Certified ML Pipeline Engineer (C-MLPE) Certification Program by Tonex
Build reliable, repeatable ML pipelines that scale from prototype to production. This program equips you to design orchestration with Airflow or Prefect, run Kubernetes-native workflows in Kubeflow, and manage experiments and registries with MLflow. You will practice data versioning, feature store design, and end-to-end lineage so models remain traceable and reproducible.
The curriculum emphasizes observability, drift detection, and safe automation for retraining and rollout. You will learn CI/CD patterns specific to ML, including approvals, canaries, and rollback strategies. Security is integrated throughout: supply-chain controls, secrets hygiene, policy as code, and compliance checks are embedded in every stage.
This reduces attack surface from poisoned data, compromised artifacts, or misconfigured runtimes. The result is faster iteration with governance you can defend to auditors and stakeholders. By the end, you will be able to standardize your organization’s MLOps stack, shorten lead time for changes, and sustain model quality in production.
Learning Objectives:
- Design reproducible ML pipelines and DAGs.
- Orchestrate workflows with Airflow or Prefect.
- Build Kubeflow pipelines on Kubernetes.
- Track experiments and manage a model registry with MLflow.
- Implement data versioning and feature stores.
- Add observability, SLOs, and lineage.
- Detect data and concept drift.
- Automate retraining and controlled releases.
- Apply security, compliance, and policy as code.
- Optimize cost, reliability, and speed.
Audience:
- ML Engineers and Data Scientists
- Data Engineers and Platform Engineers
- DevOps and SRE Professionals
- Analytics and AI Team Leads
- Cybersecurity Professionals
- QA, Risk, and Compliance Managers
Program Modules:
Module 1: Pipeline Foundations & Orchestration
- DAG design, idempotency, and retries
- Scheduling, backfills, and SLAs
- Task templating and configuration-as-code
- Dependency management and artifact passing
- Error handling and alerting patterns
- Cost and resource planning
Module 2: Kubernetes-Native with Kubeflow
- Pipeline components and KFP DSL
- Containers, images, and reproducible runs
- Caching, artifacts, and metadata
- Multi-tenant isolation and quotas
- Security contexts and secrets usage
- Portability across clusters
Module 3: Experiment Tracking & Registry (MLflow)
- Runs, parameters, metrics, and artifacts
- Model registry lifecycle and stage gates
- Reproducible environments with conda/pyproject
- Comparison dashboards and best runs
- Promotion workflows and approvals
- Governance for model lineage
Module 4: Data Management & Feature Stores
- Dataset versioning with DVC/LakeFS
- Feature store architecture and ownership
- Online/offline consistency guarantees
- Point-in-time correctness and backfills
- Data quality checks and constraints
- Access control and stewardship
Module 5: Observability, Drift, and Automation
- Data and concept drift detection methods
- Performance SLOs and error budgets
- Monitoring labels, metrics, and traces
- Alert routing and on-call playbooks
- Automated retraining triggers
- Safe rollouts and rollbacks
Module 6: CI/CD, Security, and Compliance
- CI for data, models, and pipelines
- CD strategies: blue/green and canary
- Supply-chain security and artifact signing
- Secrets management and key rotation
- Policy as code and guardrails
- Regulatory readiness and audits
Exam Domains:
- Pipeline Reliability Engineering
- Data Lifecycle Governance & Auditability
- Model Risk and Change Management
- Secure Supply Chain for ML Assets
- Operational Monitoring & Incident Response
- Ethics, Compliance, and Responsible Automation
Course Delivery:
The course is delivered through lectures, interactive discussions, guided demonstrations, and project-based learning, facilitated by experts in Certified ML Pipeline Engineer (C-MLPE). Participants access online resources, including readings, case studies, and tools for practical exercises.
Assessment and Certification:
Participants are assessed through quizzes, assignments, and a capstone project. Upon successful completion, participants receive a certificate in Certified ML Pipeline Engineer (C-MLPE).
Question Types:
- Multiple Choice Questions (MCQs)
- Scenario-based Questions
Passing Criteria:
To pass the Certified ML Pipeline Engineer (C-MLPE) Certification Training exam, candidates must achieve a score of 70% or higher.
Ready to operationalize ML with confidence? Enroll now to master reproducible pipelines, governance, and secure automation. Contact Tonex to schedule your cohort or bring this program to your team.