Industrial AIoT Bootcamp

Cohort Four | Edge AI

A FREE Gateway to a Future in Technology!

Accelerate your career in the growing field of Edge AI with our live, online bootcamp. Gain in-demand skills and hands-on experience in just 8 weeks of part-time learning!

APPLY NOW

Who can benefit from it?

Anyone willing to improve their skillset:

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Seniors and
Experts

Professionals with extensive
software or embedded systems
experience or those pursuing advanced
studies in Machine Learning, seeking
practical edge AI skills.

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Professionals from
Industry

Industry experts looking to
integrate Edge AI into IoT, robotics,
or smart systems for
innovative solutions.

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Creators with
Ideas

Entrepreneurs and innovators
aiming to develop transformative
products using Edge AI
technology.

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IT and
Tech Enthusiasts

Tech enthusiasts passionate
about exploring Edge AI for
personal or professional
growth.

Be a trailblazer in Edge AI

Join the AIoT Bootcamp and open doors to an endless world of possibilities. With us, you can gain:

Networking Opportunities to connect with fellow participants, industry experts, and potential collaborators.
Exclusive Access to SenzMate's Edge AI tools, resources, and datasets to support your learning journey.
Hands-On Experience in building and deploying AI models on edge devices like Jetson Nano and ARM microcontrollers.
Access to Expertise from SenzMate in AI deployment, optimization, and real-world applications in IoT and robotics.
BootCamp Program

Program Highlights

Target Audience

Belive

Professionals from diverse fields, including IT, engineering, IoT, robotics, electronics, and electrical, with some exposure to machine learning, looking to integrate AI into edge devices for real-world applications

Curriculum Design

Network

Our curriculum is tailored to provide a comprehensive understanding of Edge AI technologies and their practical applications in IoT, smart systems, and robotics.

Hands-on Learning

Innvate

Gain practical experience in building and deploying AI models on edge devices, guided by industry experts with proven expertise in AI and embedded systems.

About the Bootcamp

Industrial AIoT Bootcamp | Cohort 04: Edge AI

This module offers a comprehensive introduction to Edge AI technologies, including the integration of AI models on edge devices, data processing techniques, real-time applications, and industry-specific use cases. Participants will gain a deep understanding of Edge AI concepts, learn how to design and implement AI solutions on resource-constrained devices, and acquire hands-on experience with real-world edge AI projects. By the end of the module, participants will be equipped to develop and deploy AI models on edge devices, optimizing for performance and efficiency in industrial environments.

Module Plans for Cohort 4

  • Lecture :

    Setting Up a Camera Reading Application on Jetson Nano

    Hands-on :

    • Introduction to Jetson Nano and its capabilities.
    • Basic setup: hardware connections, OS installation, and remote access.
    • Installing essential packages and libraries for camera input.
    • Hands-on activity: Capturing and displaying video from a camera

    Cohort 04 | Week 01

  • Lecture :

    Real-Time Object Detection on Jetson Nano

    Hands-on :

    • Introduction to CUDA and ML package installation.
    • Model selection and understanding trade-offs between accuracy and performance.
    • Hands-on activity: Implementing real-time object detection with a pre-trained model.

    Cohort 04 | Week 02

  • Lecture :

    Real-Time People Tracking on Jetson Nano

    Hands-on :

    • Optimizing models for inference using TensorRT.
    • Implementing people tracking algorithms.
    • Deploying Jetson Nano in a production scenario.
    • Hands-on activity: Developing and testing a real-time tracking application.

    Cohort 04 | Week 03

  • Lecture :

    Deploying Models with Docker

    Hands-on :

    • Overview of Docker and containerization benefits.
    • Building Docker containers for ML applications on Jetson Nano.
    • Deployment considerations for scalable and reproducible applications.
    • Hands-on activity: Creating and deploying a Dockerized people counting application.

    Cohort 04 | Week 04

  • Lecture :

    Introduction to ARM Microcontrollers and Peripherals

    Hands-on :

    • Overview of ARM architecture and its significance in embedded systems.
    • Introduction to ARM microcontroller families, with a focus on STM32.
    • Exploring basic peripherals such as GPIO, ADC, UART, and I2C.
    • Hands-on activity: Setting up the development environment and running basic peripheral control programs.

