Introduction to IoT Using Raspberry Pi Training Course

This training aims to clarify what 5G networks are and their influence on smart technologies. We will explore both the advantages and disadvantages of the relationship between these technologies (5G/IoT), as well as the developmental trajectories of networks originally designed for the smart world.

Positioned to revolutionize IoT ecosystems via ultra-fast connectivity, advanced sensing, and integrated AI capabilities, 6G represents the next-generation wireless communication standard.

Designed for advanced-level participants eager to understand and harness the emerging intersection of 6G technologies and IoT applications, this instructor-led training is available both online and onsite.

Upon completing this course, learners will be able to:

• Articulate the core technical concepts underlying 6G.
• Analyze how 6G will transform IoT device communication and architecture.
• Assess 6G-enabled IoT use cases across various industries.
• Develop strategies for integrating 6G capabilities into existing IoT solutions.

Course Format

• Concept-focused lectures paired with expert discussions.
• Practical exercises designed to reinforce key engineering principles.
• Guided exploration of cases and scenario analysis.

Customization Options

• For customized training aligned with your organization's technology roadmap, please contact us to arrange.

Dive deeper into the innovative realm of edge computing with this advanced course. Explore sophisticated architectures and tackle integration challenges, preparing to leverage the full potential of edge computing across diverse business environments. Gain expertise in cutting-edge tools and methodologies to deploy, manage, and optimize edge computing solutions that meet specific industry needs.

Technological advancements and the exponential growth of information are fundamentally transforming business operations across various sectors, including government. The volume of government data and digital archiving is increasing rapidly, driven by the proliferation of mobile devices and applications, smart sensors and IoT devices, cloud computing solutions, and citizen-facing portals. As digital information becomes more expansive and complex, the management, processing, storage, security, and disposition of this data become increasingly challenging. New tools for capture, search, discovery, and analysis are enabling organizations to extract valuable insights from unstructured data. The government sector is at a critical tipping point, recognizing information as a strategic asset. Governments must now protect, leverage, and analyze both structured and unstructured data to better serve the public and meet mission requirements. As government leaders work to evolve into data-driven organizations, they are establishing the foundation to correlate dependencies across events, people, processes, and information.

High-impact government solutions will emerge from the integration of the most disruptive technologies:

• Mobile devices and applications
• Cloud services
• Social business technologies and networking
• Big Data and analytics

Big Data represents a key intelligent industry solution, enabling governments to make better decisions by acting on patterns revealed through the analysis of large volumes of related and unrelated, structured and unstructured data.

However, achieving these outcomes requires more than just accumulating massive amounts of data. "Making sense of these volumes of Big Data requires cutting-edge tools and technologies that can analyze and extract useful knowledge from vast and diverse streams of information," noted Tom Kalil and Fen Zhao from the White House Office of Science and Technology Policy in a post on the OSTP Blog.

The White House took a significant step toward helping agencies identify these technologies by establishing the National Big Data Research and Development Initiative in 2012. This initiative allocated over $200 million to maximize the potential of the Big Data explosion and the necessary analytical tools.

The challenges posed by Big Data are nearly as formidable as the promise it offers. One major challenge is efficient data storage. With budgets often tight, agencies must minimize storage costs per megabyte while ensuring data remains easily accessible for users to retrieve when and how they need it. Backing up such massive data volumes adds another layer of complexity.

Effective data analysis presents another significant challenge. Many agencies utilize commercial tools to sift through vast amounts of data, identifying trends that enhance operational efficiency. A recent MeriTalk study revealed that federal IT executives believe Big Data could help agencies save over $500 billion while also fulfilling mission objectives.

Custom-developed Big Data tools are also empowering agencies to meet their analytical needs. For instance, the Computational Data Analytics Group at Oak Ridge National Laboratory has made its Piranha data analytics system available to other agencies. This system has assisted medical researchers in identifying links that alert doctors to aortic aneurysms before they occur. It is also employed for more routine tasks, such as screening resumes to match job candidates with hiring managers.

Insurtech (also known as Digital Insurance) refers to the convergence of insurance and emerging technologies. Within the Insurtech domain, "digital insurers" leverage technological innovations in their business and operational models to reduce costs, enhance customer experience, and improve operational agility.

In this instructor-led training, participants will gain a comprehensive understanding of the technologies, methods, and mindset required to drive digital transformation within their organizations and across the broader insurance industry. The training is designed for managers who need to grasp the big picture, cut through the hype and jargon, and take the first concrete steps toward establishing an effective Insurtech strategy.

