Edge AI and TinyML for Devices Corporate Training
Learn to run machine learning on-device: train, optimize, and deploy tiny models to real hardware.
Build the on-device machine learning skills your team needs to ship intelligent products. Edstellar's Edge AI & TinyML for Devices corporate training program helps your embedded and ML engineers master model optimization, quantization, and edge deployment through instructor-led, hands-on sessions with real device hardware. As the corporate training company enterprises trust for emerging-technology programs, we tailor every workshop to your hardware, tools, and goals.
Instructor-led group trainingVirtual / on-site / off-site10 languagesDelivered inEnglish, Spanish, Mandarin, German, Arabic, Portuguese, Hindi, French, Japanese, Italian100+ countriesWe have delivered inUnited States, United Kingdom, India, Canada, Germany, France, Netherlands, UAE, Saudi Arabia, Singapore, Australia, Japan, Brazil, South Africa and 90+ more countries
How we build edge-AI-ready engineering teams
THE EDSTELLAR EDGE AI & TINYML CAPABILITY MODEL
Map the skill gaps
Stellar AI and the Skills Matrix reveal exactly where your team's edge AI and embedded ML skills stand today.
Set one on-device standard
One shared workflow for model training, optimization, and edge deployment across your engineers.
Practice on real hardware
Guided hands-on labs where your team optimizes and deploys TinyML models to actual devices.
Prove the capability lift
Before-and-after lab benchmarks measure each engineer's edge AI readiness.
Scale across products
One on-device ML practice rolls out across teams and device lines as your product portfolio grows.
Need more than one program?Get a group quote
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Program Overview: What is Edge AI and TinyML for Devices Corporate Training?
Edge AI & TinyML for Devices trains teams to build machine learning models that run on microcontrollers and tiny devices. It is built for embedded engineers, ML practitioners, and product teams shipping battery-powered and resource-constrained products. The program covers the on-device ML landscape, feature engineering for tiny devices, model training and optimization, microcontroller deployment, edge MLOps, and sensor fusion.
Training runs over 20 hours in hands-on lab sessions with real hardware. Teams collect sensor data, engineer features, train and quantize models, and deploy them to microcontrollers in guided labs. Instructors with shipped TinyML experience review every model, checking accuracy against memory and power budgets. Practice builds through deployment scenarios where teams debug on-device failures and push over-the-air updates. The program closes with a three-hour capstone where teams ship a complete on-device AI product.
TinyML projects die in the gap between a working notebook and a working device: models too large for flash, features that drain batteries, and no plan for updates. Hardware constraints punish every shortcut. This program teaches teams to design within the envelope from day one, so models fit, run, and stay maintainable on real devices.
The capstone is a shipped on-device AI product: teams deliver a trained, quantized model running on a microcontroller with a sensor pipeline and an update story, demonstrated live. Your engineers leave with a TinyML workflow they can repeat for every device in the roadmap.
Across seven modules, your engineers move from edge AI foundations to a working on-device AI product. Sessions use real-world use cases, hands-on labs, and guided deployment exercises, so every technique transfers directly to your device roadmap.
Delivered as a hybrid instructor-led program, it fits around your calendar, with scheduling set along with you, and content customized to your hardware and tools.
What's Included in this Workshop for Your Enterprise Teams
- 1
Edge AI foundations
Understand the on-device ML landscape, hardware options, and where edge inference pays off.
- 2
Data for tiny devices
Prepare sensor data and features for models that must run in kilobytes of memory.
- 3
Model optimization
Shrink models with quantization, pruning, and distillation without losing accuracy.
- 4
Deploy on hardware
Run TFLite Micro models on microcontrollers and profile latency and power.
- 5
Capstone product build
Ship a complete on-device AI product and demo it to stakeholders.
- Training delivered at any of your offices globally
- 10+ languages
- Schedule around your calendar
- Group size set along with you
- Run as one cohort or split into batches
- Instructor-led (virtual/onsite/hybrid)
- Recurring and annual options
- Trainer sourcing, matching and logistics
Build your team's skills. Anywhere in the world.
Real-world use cases
from day one
Instructor-led training
for your teams
Delivered globally
in 100+ countries
Enterprise Training Solutions Customized for Your Company
One partner for instructor-led corporate training, delivered the way your teams work.
