participants are assessed through quizzes, assignments, and a capstone project. Upon successful completion, participants receive a certificate in Certified Advanced MLOps Engineer (C-MLOpsE).

Question Types:

• Multiple Choice Questions (MCQs)
• Scenario-based Questions

Passing Criteria:
To pass the Certified Advanced MLOps Engineer (C-MLOpsE) Certification Training exam, candidates must achieve a score of 70% or higher.

Ready to operationalize AI with confidence? Enroll now and build secure, scalable, and observable ML systems. Lead your team to production excellence with Tonex.