Certified Lakehouse ML Engineer (C-LMLE) Certification Program by Tonex
The C-LMLE program prepares engineers to design, build, and govern production-grade ML on the lakehouse. You will master Databricks ML pipelines for reliable training and inference. You will learn Feathr and feature-store patterns for reuse, online/offline parity, and point-in-time correctness. Unity Catalog (UC) governance is emphasized to secure data, code, and models at scale.
Expect practical strategies for orchestration, quality gates, lineage, and cost control. The program connects architecture with disciplined operations so teams can iterate quickly without sacrificing compliance. Cybersecurity is central: UC policies, tags, and audits reduce data leakage and privilege creep. Robust lineage, approval workflows, and policy-as-code help mitigate data poisoning and unauthorized access. Graduates translate business goals into governed ML services that are observable, reproducible, and resilient across clouds. The result is trustworthy outcomes, faster releases, and lower risk.
Learning Objectives:
- Design lakehouse architectures for ML workloads
- Orchestrate Databricks ML pipelines end to end
- Implement feature stores with Feathr/Databricks Feature Store
- Enforce UC governance, lineage, and access controls
- Build CI/CD, testing, and observability for models and data
- Manage cost, performance, and reliability at scale
- Apply policy-as-code and audit for compliance
- Mitigate data leakage and adversarial risks
Audience:
- ML Engineers and Data Engineers
- Data Architects and Platform Owners
- MLOps/DevOps Engineers
- Analytics and AI Team Leads
- Compliance and Data Governance Officers
- Cybersecurity Professionals
- Product and Technical Program Managers
- Solutions Architects and Consultants
Program Modules:
Module 1: Lakehouse ML Foundations
- Medallion design and Delta optimization
- Batch/stream ingestion and CDC patterns
- Data quality rules and expectations
- Reproducibility, environments, and packaging
- Performance tuning and workload planning
- Reliability and cost governance
Module 2: Databricks ML Pipelines
- Workflow orchestration and scheduling
- Training, evaluation, and registry promotion
- Inference pipelines and blue/green releases
- Data and model validation gates
- CI/CD with branching and approvals
- Rollback, recovery, and runbook design
Module 3: Feature Store with Feathr
- Feature definition, entities, and registries
- Offline/online stores and serving flows
- Point-in-time joins and leakage prevention
- Backfills, versioning, and reproducibility
- Discovery, reuse, and ownership models
- Monitoring drift and freshness SLAs
Module 4: Unity Catalog Governance
- Catalogs, schemas, and permissions
- Lineage for data, code, and models
- Row/column policies and tags
- Sensitive data classification and masking
- Audit trails and access reviews
- Cross-workspace sharing and controls
Module 5: MLOps, Quality, and Observability
- Telemetry, metrics, and alerting
- Data contracts and schema evolution
- Model drift, bias checks, and thresholds
- Incident handling and postmortems
- Capacity planning and FinOps practices
- SLA/SLO design and reporting
Module 6: Security, Risk, and Compliance
- Threat modeling for data and models
- Encryption, key management, and secrets
- Policy-as-code and automated enforcement
- Third-party/package supply chain trust
- Regulatory alignment and evidence capture
- Access review cadence and segregation of duties
Exam Domains:
- Data Governance and Access Control
- Feature Engineering Strategy and Management
- Pipeline Reliability and Observability
- Model Lifecycle and Risk Management
- Cost Optimization and FinOps for ML
- Cross-Cloud Interoperability and Data Sharing
Course Delivery:
The course is delivered through lectures, interactive discussions, guided demos, and project-based learning, facilitated by experts in the field of Certified Lakehouse ML Engineer (C-LMLE). Participants will have access to online resources, including readings, case studies, and tools for structured exercises.
Assessment and Certification:
Participants will be assessed through quizzes, assignments, and a capstone project. Upon successful completion of the course, participants will receive a certificate in Certified Lakehouse ML Engineer (C-LMLE).
Question Types:
- Multiple Choice Questions (MCQs)
- Scenario-based Questions
Passing Criteria:
To pass the Certified Lakehouse ML Engineer (C-LMLE) Certification Training exam, candidates must achieve a score of 70% or higher.
Ready to build governed, production-grade lakehouse ML? Enroll now, secure your data, and accelerate delivery. Earn the C-LMLE credential with Tonex and lead with confidence.