IoT & Embedded Intelligence · Practical career preparation

IoT Engineering & Edge AI

Connect the device. Run the model. Understand every failure in between. Build a connected sensor application with local AI inference, reliable messaging and an observable backend. Learn to handle stale data, network outages and device updates while evaluating a small model on held-out sensor recordings.

11 core modules Preparation matched to your diagnostic Three guided projects integrated into the curriculum One independently assessed capstone
RoboEdify track record
10,000+
alumni transformed
1,000+
hiring partners
4.8/5
average class rating
87%
placed in 6 months
10+
years of training
Where our AI alumni work
MicrosoftAmazonSalesforceServiceNowDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL MicrosoftAmazonSalesforceServiceNowDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL
Direct answer

What does an IoT and Edge AI developer build?

An IoT developer connects devices to applications through sensing, messaging and data services. Edge AI adds model inference on the device, close to the data source. This course combines those responsibilities in a bounded sensor system, measuring both model behavior and the complete device-to-dashboard path.

The learning journey
01Acquire.

Collect sensor data with clear quality and timing information.

02Connect.

Move messages and commands through defined device/backend contracts.

03Infer.

Deploy and evaluate a compact quantized model on the MCU.

04Operate.

Observe the pipeline and recover from outages and update failures.

Why this course focuses on practical evidence

Model accuracy is only one part of a working connected device.

You will investigate leaked data splits, stale samples, duplicate commands and interrupted updates. The capstone must explain what happens when the network is unavailable as well as when the model makes a mistake.

Learning format

Learn through classes, labs and individual feedback.

The program combines live mentor-led classes, guided labs, project work and support sessions. Your diagnostic determines the preparation needed before the common core.

Choose on-campus, live online or a working-professional format. An advisor can explain the class format, lab access and learner support before enrollment.

On-campusLive onlineWorking-professional format
Who should join

A common core, with preparation matched to your starting point.

Graduates and learners entering the fieldJoin with basic firmware readiness or build it through the firmware pathway first. Preparation bridges bounded gaps in device work, Python, data and networking; it is not a complete first programming course.
Working engineersFirmware, software and data engineers can use the diagnostic to identify gaps on the other side of the system. All learners demonstrate device behavior, data-quality reasoning and integration tests.
Learning outcomes

Leave with skills you can demonstrate.

Define a device-to-backend contractTrack units, timestamps, sequence IDs and failure behavior.
Implement messaging and recoveryHandle reconnects, duplicates and bounded offline queues.
Secure device interactionsTest identity, certificate validation, authorization and update recovery.
Create a reliable datasetSplit recordings appropriately and investigate class-level errors.
Deploy a small MCU modelMatch preprocessing and account for runtime memory.
Measure the whole pipelineSeparate inference time from sampling-to-dashboard behavior.
Preparation based on your diagnostic

What you practise before the core.

Embedded firmware bridgeSensor readout, task timing, buffers and UART debugging on the selected device.
Python, data and basic statisticsParse logs, form labelled windows, use train/validation/test splits and calculate classification metrics.
Networking foundationsIP addressing, DNS, sockets, HTTP and client/server failure modes.
Reproducible project workflowGit, environment pinning, configuration and test logs.

Both routes complete the same practical exit requirements. A diagnostic identifies the preparation you need. Previously demonstrated foundations can be recognized, while broader gaps receive a separate learning plan before the core.

Course curriculum

11 modules. A complete path from foundations to an independent capstone.

Each module combines technical concepts, practical work, a failure investigation and a reviewed submission. Preparation sits before the common core.

01

Connected-system requirements and architecture

IoT Engineering & Edge AI

Define the full path and its failure contracts.

+
Topics
•Device/gateway/backend roles
•Sampling and event contracts
•Timestamps, sequence IDs
•Latency, availability and observability
Hands-on lab
Define a sensor-to-dashboard pipeline and failure matrix before selecting implementation details.
Failure scenario you solve
A dashboard shows a value without revealing that it came from a long-disconnected device.
You build
Architecture diagram and acceptance plan.
Assessed outcome: Identify where data can be lost, duplicated or delayed.
02

Device acquisition and data quality

IoT Engineering & Edge AI

Make sensor quality visible to the application.

+
Topics
•Sensor calibration, sampling, buffering, units, timestamps, missing/stale readings and firmware interfaces
Hands-on lab
Capture repeatable sensor streams with quality flags and detect saturation/disconnection.
Failure scenario you solve
A failed read repeats the previous sample and is treated as new data.
You build
Dataset schema and acquisition firmware.
Assessed outcome: Avoid passing stale measurements as new valid data.
03

Networking and MQTT messaging

IoT Engineering & Edge AI

Handle messages beyond the happy path.

