TinyML for IoT Applications Training Course

This advanced course delves into the innovative realm of edge computing, providing a comprehensive exploration of sophisticated architectures and addressing integration challenges. Participants will gain the necessary skills to leverage the full potential of edge computing in various government environments. The curriculum focuses on developing expertise in cutting-edge tools and methodologies to deploy, manage, and optimize edge computing solutions that meet specific public sector needs for government.

This instructor-led, live training in US (online or onsite) is designed for intermediate-level IT professionals and business managers who seek to understand the potential of IoT and edge computing for enhancing efficiency, enabling real-time processing, and fostering innovation across various industries.

By the end of this training, participants will be able to:

• Comprehend the principles of IoT and edge computing and their significance in digital transformation for government and private sectors.
• Identify use cases for IoT and edge computing in manufacturing, logistics, and energy sectors, including applications for government operations.
• Differentiate between edge and cloud computing architectures and deployment scenarios suitable for government and enterprise environments.
• Implement edge computing solutions to support predictive maintenance and real-time decision-making in both public and private sector contexts.

This comprehensive course integrates the transformative power of artificial intelligence (AI) with the agility of edge computing. Participants will learn to deploy AI models directly on edge devices, covering topics from understanding convolutional neural network (CNN) architectures to mastering knowledge distillation and federated learning. This hands-on training is designed to equip professionals with the skills necessary to optimize AI performance for real-time processing and decision-making at the edge, ensuring alignment with public sector workflows and governance for government applications.

This instructor-led, live training (online or onsite) is designed for intermediate-level developers, system architects, and industry professionals who aim to leverage Edge AI to enhance IoT applications with advanced data processing and analytics capabilities.

By the end of this training, participants will be able to:

• Comprehend the core principles of Edge AI and its application in IoT systems.
• Establish and configure Edge AI environments for IoT devices.
• Create and deploy AI models on edge devices to support IoT applications.
• Execute real-time data processing and decision-making within IoT frameworks.
• Integrate Edge AI with a variety of IoT protocols and platforms.
• Address ethical considerations and best practices in the deployment of Edge AI for government and industry use cases.

This instructor-led, live training (online or onsite) is aimed at product managers and developers who wish to utilize Edge Computing to decentralize data management for enhanced performance, leveraging smart devices located at the source network. This training aligns with public sector workflows and governance practices for government. By the end of this training, participants will be able to: - Understand the fundamental concepts and benefits of Edge Computing. - Identify use cases and examples where Edge Computing can be effectively applied. - Design and implement Edge Computing solutions to accelerate data processing and reduce operational costs.

Establish a robust foundation in designing and managing a resilient edge computing infrastructure for government. Gain insights into open hybrid cloud infrastructures, the management of workloads across multiple clouds, and the importance of flexibility and redundancy. This training offers critical knowledge on developing a scalable and secure infrastructure that supports the evolving needs of modern applications through edge computing.

This instructor-led, live training (available online or onsite) is designed for intermediate-level professionals who wish to apply Federated Learning to enhance IoT and edge computing solutions for government. By the end of this training, participants will be able to: - Understand the principles and benefits of Federated Learning in IoT and edge computing environments. - Implement Federated Learning models on IoT devices to enable decentralized AI processing. - Reduce latency and improve real-time decision-making capabilities in edge computing scenarios. - Address challenges related to data privacy and network constraints in government IoT systems.

This instructor-led, live training (online or onsite) is designed for intermediate-level embedded systems engineers and AI developers who are interested in deploying machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse for government applications. 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 suitable for TinyML projects. - Train, optimize, and deploy AI models on low-power microcontrollers. - Utilize TensorFlow Lite and Edge Impulse to implement practical TinyML applications. - Optimize AI models to meet power efficiency and memory constraints.

In this instructor-led, live training, participants will gain a comprehensive understanding of NB-IoT (also known as LTE Cat NB1) and develop and deploy a sample NB-IoT-based application. This training is designed to align with the needs and standards for government. By the end of this training, participants will be able to: - Identify the various components of NB-IoT and understand how they integrate to form an ecosystem. - Understand and articulate the security features inherent in NB-IoT devices. - Develop a simple application to track NB-IoT devices, enhancing operational efficiency and accountability for government.