Request a Training Quote- Customized Training Delivery
- Scale Your Training: Small to Large Teams
- In-person Onsite, Live Virtual or Hybrid Training Modes
- Plan from 2000+ Industry-ready Training Programs
- Hands-On Labs on Live Infrastructure, Led by Industry Experts
Edge AI and TinyML for Devices Course Topics and Program Outline
Seven modules follow a clear learning path from edge AI foundations to a shipped on-device product. Each session builds on the last, with hands-on labs that turn TinyML concepts into working models on real hardware.
Built by Edstellar with edge AI and TinyML engineers
Built by engineers who deploy AI models on edge devices and microcontrollers in production, shaped around your stack and use cases.
Baseline each participant's Edge AI and TinyML readiness before module 01 begins
Build strong Edge AI and TinyML foundations in instructor-led sessions for your team
Reinforce skills through guided hands-on labs on MCU deployment and model optimization
Apply Edge AI and TinyML skills to real-world scenarios and a capstone deployment project
A short pre-training assessment sets each participant's entry level, so the program starts where your team actually is. The rest is weighted toward hands-on practice: model quantization, on-device inference labs, and realistic scenarios drawn from your team's work, not slides.
Delivered as
Entry level set by the assessment. Depth and schedule customized to your team.
Set your baseline: map the edge AI use cases your products face and benchmark your team's current embedded ML fluency.
learna)Why inference at the edge matters
b)Cloud vs edge vs on-device trade-offs
c)TinyML hardware landscape: MCUs, NPUs, DSPs
d)Power, memory, and latency budgets
e)Real-world edge AI use cases
Collect sensor readings and build feature pipelines tuned for tiny models, using real device data in guided exercises.
practicea)Collecting data from sensors and peripherals
b)Feature engineering for constrained models
c)Time-series and signal preprocessing
d)Handling noisy, limited datasets
e)Data augmentation for edge workloads
Apply quantization, pruning, and distillation to a trained model and measure the accuracy and size trade-offs.
practicea)Choosing architectures for TinyML
b)Quantization: post-training and quantization-aware
c)Pruning and weight sharing
d)Knowledge distillation
e)Tools covered: TensorFlow Lite, ONNX Runtime, TFLite Micro
Deploy an optimized model to a microcontroller, then profile latency, memory, and power on the actual hardware.
practicea)TensorFlow Lite for Microcontrollers
b)Memory management and arena sizing
c)On-device inference pipelines
d)Debugging models on hardware
e)Measuring latency and power
Design an OTA update and monitoring plan for a fleet of edge devices, covering drift detection and model security.
learna)OTA model updates and versioning
b)On-device monitoring and drift detection
c)Edge security: attestation and encrypted models
d)Privacy-first design
e)Fleet management at scale
Build keyword-spotting and anomaly-detection pipelines with multi-sensor fusion under real-time constraints.
practicea)Multi-sensor data fusion
b)Audio pipelines: keyword spotting
c)Vision: person detection on-device
d)Anomaly detection for predictive maintenance
e)Real-time constraints and scheduling
Define, build, optimize, and deploy a complete on-device AI product, then present it to the cohort for review.
applya)Define the device use case
b)Build, optimize, and deploy the model
c)Profile power, latency, and memory
d)Stakeholder demo and handoff
e)Capstone review and next steps
Customize this program tailored to your team?
Tell us your training needs: what your team already works with, the level they’re at, and the topics you want them to focus on. We’ll adjust the modules, duration and hands-on labs, and send you a customized outline for your group.
Key Skills and Outcomes Your Team Will Gain
Your engineers will leave able to train, optimize, and deploy machine learning models on real device hardware. Each outcome is practiced in hands-on labs and proven in the capstone.
Choose the right edge AI hardware
Match MCUs, NPUs, and DSPs to your use case, power budget, and latency targets.
Prepare data for tiny models
Engineer sensor features and preprocess signals for models that run in kilobytes of memory.
Optimize models for the edge
Apply quantization, pruning, and knowledge distillation to shrink models without losing accuracy.
Deploy on microcontrollers
Ship TFLite Micro models to real hardware and manage memory, arenas, and inference pipelines.
Profile latency and power
Measure on-device performance and tune models to meet strict power and timing budgets.
Run edge MLOps in production
Deliver OTA model updates, monitor on-device drift, and secure models across a device fleet.
