Certified AI Deployment Engineer (C-AIDE) Certification Program by Tonex
Certified AI Deployment Engineer (C-AIDE) by Tonex prepares you to ship and operate AI models at scale across cloud, on-prem, edge, and mobile. Learn packaging with Docker and Singularity, orchestration with Kubernetes, KServe, and Seldon, and disciplined rollout strategies. Master blue/green, canary, A/B testing, and dependable rollback. Build observability and cost awareness into every release. Govern the full lifecycle with clear SLOs and ownership.
Cybersecurity impact is central. Apply SBOMs and signing to secure the supply chain. Harden containers and services, enforce zero-trust access, and protect data in transit and at rest. Detect drift and abuse patterns, preserve model integrity, and meet healthcare, finance, and defense requirements with auditable controls.
Tonex leads by focusing on mission-critical deployment and monitoring—areas often missing from generic programs. Instructors bring field experience from regulated environments. You gain repeatable playbooks, compliance-ready documentation habits, and pragmatic checklists for safe change. Deliver reliable, traceable AI services. Reduce risk. Increase uptime and trust. Accelerate value from AI investments.
Learning Objectives:
- Plan multi-environment AI deployments.
- Package and version models for reproducibility.
- Orchestrate serving with Kubernetes, KServe, and Seldon.
- Run blue/green, canary, A/B tests, and fast rollbacks.
- Monitor latency, drift, quality, and cost.
- Embed security, privacy, and compliance into pipelines.
- Optimize edge and mobile inference performance.
- Produce auditable release and operations artifacts.
Audience:
- AI/ML Engineers
- MLOps and DevOps Engineers
- Platform and Cloud Engineers
- Data Engineers
- Software Architects
- Cybersecurity Professionals
- Site Reliability Engineers (SREs)
- Product and IT Managers
Program Modules:
Module 1: Foundations of AI Deployment Architecture
- Target platforms: cloud, on-prem, edge, mobile
- Packaging models with Docker and Singularity
- CI/CD for ML with environment parity
- Reproducibility and artifact registries
- Versioning, promotion, and release gates
- Security baselines and compliance mapping
Module 2: Orchestration and Model Serving
- Kubernetes concepts for AI workloads
- KServe, Seldon Core, Triton deployment patterns
- Autoscaling, GPU scheduling, node pools
- Blue/green and canary release workflows
- Inference graphs, ensembles, and routing
- Service mesh, ingress, and TLS termination
Module 3: Data and Feature Delivery
- Streaming vs. batch feature serving
- Low-latency caches and feature stores
- Schema governance and data contracts
- Drift hooks at ingest and validation
- PII handling and end-to-end encryption
- Data freshness SLOs and alerts
Module 4: Reliability and Observability
- SLOs for latency, throughput, and cost
- Metrics, logs, and traces integration
- Model monitoring for drift and outliers
- Alert routing and on-call runbooks
- Rollback playbooks and change safety checks
- Capacity planning and load management
Module 5: Security and Compliance for AI Ops
- Supply chain security, SBOMs, and signing
- Secrets management and zero-trust policies
- Adversarial robustness checks and gating
- Tenant isolation and access control
- Incident response for model compromise
- Audit trails and regulatory evidence
Module 6: Edge and Mobile AI Operations
- Quantization, pruning, and compression
- On-device runtimes: Core ML, NNAPI, TensorRT
- Over-the-air updates and safe rollback
- Intermittent connectivity strategies
- Energy and thermal constraints
- Privacy-preserving inference patterns
Exam Domains:
- Deployment Strategy and Release Engineering
- Production Model Serving and Scaling
- Data Operations and Feature Delivery
- Reliability Engineering and Observability
- AI Security, Governance, and Compliance
- Edge and Mobile AI Operations
Course Delivery:
The course is delivered through lectures, interactive discussions, guided exercises, and project-based learning led by experts in Certified AI Deployment Engineer (C-AIDE). Participants access curated online resources, readings, case studies, and tools that support practical application and reflection.
Assessment and Certification:
Participants are assessed through quizzes, assignments, and a capstone project. Upon successful completion, participants receive the Certified AI Deployment Engineer (C-AIDE) certificate from Tonex.
Question Types:
- Multiple Choice Questions (MCQs)
- Scenario-based Questions
Passing Criteria:
To pass the Certified AI Deployment Engineer (C-AIDE) certification exam, candidates must achieve a score of 70% or higher.
Ready to operationalize AI with confidence? Enroll with Tonex today. Build resilient, secure, and auditable AI deployments that stand up in mission-critical environments.