    Cohort 04 | Week 05

  • Lecture :

    Estimating Compatibility with the Microcontroller and Applications

    Hands-on :

    • Evaluating the hardware capabilities of ARM microcontrollers.
    • Understanding resource constraints: memory, processing power, and peripherals.
    • Planning applications with STM32 microcontrollers: case studies and examples.
    • Hands-on activity: Application compatibility analysis using STM32CubeMX.

    Cohort 04 | Week 06

  • Lecture :

    Deploying a Neural Network Model in an ARM STM32 Microcontroller

    Hands-on :

    • Overview of neural network deployment in resource-constrained devices.
    • Training and converting a lightweight ML model for ARM.
    • Deployment steps using STM32Cube.AI
    • Hands-on activity: Deploying a pre-trained model onto an STM32 microcontroller.

    Cohort 04 | Week 07

  • Lecture :

    Simple Computer Vision Models in STM32

    Hands-on :

    • Introduction to STM32 for AI computer vision.
    • Optimizing models for STM32.
    • Deploying and testing a computer vision application.
    • Hands-on activity: Building and running a driver behavior analysis model

    Cohort 04 | Week 08

Cohort 04 | Week 01

Lecture :

Setting Up a Camera Reading Application on Jetson Nano

Hands-on :

  • Introduction to Jetson Nano and its capabilities.
  • Basic setup: hardware connections, OS installation, and remote access.
  • Installing essential packages and libraries for camera input.
  • Hands-on activity: Capturing and displaying video from a camera

Cohort 04 | Week 02

Lecture :

Real-Time Object Detection on Jetson Nano

Hands-on :

  • Introduction to CUDA and ML package installation.
  • Model selection and understanding trade-offs between accuracy and performance.
  • Hands-on activity: Implementing real-time object detection with a pre-trained model.

Cohort 04 | Week 03

Lecture :

Real-Time People Tracking on Jetson Nano

Hands-on :

  • Optimizing models for inference using TensorRT.
  • Implementing people tracking algorithms.
  • Deploying Jetson Nano in a production scenario.
  • Hands-on activity: Developing and testing a real-time tracking application.

Cohort 04 | Week 04

Lecture :

Deploying Models with Docker

Hands-on :

  • Overview of Docker and containerization benefits.
  • Building Docker containers for ML applications on Jetson Nano.
  • Deployment considerations for scalable and reproducible applications.
  • Hands-on activity: Creating and deploying a Dockerized people counting application.

Cohort 04 | Week 05

Lecture :

Introduction to ARM Microcontrollers and Peripherals

Hands-on :

  • Overview of ARM architecture and its significance in embedded systems.
  • Introduction to ARM microcontroller families, with a focus on STM32.
  • Exploring basic peripherals such as GPIO, ADC, UART, and I2C.
  • Hands-on activity: Setting up the development environment and running basic peripheral control programs.

Cohort 04 | Week 06

Lecture :

Estimating Compatibility with the Microcontroller and Applications

Hands-on :

  • Evaluating the hardware capabilities of ARM microcontrollers.
  • Understanding resource constraints: memory, processing power, and peripherals.
  • Planning applications with STM32 microcontrollers: case studies and examples.
  • Hands-on activity: Application compatibility analysis using STM32CubeMX.

Cohort 04 | Week 07

Lecture :

Deploying a Neural Network Model in an ARM STM32 Microcontroller

Hands-on :

  • Overview of neural network deployment in resource-constrained devices.
  • Training and converting a lightweight ML model for ARM.
  • Deployment steps using STM32Cube.AI
  • Hands-on activity: Deploying a pre-trained model onto an STM32 microcontroller.

Cohort 04 | Week 08

Lecture :

Simple Computer Vision Models in STM32

Hands-on :

  • Introduction to STM32 for AI computer vision.
  • Optimizing models for STM32.
  • Deploying and testing a computer vision application.
  • Hands-on activity: Building and running a driver behavior analysis model

Expected Outcomes of Cohort 4

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Comprehensive understanding of Edge AI concepts and technologies.

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Proficiency in deploying AI models on edge devices for real-time applications such as computer vision, predictive analytics, and automation.

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Expertise in optimizing AI models for resource-constrained devices like Jetson Nano and ARM microcontrollers.