Ship on-device AI products
Scope, build, and demo a complete TinyML product that works without the cloud.
Teams that Benefit from this Edge AI and TinyML for Devices Group Training
This program is built for embedded and ML engineers shipping intelligent devices, from firmware developers to data scientists. No prior TinyML experience is needed; content adapts to each cohort's hardware and experience, so every engineer practices the work their role actually owns.
Ship intelligence on your own hardware.
Firmware and embedded engineers master TinyML deployment, memory management, and on-device profiling, so ML models run reliably on their microcontrollers.
Workflow friction
- ML models that never fit the memory budget
- Latency and power targets that keep slipping
- No repeatable way to update models in the field
How the program helps
- Deploy models that fit your hardware
- Hit latency and power budgets with confidence
- Ship OTA updates across your device fleet
Practice use cases
- Model deployment
- Hardware profiling
- Fleet updates
Take your models beyond the cloud.
ML engineers and data scientists learn quantization, pruning, and edge deployment, so their models run on-device instead of depending on cloud connectivity.
Workflow friction
- Models too large for edge hardware
- Accuracy lost during optimization
- No experience with embedded constraints
How the program helps
- Shrink models without losing accuracy
- Design for memory and power from day one
- Deploy with TFLite Micro and ONNX tooling
Practice use cases
- Model optimization
- Accuracy benchmarking
- Edge evaluation
Build devices that think locally.
IoT architects and product engineers connect device design to on-device AI, so products gain intelligence without cloud cost or latency.
Workflow friction
- Cloud dependence adds latency and cost
- Product roadmaps stall on AI feasibility
- Hard to prove on-device AI value to stakeholders
How the program helps
- Cut cloud cost with on-device inference
- Scope feasible edge AI features
- Demo working TinyML products fast
Practice use cases
- Product scoping
- Feasibility studies
- Stakeholder demos
Each cohort works through role-relevant hands-on labs, then leaves with a working on-device AI prototype for day-to-day product work.
Prerequisites: Participants should be comfortable with Python and basic machine learning concepts, plus some exposure to embedded systems or microcontroller programming. No prior TinyML experience is needed: the program starts with edge AI foundations, then builds to hands-on labs where every engineer trains, optimizes, and deploys models on real device hardware.
Embedded Developer to Edge AI Product Lead
The program takes engineers from edge AI basics to production-ready TinyML: foundation concepts, intermediate optimization and deployment, then advanced edge MLOps and a capstone product build.
Engineers grow from edge AI foundations to shipping on-device products as the modules progress.
Mixed cohort, or several teams at once?
We can tailor the training to different teams, roles, and experience levels within the same program.
Certificate of Training Completion.
On successful completion, employees receive an Edstellar course completion certificate, recognizing their commitment to ongoing learning and professional development. It validates the skills they have built and motivates them to keep growing and contribute to organizational success.
Earned, not attended. Awarded on completion of all hands-on labs and the capstone project.
Training Delivery Modes: Virtual, Onsite and Hybrid
Every mode is shaped around your hardware and product goals before delivery. The curriculum, hands-on labs, and capstone stay the same in each format, so what your engineers can do after training never depends on how you run it.
Hybrid Training
Live instruction across office and remote sessions, with hands-on labs and coaching throughout. Your engineers optimize models, deploy to microcontrollers, and profile hardware with instructor guidance. Best for multi-site teams.
- Live instruction across office and remote sessions
- Hands-on labs with instructor coaching
- Real-world use cases on device hardware
- Best for teams across sites and time zones
Onsite Training
Your instructor delivers at your facility, with labs set up on your device hardware. Engineers train, optimize, and deploy TinyML models with direct coaching. Best for co-located teams.
- Instructor delivers at your facility
- Labs set up on your device hardware
- Direct coaching during hands-on work
- Best for co-located teams wanting maximum interaction
Live Virtual Training
Live virtual sessions on Zoom or Microsoft Teams, with remote lab environments and screen-shared guidance through every task. Your engineers collaborate on model optimization and deployment in real time. Best for distributed engineering teams.
- Live sessions on Zoom or Microsoft Teams
- Remote access to the lab environment
- Screen-shared guidance through every task
- Best for distributed engineering teams
Need a training format that works for your team?
Full days, half days across weeks, or split by module. Tell us the delivery window and we schedule the cohort around it.
Learn from industry experts.
