TinyML for IoT Applications Training Course
TinyML extends machine learning capabilities to ultra-low-power IoT devices, enabling real-time intelligence at the edge.
This instructor-led, live training (online or onsite) is aimed at intermediate-level IoT developers, embedded engineers, and AI practitioners who wish to implement TinyML for predictive maintenance, anomaly detection, and smart sensor applications.
By the end of this training, participants will be able to:
- Understand the fundamentals of TinyML and its applications in IoT.
- Set up a TinyML development environment for IoT projects.
- Develop and deploy ML models on low-power microcontrollers.
- Implement predictive maintenance and anomaly detection using TinyML.
- Optimize TinyML models for efficient power and memory usage.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Course Outline
Introduction to TinyML and IoT
- What is TinyML?
- Benefits of TinyML in IoT applications
- Comparison of TinyML with traditional cloud-based AI
- Overview of TinyML tools: TensorFlow Lite, Edge Impulse
Setting Up the TinyML Environment
- Installing and configuring Arduino IDE
- Setting up Edge Impulse for TinyML model development
- Understanding microcontrollers for IoT (ESP32, Arduino, Raspberry Pi Pico)
- Connecting and testing hardware components
Developing Machine Learning Models for IoT
- Collecting and preprocessing IoT sensor data
- Building and training lightweight ML models
- Converting models to TensorFlow Lite format
- Optimizing models for memory and power constraints
Deploying AI Models on IoT Devices
- Flashing and running ML models on microcontrollers
- Validating model performance in real-world IoT scenarios
- Debugging and optimizing TinyML deployments
Implementing Predictive Maintenance with TinyML
- Using ML for equipment health monitoring
- Sensor-based anomaly detection techniques
- Deploying predictive maintenance models on IoT devices
Smart Sensors and Edge AI in IoT
- Enhancing IoT applications with TinyML-powered sensors
- Real-time event detection and classification
- Use cases: environmental monitoring, smart agriculture, industrial IoT
Security and Optimization in TinyML for IoT
- Data privacy and security in edge AI applications
- Techniques for reducing power consumption
- Future trends and advancements in TinyML for IoT
Summary and Next Steps
Requirements
- Experience with IoT or embedded systems development
- Familiarity with Python or C/C++ programming
- Basic understanding of machine learning concepts
- Knowledge of microcontroller hardware and peripherals
Audience
- IoT developers
- Embedded engineers
- AI practitioners
Open Training Courses require 5+ participants.
TinyML for IoT Applications Training Course - Booking
TinyML for IoT Applications Training Course - Enquiry
TinyML for IoT Applications - Consultancy Enquiry
Consultancy Enquiry
Testimonials (1)
The oral skills and human side of the trainer (Augustin).
Jeremy Chicon - TE Connectivity
Course - NB-IoT for Developers
Upcoming Courses (Minimal 5 peserta)
Related Courses
5G and Edge AI: Enabling Ultra-Low Latency Applications
21 HoursThis instructor-led, live training in Indonesia (online or onsite) is aimed at intermediate-level telecom professionals, AI engineers, and IoT specialists who wish to explore how 5G networks accelerate Edge AI applications.
By the end of this training, participants will be able to:
- Understand the fundamentals of 5G technology and its impact on Edge AI.
- Deploy AI models optimized for low-latency applications in 5G environments.
- Implement real-time decision-making systems using Edge AI and 5G connectivity.
- Optimize AI workloads for efficient performance on edge devices.
Advanced Edge AI Techniques
14 HoursThis instructor-led, live training in Indonesia (online or onsite) is aimed at advanced-level AI practitioners, researchers, and developers who wish to master the latest advancements in Edge AI, optimize their AI models for edge deployment, and explore specialized applications across various industries.
By the end of this training, participants will be able to:
- Explore advanced techniques in Edge AI model development and optimization.
- Implement cutting-edge strategies for deploying AI models on edge devices.
- Utilize specialized tools and frameworks for advanced Edge AI applications.
- Optimize performance and efficiency of Edge AI solutions.
- Explore innovative use cases and emerging trends in Edge AI.
- Address advanced ethical and security considerations in Edge AI deployments.
Building AI Solutions on the Edge
14 HoursThis instructor-led, live training in Indonesia (online or onsite) is aimed at intermediate-level developers, data scientists, and tech enthusiasts who wish to gain practical skills in deploying AI models on edge devices for various applications.
By the end of this training, participants will be able to:
- Understand the principles of Edge AI and its benefits.
- Set up and configure the edge computing environment.
- Develop, train, and optimize AI models for edge deployment.
- Implement practical AI solutions on edge devices.
- Evaluate and improve the performance of edge-deployed models.
- Address ethical and security considerations in Edge AI applications.
Building Secure and Resilient Edge AI Systems
21 HoursThis instructor-led, live training in Indonesia (online or onsite) is aimed at advanced-level cybersecurity professionals, AI engineers, and IoT developers who wish to implement robust security measures and resilience strategies for Edge AI systems.
By the end of this training, participants will be able to:
- Understand security risks and vulnerabilities in Edge AI deployments.
- Implement encryption and authentication techniques for data protection.
- Design resilient Edge AI architectures that can withstand cyber threats.
- Apply secure AI model deployment strategies in edge environments.
Digital Transformation with IoT and Edge Computing
14 HoursThis instructor-led, live training in Indonesia (online or onsite) is aimed at intermediate-level IT professionals and business managers who wish to understand the potential of IoT and edge computing for enabling efficiency, real-time processing, and innovation in various industries.
By the end of this training, participants will be able to:
- Understand the principles of IoT and edge computing and their role in digital transformation.
- Identify use cases for IoT and edge computing in manufacturing, logistics, and energy sectors.