+
Topics
•TCP/TLS boundaries, MQTT topics, QoS, retained messages, session behavior, reconnects and application acknowledgements
Hands-on lab
Implement telemetry and a bounded configuration command; test broker/network loss and duplicates.
Failure scenario you solve
A repeated command produces the same side effect twice after reconnection.
You build
Topic/API contract and messaging test log.
Assessed outcome: Explain why transport QoS does not automatically make application side effects exactly once.
04

Gateway and industrial integration

IoT Engineering & Edge AI

Translate device data without losing its meaning.

+
Topics
•Linux gateway service, serial/Modbus RTU basics, register decoding, polling, buffering and protocol translation
Hands-on lab
Read a simulated or lab Modbus device and publish normalized measurements through MQTT.
Failure scenario you solve
The gateway applies the wrong register endianness or scale.
You build
Gateway adapter and protocol mapping.
Assessed outcome: Handle timeouts and document register endianness/scaling.
05

Device identity and update lifecycle

IoT Engineering & Edge AI

Keep identity and recovery intact when something goes wrong.

+
Topics
•TLS certificate validation, per-device identity, authorization, secret handling, signed firmware, rollback/recovery and configuration versioning
Hands-on lab
Reject an invalid server certificate and unauthorized command; exercise a signed update and interrupted-update recovery on the qualified platform.
Failure scenario you solve
An interrupted update leaves the device unable to start, or a client accepts an invalid certificate.
You build
Identity/update design and negative-test evidence.
Assessed outcome: Restore a functioning device without disabling verification to make the lab pass.
06

Backend ingestion and fleet observability

IoT Engineering & Edge AI

Show whether the device and data are actually current.

+
Topics
•Schema validation, persistence, idempotency, device status, last-seen timestamps, logs and dashboard freshness
Hands-on lab
Build a small containerized broker/ingestion/database/dashboard stack with supplied scaffolding.
Failure scenario you solve
Ingestion succeeds but the dashboard never marks an offline device as stale.
You build
Service configuration and end-to-end trace.
Assessed outcome: Show device-offline and data-stale states correctly.
07

TinyML dataset and baseline

IoT Engineering & Edge AI

Evaluate on recordings the model has not seen.

+
Topics
•Problem framing, labels, sessions, class imbalance, leakage, metrics, baseline models and deployment constraints
Hands-on lab
Collect or use a licensed sensor dataset; split by recording/session before forming overlapping windows.
Failure scenario you solve
Overlapping windows from the same session appear in both training and test sets.
You build
Dataset card, split manifest and baseline evaluation.
Assessed outcome: Detect train/test leakage and explain class-level errors.
08

Quantization and MCU inference

IoT Engineering & Edge AI

Match the deployed numeric and feature pipeline.

+
Topics
•Feature pipeline, integer quantization, representative calibration data, supported operators, tensor arena and runtime integration
Hands-on lab
Deploy a compact classifier with a qualified TFLM runtime and compare host/device outputs on identical windows.
Failure scenario you solve
The model performs well on the host but receives differently normalized inputs on the MCU.
You build
Model manifest and firmware inference tests.
Assessed outcome: Explain accuracy changes and account for model, arena, stack and buffers.
09

System performance and resilience

IoT Engineering & Edge AI

Measure the system when connections and workloads change.

+
Topics
•Sampling-to-alert latency, inference timing, offline queues, bounded retries, backpressure, model versioning and drift awareness
Hands-on lab
Inject outages and workload bursts; measure resource use and compare a simple threshold baseline with the model.
Failure scenario you solve
A reconnect loop fills memory while inference-only latency still looks healthy.
You build
End-to-end measurement report and recovery tests.
Assessed outcome: Separate inference-only performance from complete system latency.
10

Independent connected Edge AI capstone

Capstone

Demonstrate an observable and recoverable connected model.

+
Topics
•Device, local model, messaging, backend, update/recovery and individual ownership
Hands-on lab
Deliver a connected sensor condition classifier with local inference and an observable dashboard.
Failure scenario you solve
The classifier works locally but loses results or repeats commands during an outage.
You build
Firmware/model/backend repository and fault-injection demonstration.
Assessed outcome: Meet the frozen device/data contracts and defend held-out performance.
11

Interview and portfolio defense

Career preparation

Trace a failure to the right layer.

