Edge AI for Agriculture: Smart Farming and Precision Monitoring Training Course

The 6G and the Intelligent Edge is a forward-looking course that explores the integration of 6G wireless technologies with edge computing, IoT ecosystems, and AI-driven data processing to support intelligent, low-latency, and adaptive infrastructures for government.

This instructor-led, live training (online or onsite) is designed for intermediate-level IT architects who wish to understand and design next-generation distributed architectures leveraging the synergy of 6G connectivity and intelligent edge systems.

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

• Understand how 6G will transform edge computing and IoT architectures for government applications.
• Design distributed systems for ultra-low latency, high bandwidth, and autonomous operations in public sector environments.
• Integrate AI and data analytics at the edge to support intelligent decision-making for government services.
• Plan scalable, secure, and resilient 6G-ready edge infrastructures that meet government standards.
• Evaluate business and operational models enabled by 6G-edge convergence in a public sector context.

Format of the Course

• Interactive lectures and discussions tailored to government IT professionals.
• Case studies and applied architecture design exercises relevant to government projects.
• Hands-on simulation with optional edge or container tools, adapted for government use cases.

Course Customization Options

• To request a customized training for this course tailored to specific government needs, please contact us to arrange.

This instructor-led, live training (online or onsite) is designed for government and 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 the 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.

Cambricon MLUs (Machine Learning Units) are specialized AI chips designed for optimizing inference and training in both edge and data center environments.

This instructor-led, live training (available online or onsite) is tailored for intermediate-level developers who aim to construct and deploy AI models using the BANGPy framework and Neuware SDK on Cambricon MLU hardware.

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

• Set up and configure the BANGPy and Neuware development environments for government applications.
• Develop and optimize Python- and C++-based models for deployment on Cambricon MLUs.
• Deploy models to edge and data center devices running the Neuware runtime.
• Integrate machine learning workflows with MLU-specific acceleration features to enhance performance.

Format of the Course

• Interactive lecture and discussion sessions.
• Hands-on practice using BANGPy and Neuware for development and deployment tasks.
• Guided exercises focused on optimization, integration, and testing to ensure robust model performance.

Course Customization Options

• To request a customized training for this course based on specific Cambricon device models or use cases, please contact us to arrange.

The Huawei Ascend CANN toolkit facilitates robust AI inference on edge devices such as the Ascend 310. CANN provides critical tools for compiling, optimizing, and deploying models in environments with limited compute and memory resources.

This instructor-led, live training (online or onsite) is designed for intermediate-level AI developers and integrators who aim to deploy and optimize models on Ascend edge devices using the CANN toolchain.

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

• Prepare and convert AI models for deployment on the Ascend 310 using CANN tools.
• Construct lightweight inference pipelines utilizing MindSpore Lite and AscendCL.
• Enhance model performance in compute- and memory-constrained environments.
• Deploy and monitor AI applications in practical edge scenarios.

Format of the Course

• Interactive lecture and demonstration.
• Hands-on lab work with edge-specific models and scenarios.
• Live deployment examples on virtual or physical edge hardware.

Course Customization Options for Government

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

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.