- Differentiate between edge and cloud computing architectures and deployment scenarios.
- Implement edge computing solutions for predictive maintenance and real-time decision-making.
Edge AI for Agriculture: Smart Farming and Precision Monitoring
21 HoursThis instructor-led, live training in Indonesia (online or onsite) is aimed at beginner-level to intermediate-level agritech professionals, IoT specialists, and AI engineers who wish to develop and deploy Edge AI solutions for smart farming.
By the end of this training, participants will be able to:
- Understand the role of Edge AI in precision agriculture.
- Implement AI-driven crop and livestock monitoring systems.
- Develop automated irrigation and environmental sensing solutions.
- Optimize agricultural efficiency using real-time Edge AI analytics.
Edge AI in Autonomous Systems
14 HoursThis instructor-led, live training in Indonesia (online or onsite) is aimed at intermediate-level robotics engineers, autonomous vehicle developers, and AI researchers who wish to leverage Edge AI for innovative autonomous system solutions.
By the end of this training, participants will be able to:
- Understand the role and benefits of Edge AI in autonomous systems.
- Develop and deploy AI models for real-time processing on edge devices.
- Implement Edge AI solutions in autonomous vehicles, drones, and robotics.
- Design and optimize control systems using Edge AI.
- Address ethical and regulatory considerations in autonomous AI applications.
Edge AI for IoT Applications
14 HoursThis instructor-led, live training in Indonesia (online or onsite) is aimed at intermediate-level developers, system architects, and industry professionals who wish to leverage Edge AI for enhancing IoT applications with intelligent data processing and analytics capabilities.
By the end of this training, participants will be able to:
- Understand the fundamentals of Edge AI and its application in IoT.
- Set up and configure Edge AI environments for IoT devices.
- Develop and deploy AI models on edge devices for IoT applications.
- Implement real-time data processing and decision-making in IoT systems.
- Integrate Edge AI with various IoT protocols and platforms.
- Address ethical considerations and best practices in Edge AI for IoT.
Edge Computing
7 HoursThis instructor-led, live training in Indonesia (online or onsite) is aimed at product managers and developers who wish to use Edge Computing to decentralize data management for faster performance, leveraging smart devices located on the source network.
By the end of this training, participants will be able to:
- Understand the basic concepts and advantages of Edge Computing.
- Identify the use cases and examples where Edge Computing can be applied.
- Design and build Edge Computing solutions for faster data processing and reduced operational costs.
Federated Learning in IoT and Edge Computing
14 HoursThis instructor-led, live training in Indonesia (online or onsite) is aimed at intermediate-level professionals who wish to apply Federated Learning to optimize IoT and edge computing solutions.
By the end of this training, participants will be able to:
- Understand the principles and benefits of Federated Learning in IoT and edge computing.
- Implement Federated Learning models on IoT devices for decentralized AI processing.
- Reduce latency and improve real-time decision-making in edge computing environments.
- Address challenges related to data privacy and network constraints in IoT systems.
Deploying AI on Microcontrollers with TinyML
21 HoursThis instructor-led, live training in Indonesia (online or onsite) is aimed at intermediate-level embedded systems engineers and AI developers who wish to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.
By the end of this training, participants will be able to:
- Understand the fundamentals of TinyML and its benefits for edge AI applications.
- Set up a development environment for TinyML projects.
- Train, optimize, and deploy AI models on low-power microcontrollers.
- Use TensorFlow Lite and Edge Impulse to implement real-world TinyML applications.
- Optimize AI models for power efficiency and memory constraints.
NB-IoT for Developers
7 HoursIn this instructor-led, live training in Indonesia, participants will learn about the various aspects of NB-IoT (also known as LTE Cat NB1) as they develop and deploy a sample NB-IoT based application.
By the end of this training, participants will be able to:
- Identify the different components of NB-IoT and how to fit together to form an ecosystem.
- Understand and explain the security features built into NB-IoT devices.
- Develop a simple application to track NB-IoT devices.
Setting Up an IoT Gateway with ThingsBoard
35 HoursThingsBoard is an open source IoT platform that offers device management, data collection, processing and visualization for your IoT solution.
In this instructor-led, live training, participants will learn how to integrate ThingsBoard into their IoT solutions.
By the end of this training, participants will be able to:
- Install and configure ThingsBoard
- Understand the fundamentals of ThingsBoard features and architecture
- Build IoT applications with ThingsBoard
- Integrate ThingsBoard with Kafka for telemetry device data routing
- Integrate ThingsBoard with Apache Spark for data aggregation from multiple devices
Audience
- Software engineers
- Hardware engineers
- Developers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Note
- To request a customized training for this course, please contact us to arrange.
Introduction to TinyML
14 HoursThis instructor-led, live training in Indonesia (online or onsite) is aimed at beginner-level engineers and data scientists who wish to understand TinyML fundamentals, explore its applications, and deploy AI models on microcontrollers.
By the end of this training, participants will be able to:
- Understand the fundamentals of TinyML and its significance.
- Deploy lightweight AI models on microcontrollers and edge devices.
- Optimize and fine-tune machine learning models for low-power consumption.
- Apply TinyML for real-world applications such as gesture recognition, anomaly detection, and audio processing.
TinyML: Running AI on Ultra-Low-Power Edge Devices
21 HoursThis instructor-led, live training in Indonesia (online or onsite) is aimed at intermediate-level embedded engineers, IoT developers, and AI researchers who wish to implement TinyML techniques for AI-powered applications on energy-efficient hardware.
By the end of this training, participants will be able to:
- Understand the fundamentals of TinyML and edge AI.
- Deploy lightweight AI models on microcontrollers.
- Optimize AI inference for low-power consumption.
- Integrate TinyML with real-world IoT applications.