Certified MLOps & Production ML Engineer (C-MLOpsP)

Certified MLOps & Production ML Engineer (C-MLOpsP) Certification Program by Tonex

Build production-grade ML and LLM systems with confidence. This program dives into CI/CD for models, robust data/version control, and cloud-native deployment on AWS, GCP, and Azure. You will design resilient pipelines, automate testing, and ship with traceability. Learn how to monitor models in real time, detect drift, and respond quickly.

Master Kubernetes, Kubeflow, MLflow, and Airflow to scale reliably across hybrid and on-prem environments. Cybersecurity is integral throughout. You will secure the ML supply chain, protect secrets and data, and implement zero-trust controls. Governance, auditability, and compliance are emphasized to reduce model risk and prevent adversarial or data poisoning threats. Graduate ready to operate safe, scalable, and compliant ML platforms.

Learning Objectives:

• Build CI/CD pipelines for ML/LLM workloads.
• Implement data versioning, lineage, and reproducibility.
• Monitor models, detect drift, and manage rollbacks.
• Orchestrate pipelines with Airflow, Kubeflow, and MLflow.
• Deploy on Kubernetes across cloud, hybrid, and on-prem.
• Apply security, governance, and compliance to MLOps.

Audience:

• ML/AI Engineers and Data Scientists
• Data/Platform Engineers
• DevOps and SRE Professionals
• Software and Cloud Architects
• Product/Engineering Managers
• Cybersecurity Professionals

Program Modules:

Module 1: CI/CD for ML and LLM

• Pipeline design and repository strategy
• GitOps and infrastructure as code
• Model packaging and artifact promotion
• Automated testing for data, model, and infra
• Continuous training and evaluation gates
• Progressive delivery with canary and blue-green

Module 2: Data Management and Versioning

• Data contracts, schemas, and validation
• DVC/LakeFS workflows and checkpoints
• Feature stores lifecycle and governance
• Experiment tracking and metadata
• Reproducibility and lineage across stages
• PII handling, masking, and retention

Module 3: Monitoring, Drift, and Reliability

• Online/offline metrics and SLIs/SLOs
• Data and concept drift detection patterns
• Performance regression and alert design
• Bias, fairness, and safety checks
• Incident response and playbooks
• Rollback, shadow, and replay testing

Module 4: Orchestration and Pipelines

• Airflow DAGs for ETL and training
• Kubeflow Pipelines design patterns
• MLflow tracking and model registry
• Event-driven orchestration with queues
• Caching, artifact stores, and wheels
• Dependency and environment management

Module 5: Deployment Architectures

• Cloud-native on AWS, GCP, and Azure
• Hybrid and on-prem reference patterns
• Kubernetes operators, HPA, and autoscaling
• Real-time, batch, and streaming serving
• Multi-model routing and A/B strategies
• Cost-aware placement and right-sizing

Module 6: Security, Governance, and Compliance

• Supply chain security, SBOM, and signing
• Secrets management and key protection
• Network policies and zero-trust controls
• Audit trails and model provenance
• Policy enforcement and approvals
• Regulatory alignment and risk management

Exam Domains:

1. MLOps Governance and Lifecycle Management
2. Data Integrity, Lineage, and Feature Management
3. Secure ML Supply Chain and DevSecOps
4. Production Observability and Incident Response
5. Cloud-Native and Hybrid Serving Architectures
6. Cost, Performance, and Reliability Engineering

Course Delivery:
The course is delivered through lectures, interactive discussions, guided exercises, and project-based learning led by experts in Certified MLOps & Production ML Engineer (C-MLOpsP). Participants gain access to online resources, readings, case studies, and tools for practical exercises.

Assessment and Certification:
Participants are assessed via quizzes, assignments, and a capstone project. Upon successful completion, participants receive the Certified MLOps & Production ML Engineer (C-MLOpsP) certificate.

Question Types:

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

Passing Criteria:
To pass the Certified MLOps & Production ML Engineer (C-MLOpsP) Certification exam, candidates must achieve a score of 70% or higher.

Ready to level up your production ML? Enroll with Tonex now. Bring your team, or request a tailored private cohort. Let’s build secure, scalable MLOps together.