TinyML is the practice of deploying machine learning models on highly resource-constrained hardware. This instructor-led, live training (online or onsite) is aimed at advanced-level practitioners who wish to optimize TinyML models for low-latency, memory-efficient deployment on embedded devices. Upon completing this training, participants will be able to: - Apply quantization, pruning, and compression techniques to reduce model size without sacrificing accuracy. - Benchmark TinyML models for latency, memory consumption, and energy efficiency. - Implement optimized inference pipelines on microcontrollers and edge devices. - Evaluate trade-offs between performance, accuracy, and hardware constraints. **Format of the Course** - Instructor-led presentations supported by technical demonstrations. - Practical optimization exercises and comparative performance testing. - Hands-on implementation of TinyML pipelines in a controlled lab environment. **Course Customization Options for Government** For tailored training aligned with specific hardware platforms or internal workflows, please contact us to customize the program.

ThingsBoard is an open-source IoT platform designed to offer comprehensive device management, data collection, processing, and visualization capabilities for your IoT solutions.

In this instructor-led, live training, participants will learn how to integrate ThingsBoard into their IoT projects for government use.

By the end of this training, participants will be able to:

• Install and configure ThingsBoard
• Understand the core features and architecture of ThingsBoard
• Develop IoT applications using ThingsBoard
• Integrate ThingsBoard with Kafka for efficient telemetry data routing from devices
• Integrate ThingsBoard with Apache Spark to aggregate and analyze data from multiple devices

Audience

• Software engineers
• Hardware engineers
• Developers

Format of the Course

• Part lecture, part discussion, with exercises and extensive hands-on practice

Note

• To request a customized training for government use, please contact us to arrange.

This instructor-led, live training (online or onsite) is designed for government engineers and data scientists at the beginner level who wish to gain a foundational understanding of TinyML, 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 for government operations. - Deploy lightweight AI models on microcontrollers and edge devices used in public sector workflows. - Optimize and fine-tune machine learning models for low-power consumption, ensuring efficiency in resource-constrained environments. - Apply TinyML for real-world applications such as gesture recognition, anomaly detection, and audio processing to enhance governance and accountability.

This instructor-led, live training in [location] (online or onsite) is aimed at intermediate-level embedded engineers, IoT developers, and AI researchers who wish to implement TinyML techniques for government 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.

TinyML is the integration of machine learning into low-power, resource-limited wearable and medical devices for government and public sector applications. This instructor-led, live training (online or onsite) is aimed at intermediate-level practitioners who wish to implement TinyML solutions for healthcare monitoring and diagnostic applications. After completing this training, participants will be able to: - Design and deploy TinyML models for real-time health data processing. - Collect, preprocess, and interpret biosensor data for AI-driven insights. - Optimize models for low-power and memory-constrained wearable devices. - Evaluate the clinical relevance, reliability, and safety of TinyML-driven outputs. **Format of the Course** - Lectures supported by live demonstrations and interactive discussion. - Hands-on practice with wearable device data and TinyML frameworks. - Implementation exercises in a guided lab environment. **Course Customization Options** - For tailored training that aligns with specific healthcare devices or regulatory workflows for government, please contact us to customize the program.

TinyML is a framework designed for deploying machine learning models on low-power, resource-constrained devices in the field. This instructor-led, live training (online or onsite) is tailored for intermediate-level professionals who wish to apply TinyML techniques to smart agriculture solutions that enhance automation and environmental intelligence. Upon completing this program, participants will gain the ability to: - Build and deploy TinyML models for agricultural sensing applications. - Integrate edge AI into IoT ecosystems for automated crop monitoring. - Use specialized tools to train and optimize lightweight models. - Develop workflows for precision irrigation, pest detection, and environmental analytics. **Format of the Course** - Guided presentations and applied technical discussions. - Hands-on practice using real-world datasets and devices. - Practical experimentation in a supported lab environment. **Course Customization Options** - For tailored training aligned with specific agricultural systems or requirements for government projects, please contact us to customize the program.