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

• Discuss Insurtech and its components in a structured and intelligent manner
• Identify and clarify the role of each key technology within the Insurtech ecosystem
• Draft a general strategy for implementing Insurtech initiatives within their organization

Audience

• Insurers
• Technology professionals within the insurance industry
• Insurance stakeholders
• Consultants and business analysts

Course Format

• A blend of lectures, group discussions, practical exercises, and case study activities

This instructor-led, live training in Uzbekistan (online or onsite) is designed for intermediate IT professionals and business managers who wish to understand how IoT and edge computing can drive efficiency, real-time processing, and innovation across various industries.

Upon completion of this training, participants will be able to:

• Understand the core principles of IoT and edge computing and their role in digital transformation.
• Identify use cases for IoT and edge computing in the 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.

In this comprehensive course, combine the transformative potential of AI with the agility of edge computing. Learn to deploy AI models directly on edge devices, from understanding CNN architectures to mastering knowledge distillation and federated learning. This hands-on training will equip you with the skills needed to optimise AI performance for real-time processing and decision-making at the edge.

This instructor-led, live training in Uzbekistan (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.

This instructor-led, live training in Uzbekistan (available online or on-site) is designed for product managers and developers who aim to leverage Edge Computing to decentralize data management, thereby achieving faster performance through the use of smart devices deployed directly on the source network.

Upon completing this training, participants will be able to:

• Grasp the fundamental concepts and key advantages of Edge Computing.
• Recognize practical use cases and real-world examples where Edge Computing can be effectively applied.
• Design and implement Edge Computing solutions that enable faster data processing while reducing operational costs.

Build a strong foundation in designing and managing a resilient edge computing infrastructure. Learn about open hybrid cloud infrastructures, managing workloads across diverse clouds, and ensuring flexibility and redundancy. This training provides essential knowledge on creating a scalable and secure infrastructure that supports the dynamic needs of modern applications with edge computing.

This instructor-led, live training in Uzbekistan (available online or on-site) is designed for intermediate-level professionals seeking to leverage Federated Learning to optimize IoT and edge computing solutions.

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

• Grasp the core principles and advantages of Federated Learning in IoT and edge computing.
• Deploy Federated Learning models on IoT devices to enable decentralized AI processing.
• Minimize latency and enhance real-time decision-making capabilities in edge computing environments.
• Tackle challenges related to data privacy and network constraints within IoT systems.

The Internet of Things (IoT) refers to a network infrastructure that wirelessly links physical objects with software applications, enabling them to communicate and exchange data through network communications, cloud computing, and data capture mechanisms. Python, a high-level programming language, is highly recommended for IoT development due to its clear syntax and robust community support.

In this instructor-led live training, participants will gain practical skills in programming IoT solutions using Python.

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

• Grasp the fundamentals of IoT architecture
• Master the basics of using Raspberry Pi
• Install and configure Python on a Raspberry Pi device
• Identify the advantages of utilizing Python for programming IoT systems
• Build, test, deploy, and troubleshoot an IoT system using Python and Raspberry Pi

Audience

• Developers
• Engineers

Format of the course

• A blend of lectures, discussions, exercises, and extensive hands-on practice

Note

• To request customized training for this course, please contact us to make arrangements.

In this instructor-led live training in Uzbekistan, participants will explore the various aspects of NB-IoT (also known as LTE Cat NB1) while developing and deploying 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 understand how they integrate to form a complete ecosystem.
• Understand and explain the security features built into NB-IoT devices.
• Develop a simple application to track NB-IoT devices.

The Raspberry Pi is an extremely compact, single-board computer.

In this instructor-led, live training session, attendees will learn how to configure and program the Raspberry Pi to function as an interactive and potent embedded system.

Upon completion of this training, participants will be capable of:

• Configuring an IDE (integrated development environment) to optimize development productivity.
• Programming the Raspberry Pi to manage devices such as motion sensors, alarms, web servers, and printers.
• Gaining a comprehensive understanding of the Raspberry Pi's architecture, including input and connector options for add-on devices.
• Evaluating various programming language and operating system choices available for the platform.
• Testing, debugging, and deploying the Raspberry Pi to address real-world challenges.

Audience

• Developers
• Hardware/software technicians
• Technical professionals across all industries
• Hobbyists

Course Format

• A blend of lectures, discussions, exercises, and extensive hands-on practice.

Important Note

• The Raspberry Pi is compatible with various operating systems and programming languages. This course utilizes Linux-based Raspbian as the operating system and Python as the programming language. For alternative setups, please contact us to arrange.
• Participants are responsible for purchasing their own Raspberry Pi hardware and components.

ThingsBoard is an open-source IoT platform that provides 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 using 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

Course format

• A combination of lectures, discussions, exercises, and extensive hands-on practice

Note

• To request customized training for this course, please contact us to arrange.