Every session is led by a practitioner who has run monitoring for models in production, not a generalist working from slides.
Chandrika
Data Science and Machine Learning Trainer in Mumbai
10+ years of Experience
Priyadharshini
Machine Learning with Python Trainer in Chennai
4+ years of Experience
Sudhanshu
Machine Learning Trainer in Bareilly
19+ years of Experience
Deepesh
Generative AI Trainer in Gaziabad
8+ years of Experience
Anudeep
Data Science with Python Trainer in Hyderabad
11+ years of Experience
Aswathi
C programming Trainer in Bengaluru
10+ years of Experience
Udayraj
Deep Learning and Python Trainer in Ahmednagar
7+ years of Experience
Trainers are matched to your stack and delivery window at scheduling. We confirm your assigned trainer, with a full profile, before the program is booked.
Frequently Asked Questions
Share your team size, preferred dates, and delivery mode in the lead form. We reply with a tailored proposal covering scope, schedule, and pricing for your group.
Both. Choose live virtual sessions on Zoom or Teams, onsite delivery at your office, or a hybrid mix across sites and time zones.
The standard program runs 20 hours of instructor-led sessions, typically over three days. We can reshape the schedule around your calendar.
We recommend 12 to 20 engineers per cohort for strong hands-on coaching. Larger engineering teams can run as parallel batches.
Yes. We shape labs, tools, and the capstone around your device platforms, sensors, and product goals before delivery.
Question not answered here?
Put it in the request. A training specialist answers it directly, within one business day.
Where this Training Fits in Your Team's learning path.
TinyML builds on IoT basics and TensorFlow, and is followed by edge security. Your team needs to know how connected devices work and how models are trained before squeezing them onto microcontrollers, then secures the devices that now run machine learning in the field.
Why Choose Edstellar as Your Edge AI and TinyML for Devices Enterprise Training Provider
If your engineering team needs to ship machine learning on-device, start here. Tell us about your hardware, timelines, and preferred delivery mode, and we will shape a program around them.
EXPERT TRAINERS
YEARS DELIVERING
ORGANIZATIONS TRAINED
CERTIFIED
Tailored
Content shaped around your edge AI and TinyML use cases, so every session maps to your real device needs.
Proven Methodology
5+ years delivering corporate training to firmware and IoT product engineers across industries and regions.
Practice-Based Learning
7 hands-on labs across 7 modules. Your team builds on-device ML models for microcontrollers with instructor guidance.
Expert Trainers
5,000+ certified trainers matched to your edge AI stack, with a full trainer profile shared before your program is booked.
Global Scale
One edge AI program across your offices worldwide, in hybrid, onsite, or live virtual.
Measurable Outcomes
Baseline-to-capstone measurement and Skills Matrix reporting keep your edge AI capability lift visible.
Also from Edstellar
Talent assessments
Psychometric, behavioral, leadership and 360-degree assessment.
→Training needs analysis
Needs analysis, gap identification, and a training roadmap.
→L&D consulting
Learning strategy, content, technology and ROI measurement.
→OD consulting
Org design, succession planning and cultural change.
→Managed training services
Training outsourcing, vendor management, logistics and administration.
→Coaching solutions
Executive and manager coaching that keeps skills in use.
Start an RFP shaped to your needs.
Answer three quick questions about team size, scope, and how often you need the program run, then send the request from the last step. Everything you choose travels with it, so you only fill this in once.
How many team members need training?
Edge AI & TinyML for Devices Training Packages & Pricing
Where L&D holds an annual budget, license packages price the whole year at once instead of raising a purchase order per workshop. Hours and licenses are pooled across teams, programs and locations, so unused capacity is not stranded in one department.
Starter
120
trainee licenses64 hours of group training
Tailored for SMBs
Growth
320
trainee licenses160 hours of group training
Ideal for growing SMBs
Enterprise
800
trainee licenses400 hours of group training
Designed for large corporations
Custom
Unlimited
trainee licensesDuration set by the rollout
Multi-region and multi-year
Training Packages & Pricing
Choose the training format that works for your team. Pricing is customized based on your requirements, cohort size, and delivery needs.
Request Edge AI and TinyML for Devices Training for Your Team
Share your team size, hardware platforms, preferred delivery mode, and timelines. We will respond with a tailored training plan covering scope, schedule, and pricing, and confirm trainer availability.