+
Topics
•Device debugging, protocols, data leakage, resource budgets and system failures
Hands-on lab
Trace an unseen end-to-end failure and explain a model misclassification.
Failure scenario you solve
An assessor presents a wrong alert and asks whether sensing, preprocessing, messaging or inference caused it.
You build
Portfolio case study and individual defense.
Assessed outcome: Distinguish a sensor, network, backend and model problem with evidence.

Scope note: Core depth is connected-device integration plus small sensor-model deployment. No custom Linux kernel driver, custom AI accelerator, large on-device language model, production industrial certification or cloud-platform certification is implied. Firmware fundamentals are prerequisites; gateway application work does not replace the Embedded Linux BSP course.

Tools and methods

Use a focused stack to build and explain your work.

Embedded C and a qualified MCU SDKAcquire sensor data and integrate the device application.
MQTT, TLS and a local brokerImplement messaging, identity and negative tests.
Python and model-training/conversion toolsPrepare data and evaluate a compact classifier.
TensorFlow Lite for MicrocontrollersRun the supported quantized model on the selected target.
Containers, database and dashboard scaffoldingObserve ingestion and device status.
Optional compatible optimized kernelsCompare supported deployment optimizations where applicable.

The selected network-capable MCU must support the sensor, model runtime and signed-update/recovery exercise. The exact stack is qualified as a complete combination. A local backend supports the core work; a paid cloud account is not a mandatory core dependency.

Projects and portfolio

Three guided projects, followed by an independent capstone.

The guided projects develop across the modules and are part of the core curriculum.

Guided project 1

Resilient sensor telemetry

Connect a device to MQTT and test disconnects, stale readings, duplicate messages and bounded buffering.

Portfolio evidence
Topic contractFirmwareOutage test logs
Guided project 2

Gateway and observable backend

Translate a lab/simulated Modbus source and build a small ingestion/database/dashboard stack using scaffolding.

Portfolio evidence
Gateway adapterSchemaDevice-status dashboard
Guided project 3

TinyML sensor classifier

Create session-based data splits, train a compact model and compare quantized host/device outputs.

Portfolio evidence
Dataset cardModel manifestDevice inference tests
Capstone

Connected sensor classifier with local inference

Build a small MCU classifier that publishes results to a local backend and dashboard. Demonstrate offline buffering, status reporting, bounded configuration commands and a signed-update/recovery path on the selected platform.

What you submit
  • •A clear scope, design or integration plan, and acceptance checklist.
  • •Your source files, scripts and configuration with a readable project guide.
  • •Device firmware, dataset and model evidence, backend configuration and end-to-end fault tests.
  • •A failure investigation showing the cause, correction and recheck.
  • •A final report explaining results, assumptions and remaining limitations.
  • •An individual walkthrough and an unfamiliar debugging task.
What the assessor checks: Your implementation addresses the agreed project requirements, tests the relevant boundary and failure cases, and can be reproduced from the submitted materials. You explain your own contribution and support conclusions with actual results. A polished group demonstration alone does not meet the individual exit requirement.
Assessment and completion

Demonstrate what you can do.

RoboEdify · Certificate of Completion
IoT Engineering & Edge AI
Presented to
Learner name
Awarded for completing the course's practical assessments and independently defending its capstone project.
Manikanta Kona
Founder · RoboEdify
ROBO
EDIFY
CERT
30%
Module labs
20%
Practical checkpoints
35%
Capstone
15%
Individual debugging and defense
The proposed completion standard is 70% overall, at least 60% separately in the capstone and individual defense, and completion of all mandatory practical requirements. Feedback identifies specific gaps for remediation and reassessment.
This is a proposed RoboEdify course credential. External accreditation, vendor certification and partner endorsement are not implied by the course title.
Career preparation

Prepare for relevant roles with work you can explain.

IoT Firmware/Integration TraineePractise device messaging and failure-aware integration.
Connected-Device DeveloperBuild sensor-to-backend contracts and observable applications.
Junior Edge AI Deployment EngineerDevelop small-model integration and measurement skills.

Your career-preparation work includes a reviewed technical project summary, a readable repository, resume statements grounded in your contribution and a live technical interview. Role eligibility depends on each employer's requirements and your demonstrated skills.

Career support

Build your portfolio. Prepare your profile. Practise your interviews.

Career support includes portfolio and profile preparation, interview practice, and role-fit introductions where available. The shared hiring-partner network includes Infosys, TCS, Deloitte, Accenture, Cognizant, NTT Data and Capgemini.
01 / PORTFOLIO

Evidence from your own work.

