Certified Enterprise AI Cloud Engineer (C-EAICE) Certification Program by Tonex
Enterprises are accelerating AI adoption across AWS, Azure, and Google Cloud, yet production success demands more than model accuracy. The Certified Enterprise AI Cloud Engineer (C-EAICE) program equips professionals to design, deploy, and operate reliable AI/ML systems with security, governance, and cost discipline baked in. You will learn multi-cloud patterns for data pipelines, training, inference, and MLOps; how to choose the right managed services; and how to build compliant architectures that scale.
The curriculum emphasizes reproducibility, observability, and automation so teams can move from prototype to resilient, audited workloads. Cybersecurity is addressed throughout: identity-first design, secret management, encryption, network controls, model and data risk, and secure supply chains. Participants practice mapping enterprise requirements to cloud guardrails, implementing least-privilege access, and hardening endpoints that serve models to internal users and external clients.
We also cover incident readiness, lineage, and responsible AI controls to reduce operational and reputational risk. By graduation, you will be able to orchestrate end-to-end AI services on AWS, Azure, and GCP, integrate CI/CD for ML, and measure business value. The result is practical, vendor-aware competence that helps organizations modernize faster while staying safe, compliant, and cost-effective. Content maps to enterprise policies and real cloud services used daily globally.
Learning Objectives:
- Design multi-cloud AI architectures aligned to enterprise controls
- Build portable data and feature pipelines across AWS, Azure, and GCP
- Deploy training and inference with reliable, scalable patterns
- Implement MLOps with CI/CD, testing, and automated rollbacks
- Secure identities, secrets, data, and endpoints end-to-end
- Instrument models for observability, drift, and performance SLOs
- Optimize cost, capacity, and sustainability for AI workloads
- Map compliance and responsible AI requirements to cloud services
Audience:
- Cloud and AI/ML Engineers
- Data Engineers and Platform Engineers
- MLOps Engineers and SRE/DevOps
- Solutions and Enterprise Architects
- Cybersecurity Professionals
- IT Auditors, Risk and Compliance Leads
Program Modules:
Module 1: Multi-Cloud AI Architecture Foundations
- Reference architectures for AWS, Azure, and GCP
- Landing zones, networks, and private connectivity
- Identity, roles, and cross-account access patterns
- Storage tiers for datasets, models, and artifacts
- Service selection and portability trade-offs
- High availability, DR, and regional strategy
Module 2: Data Engineering for ML in the Cloud
- Batch and streaming ingestion patterns
- Data lakes, warehouses, and lakehouse choices
- Feature stores and offline/online consistency
- Data quality, lineage, and governance catalogs
- ETL/ELT orchestration and scheduling
- Secure sharing and cross-cloud data movement
Module 3: Training and Tuning at Scale
- Managed training on SageMaker, Vertex AI, Azure ML
- Distributed training and accelerator choices
- Hyperparameter tuning and experiment tracking
- Dataset versioning and reproducibility controls
- Bias checks and evaluation methodologies
- Packaging, registry, and model artifact management
Module 4: MLOps and CI/CD for Models
- Reproducible environments and dependency control
- Build, test, and deploy pipelines for ML services
- Canary, blue-green, and shadow deployments
- Model registry, approvals, and promotions
- Inference graph and microservice patterns
- Rollback, feature flags, and release governance
Module 5: Security, Governance, and Compliance for AI
- Least-privilege access and secret management
- Encryption in transit and at rest
- Network segmentation and private endpoints
- Data residency, retention, and sovereignty
- Responsible AI policies and guardrails
- Audit readiness and evidence automation
Module 6: Operations, Reliability, and Cost Management
- Telemetry, tracing, and model-aware observability
- Drift, data quality, and anomaly detection
- Capacity planning and autoscaling strategies
- SLOs, error budgets, and incident workflows
- FinOps for training and inference spend
- Business value tracking and ROI reporting
Exam Domains:
- Enterprise AI Cloud Strategy and Readiness
- Secure Data Lifecycle for AI Systems
- Model Lifecycle Engineering and Automation
- Platform Reliability, Observability, and SRE for AI
- Compliance, Risk, and Responsible AI Governance
- FinOps and Cost Optimization for AI Platforms
Course Delivery:
The course is delivered through lectures, interactive discussions, expert-guided workshops, case studies, and project-based learning tailored to C-EAICE. Participants receive curated online resources, readings, reference architectures, and provider documentation to support practical exercises and team activities.
Assessment and Certification:
Participants are assessed through quizzes, assignments, and a capstone project. Upon successful completion, participants receive the Certified Enterprise AI Cloud Engineer (C-EAICE) certificate.
Question Types:
- Multiple Choice Questions (MCQs)
- Scenario-based Questions
Passing Criteria:
To pass the C-EAICE Certification Training exam, candidates must achieve a score of 70% or higher.
Ready to deploy enterprise-grade AI across AWS, Azure, and GCP? Enroll in C-EAICE today and accelerate secure, reliable, and cost-effective AI delivery for your organization.