Certified Edge AI & TinyML Developer (C-EdgeML) Certification Program by Tonex
Edge AI is reshaping how intelligence runs on devices with tight compute, memory, and power budgets. This program builds practical competence in TinyML, quantization, and model compression for embedded targets. You will learn to design, optimize, and deploy models on MCUs, NPUs, and heterogeneous SoCs with predictable latency and power use. We cover dataflow, toolchains, and integration patterns that keep footprints small and performance high.
The curriculum also emphasizes cybersecurity. You will harden models and firmware against tampering, protect model IP, and reduce privacy risk at the edge. Adversarial robustness and secure update hygiene are addressed throughout. Graduates leave with a blueprint for reliable, secure, and efficient on-device ML deployments in real products. The program fits engineers who want disciplined methods, measurable trade-offs, and strong governance around edge intelligence.
Learning Objectives:
- Design TinyML models that fit strict memory and power budgets
- Apply quantization, pruning, and distillation effectively
- Build edge inference pipelines with deterministic latency
- Use embedded toolchains and accelerators for deployment
- Implement telemetry, testing, and CI/CD for edge ML
- Secure models, firmware, and data paths end to end
Audience:
- Embedded and firmware engineers
- Data scientists and ML engineers
- Edge and IoT developers
- Systems and platform engineers
- Product and solutions architects
- Cybersecurity Professionals
Program Modules:
Module 1: Edge AI Foundations
- Edge vs. cloud inference trade-offs
- Real-time constraints and latency budgets
- Power, memory, and compute profiling
- Accelerator basics: DSP, GPU, NPU
- Dataflow and streaming on devices
- Performance KPIs and benchmarking
Module 2: TinyML Modeling
- MCU-friendly architectures and ops
- Quantization-aware training patterns
- Pruning and knowledge distillation
- Dataset curation under constraints
- Accuracy vs. footprint trade studies
- Validation on representative targets
Module 3: Compression & Quantization
- PTQ vs. QAT decision framework
- INT8/UINT8/FP8 formats and ranges
- Per-channel vs. per-tensor scaling
- Sparsity, clustering, and entropy coding
- Error analysis and mitigation tactics
- Latency, energy, and accuracy evaluation
Module 4: Embedded Systems & Toolchains
- MCU vs. MPU, RTOS vs. bare-metal
- Memory maps, stacks, and buffers
- HAL, drivers, and peripheral I/O
- TFLM, CMSIS-NN, uTVM fundamentals
- Build, debug, and profiling flows
- Packaging artifacts for targets
Module 5: Deployment & Edge MLOps
- Model packaging and versioning
- OTA updates and rollback safety
- A/B canaries on constrained fleets
- Telemetry, drift, and health signals
- Contract tests and HIL strategies
- CI/CD for firmware and models
Module 6: Security & Safety for Edge AI
- Secure boot and trust anchors
- Model IP protection and obfuscation
- Adversarial and fault injection defenses
- Privacy by design and data minimization
- Threat modeling for edge pipelines
- Standards, safety, and compliance checks
Exam Domains:
- Edge Data Engineering & Pipelines
- TinyML Architecture & Optimization
- Embedded Runtime & Acceleration
- Secure Model Delivery & Protection
- Reliability, Safety & Compliance for Edge AI
- Performance, Power & Lifecycle Management
Course Delivery:
The course is delivered through lectures, interactive discussions, expert demos, and guided exercises led by Tonex instructors. Participants gain access to curated online resources for readings, case studies, templates, and tools that support practice and review for Certified Edge AI & TinyML Developer (C-EdgeML).
Assessment and Certification:
Participants are assessed through quizzes, assignments, and a capstone project. Upon successful completion, participants receive a certificate in Certified Edge AI & TinyML Developer (C-EdgeML).
Question Types:
- Multiple Choice Questions (MCQs)
- Scenario-based Questions
Passing Criteria:
To pass the Certified Edge AI & TinyML Developer (C-EdgeML) Certification Training exam, candidates must achieve a score of 70% or higher.
Ready to build efficient, secure on-device intelligence? Enroll with Tonex and accelerate your edge roadmap. Speak with our team to align this program to your products and timelines.