For this course, your portfolio centres on device firmware, dataset and model evidence, backend configuration and end-to-end fault tests you complete.

02 / PROFILE

Profile and resume preparation.

A reviewed technical project summary, a readable repository and resume statements grounded in your contribution.

03 / INTERVIEWS

Interview practice and introductions.

Technical interview practice, with role-fit introductions where available.

RoboEdify does not guarantee an interview, offer, salary, employer, location or timeline.

Institute leadership

Meet the team behind RoboEdify.

MK
Manikanta Kona
Founder, RoboEdify · Enterprise AI Architect
Enterprise AI · Agentic Systems · LLM Platforms · Robotics & Edge AI
15 yrs
ENTERPRISE AI
2,400+
LEARNERS
4.9 /5
RATING

Manikanta brings 15 years of enterprise platform architecture experience from AT&T, Salesforce, Cox Communications and Broadcom. His background includes enterprise platform and AI rollouts for Fortune-500 banks, telcos and insurers, and production agentic-AI deployments for governed case handling.

Education: M.S. in Engineering, Purdue University.

RK
Ravi Krishna
Chief Technologist, RoboEdify · Implementation & Delivery Lead
Enterprise automation · Deployment · Evaluation evidence · Delivery governance
10 yrs
IMPLEMENTATION & DELIVERY
1,800+
LEARNERS
4.8 /5
RATING

Ravi leads RoboEdify's implementation and delivery practice. His background spans enterprise automation programs, deployment, evaluation evidence and delivery governance.

Industry voices from RoboEdify’s AI programs

What employers say about RoboEdify’s AI and robotics graduates.

The following testimonials retain their original program context. They describe AI, robotics and enterprise-program experience, rather than outcomes from this course.

Microsoft logo

RoboEdify grads ramp 40% faster on applied AI projects than typical hires. Best AI engineering pipeline in India.

Aakash Mehta

Aakash Mehta, Partner Programme Lead, Microsoft

Deloitte logo

We've onboarded 80+ RoboEdify alumni in 18 months. Lowest ramp time we've seen for ML plus AI agent practices.

Anita Sharma

Anita Sharma, Senior Manager, Deloitte

Mphasis logo

The programme is comprehensive — predictive ML, LLM systems, plus agentic and robotics work. Grads come pre-trained for enterprise.

Rahul Bhatt

Rahul Bhatt, Solutions Lead, Mphasis

TCS logo

Their agent + robotics track produces engineers who ship production-grade perception and control code on day one. Genuinely rare.

Deepak Pillai

Deepak Pillai, Senior Architect, TCS

Accenture logo

What sets RoboEdify apart is the simulation-to-hardware layer baked into the AI track. Our clients ask for exactly this profile.

Suresh Menon

Suresh Menon, Practice Lead, Accenture

Infosys logo

Their fundamentals prep is rigorous, and the capstone with a real deployed system and safety case is what closes interviews for us.

Vikram Iyer

Vikram Iyer, Director, Infosys

Wipro logo

RoboEdify's AI grads get models into production twice as fast in the first 90 days. Our internal metrics back this up clearly.

Lakshmi Nair

Lakshmi Nair, VP Engineering, Wipro

Cognizant logo

Best AI + robotics pipeline we've sourced from in India. Their projects are production work, not toy code.

Karthik Subramanian

Karthik Subramanian, Engineering Director, Cognizant

Capgemini logo

Strong ML and edge-deployment foundation. Their grads need almost zero ramp time on enterprise engagements with us.

Arun Joshi

Arun Joshi, Practice Director, Capgemini

IBM logo

We've placed 40+ RoboEdify alumni across our AI and automation teams. Strong fundamentals, sharp on the agent stack.

Sanjay Verma

Sanjay Verma, Talent Director, IBM

LTIMindtree logo

ITOM + Predictive Intelligence is exactly the talent gap we've been struggling to close. RoboEdify is filling it for us reliably.

Anjali Desai

Anjali Desai, Practice Head, LTIMindtree

Tech Mahindra logo

Their AI track delivers engineers who navigate data, models and integrations on customer engagements unsupervised.

Ramesh Iyer

Ramesh Iyer, Senior Manager, Tech Mahindra

Cyient logo

Hired 25+ RoboEdify graduates for our AI practice. Strong coding, strong ML depth, sharp on the agent layer.

Geetha Pillai

Geetha Pillai, Talent Acquisition Lead, Cyient

Microsoft logo

RoboEdify grads who blend robotics with Azure OpenAI land production-ready on day one. Rare combination, well-trained.