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Hands-on experience with tools and frameworks for Edge AI, including TensorRT, Docker, and STM32Cube.AI.

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Knowledge of industry best practices for implementing scalable and efficient Edge AI solutions.

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Confidence in designing, developing, and deploying Edge AI solutions for IoT, robotics, and smart systems.

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Networking opportunities with industry professionals and peers.

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Certificate of Completion to validate your skills and knowledge in Edge AI.

Prerequisites for Cohort 4

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Expected to dedicate 8-10 hours per week to complete the bootcamp, running from January to March

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Engage themselves in 2-hour hands-on sessions every Saturday (attendance is mandatory).

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Expected to deepen their understanding with dedicated 1-hour Q&A sessions every Sunday

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Basic understanding of embedded systems, AI concepts, and machine learning is preferred, with familiarity in Python programming and data processing techniques.

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Interest in working with edge devices like Jetson Nano and ARM microcontrollers, with a passion for solving real-world problems in IoT, robotics, and smart systems.

How to enroll

Apply and join BootCamp in 5 easy steps

Kickstart your journey by expressing your interest in joining the AIoT Bootcamp. Complete the application form to communicate your passion and career aspirations.
1
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After expressing your interest, our team will thoroughly review your application. Once selected, you'll receive a confirmation along with detailed information about the bootcamp.
2
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Participate in an orientation session where you'll get insights into the structure of the bootcamp, meet the instructors, and connect with fellow participants. This session sets the stage for a collaborative and engaging learning experience.
3
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Get hands-on with the basics! Our expert instructors will guide you through fundamental big data and IoT concepts, ensuring you have a strong foundation before delving into more advanced topics.
4
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Experience the practical side of big data and IoT! Engage in a collaborative project kickoff where you'll work on real-world applications. From concept to implementation, our team will support you every step of the way.
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SenzMate's Industrial AIoT Bootcamp | Cohort o4- Enrollment Form

Join SenzMate's Edge AI Bootcamp by filling out the form below. This 8-week program is designed for professionals eager to explore the world of Edge AI and embedded systems. Gain hands-on experience in deploying AI models on edge devices like Jetson Nano and ARM microcontrollers while developing practical skills in real-time applications. Take the next step in your career by mastering Edge AI in a collaborative, industry-focused learning environment.

Important Information:
  • Funding: This program is fully funded.
  • Selection Process: In the first round, we are selecting 30 candidates based on your profile for the 4th cohort.
  • Registration Deadline: Please complete your registration by 13th January 2025.

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Frequently Asked Questions



What is an Edge AI Bootcamp like? ▼

  • Edge AI Bootcamps provide an immersive, hands-on learning experience designed to equip you with the skills to build AI applications on resource-constrained edge devices.
  • Over 8 weeks, you will work on real-world projects and gain practical experience in deploying AI models on devices like Jetson Nano and ARM microcontrollers.
  • This bootcamp is 100% focused on industrial applications, ensuring that the skills you acquire are directly relevant to real-world challenges in IoT, robotics, and embedded AI systems.
  • Courses will be conducted on a dedicated Moodle LMS platform for an engaging, remote, part-time learning experience.

How long does it take to complete the Edge AI Bootcamp? ▼

  • SenzMate's Edge AI Bootcamp is a 8-week program designed to provide you with the critical skills needed to deploy and optimize AI models on edge devices.
  • Whether you are a professional looking to upskill or a beginner in the field, this bootcamp offers a comprehensive and practical learning approach to help you thrive in the growing Edge AI industry.

How much does the Edge AI Bootcamp cost? ▼

  • The Edge AI Bootcamp has been developed and resourced by SenzMate AIoT Intelligence as part of our CSR initiative.
  • As a result, this entire 8-week program is completely free for selected participants.

What are the skills needed for Edge AI? ▼

  • To succeed in the Edge AI Bootcamp, it's helpful to have a basic understanding of AI concepts, machine learning, and embedded systems. Knowledge of Python programming is essential, as well as an interest in working with edge devices like Jetson Nano or ARM microcontrollers.
  • Prior experience with machine learning frameworks, data processing, or IoT systems is a plus, but not mandatory. Enthusiasm for real-time applications, resource optimization, and hands-on problem-solving will significantly contribute to your success.

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