Priya Reddy

Priya Reddy, Talent Lead, Microsoft

AI alumni across RoboEdify

Meet alumni featured in our AI programs.

SB
Spandana Bala
ML Engineer
Hyderabad · India
Now at · Infosys
NV
Naveen Vedala
AI Agent Engineer
Hyderabad · India
Now at · TCS
TA
Tejashwini Addla
Simulation Engineer
Hyderabad · India
Now at · Deloitte
TD
Tharunesh Dillikar
Robotics Software Engineer
Seattle · United States
Now at · Accenture
MM
Mujahed Mohammed
Edge AI Engineer
Hyderabad · India
Now at · Accenture
BK
Bhargav Kumar Murala
Physical AI Engineer
Hyderabad · India
Now at · Capgemini
SL
Sai Manasa Leburi
Autonomous Systems Engineer
New York · United States
Now at · NTT Data
RD
Rahul Dhamma
Robot Learning Engineer
Hyderabad · India
Now at · Cognizant
Our locations

Come chat with us—on campus or online.

Flagship campus
Hyderabad
2nd Floor, Hitech City Road · Above Domino's · Opp. Cyber Towers, Jai Hind Enclave · Hyderabad, Telangana
Call
+91 8142998866
US desk
+1 256 388 7766
Opening hours
Mon–Sun · 7 AM–9 PM
Online
Global
Live online classes and mentorship, with working-professional learning options. IoT labs use a qualified connected MCU and local backend, with device access explained before enrollment.
Format
Live online + mentorship
Options
Working-professional
Learning support

Support when you need to catch up.

Freeze your seat for up to 90 days and rejoin the next class at no extra fee. TAs run catch-up sessions every Saturday, and recordings of every live session are available for the lifetime of your account.

FAQs

Questions about prerequisites, tools and completion.

Can I join without embedded firmware experience?+
You need basic firmware readiness. Complete suitable foundations or the firmware pathway first if the diagnostic reveals broader gaps.
Will I train large AI models?+
No. The core uses a compact sensor classifier that fits the selected MCU. Large language models and custom AI accelerators are separate topics.
Do I need a paid cloud subscription?+
The core uses a local broker and containerized backend. Optional cloud extensions would have their own requirements.
Is model accuracy guaranteed?+
No fixed accuracy is promised without a defined dataset and task. You report held-out metrics, class errors, limitations and a simple comparison baseline.
Does this replace the Embedded Linux course?+
No. Gateway work here is application integration. Kernel drivers, device tree and BSP development belong to the Linux course.
Will I test security and updates?+
Yes, within the qualified teaching platform: invalid certificates, unauthorized commands and interrupted-update recovery are practical scenarios.
Can graduates and working engineers both join?+
Yes, subject to the stated entry requirements. Both follow the same practical core, with preparation assigned through a diagnostic. Advanced pathways require the relevant foundations before their bridge.
How is the learning workload structured?+
The program combines live mentor-led classes, guided labs, project work and support sessions. An advisor can explain the current class format before enrollment.
Can I study online or on campus?+
RoboEdify offers its Hyderabad campus, live online classes and a working-professional format. Discuss the course-specific lab access and class format with an advisor.
What if I fall behind or need to pause?+
You can freeze your seat for up to 90 days and rejoin the next class at no extra fee. Saturday catch-up sessions and recordings of every live session support your learning. Recordings remain available for the lifetime of your account.
Is placement guaranteed?+
No. Career support includes portfolio and profile preparation, interview practice and role-fit introductions where available. RoboEdify does not guarantee an interview, offer, salary, employer, location or timeline.
What if I do not meet a practical requirement?+
Feedback identifies the missing capability and the work needed for reassessment. Attendance alone does not meet the proposed completion standard. Confirm course-specific reassessment arrangements before enrollment.
How can I learn about fees and lab access?+
Speak to a course advisor about the current offering, preparation requirements, fees, equipment or software access and learner-support terms.

Still have a question?

Find your starting point in IoT Engineering & Edge AI.
One million AI-native professionals by 2027.

Tell us about your technical background and the work you want to do. We’ll help you understand the preparation you need and how this course's projects connect to your learning goals.

Plan your learning

Course
IoT Engineering & Edge AI
Preparation
Diagnostic-based preparation before the common core
Level
Specialist
Curriculum
11 modules

Confirm your intake dates, delivery mode, fees, assessment and practical access with RoboEdify before enrolling. Course content describes the learning scope; an enquiry does not reserve a seat.