CANN for Edge AI Deployment Training Course

This instructor-led, live training in [location] (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 for government. 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.

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.

The Huawei Ascend family of AI processors is designed for high-performance inference and training applications.

This instructor-led, live training (online or onsite) is aimed at intermediate-level AI engineers and data scientists who wish to develop and optimize neural network models using Huawei’s Ascend platform and the CANN toolkit. The course is tailored to align with public sector workflows, governance, and accountability for government.

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

• Set up and configure the CANN development environment.
• Develop AI applications using MindSpore and CloudMatrix workflows.
• Optimize performance on Ascend NPUs using custom operators and tiling techniques.
• Deploy models to edge or cloud environments, ensuring compliance with government standards.

Format of the Course

• Interactive lecture and discussion sessions.
• Hands-on use of Huawei Ascend and the CANN toolkit in sample applications relevant to government operations.
• Guided exercises focused on model building, training, and deployment within a governmental context.

Course Customization Options

• To request a customized training for this course based on your specific infrastructure or datasets, please contact us to arrange. We can tailor the content to meet the unique needs of government agencies.

The Compute Architecture for Neural Networks (CANN) is Huawei’s AI computing stack designed for deploying and optimizing AI models on Ascend AI processors. This instructor-led, live training (online or onsite) is aimed at intermediate-level AI developers and engineers who wish to deploy trained AI models efficiently to Huawei Ascend hardware using the CANN toolkit and tools such as MindSpore, TensorFlow, or PyTorch. By the end of this training, participants will be able to: - Understand the CANN architecture and its role in the AI deployment pipeline. - Convert and adapt models from popular frameworks to Ascend-compatible formats. - Utilize tools like ATC, OM model conversion, and MindSpore for edge and cloud inference. - Diagnose deployment issues and optimize performance on Ascend hardware. **Format of the Course** - Interactive lecture and demonstration. - Hands-on lab work using CANN tools and Ascend simulators or devices. - Practical deployment scenarios based on real-world AI models. **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, data scientists, and technology enthusiasts who seek to acquire practical skills in deploying artificial intelligence (AI) models on edge devices for a variety of 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 deployment on edge devices. - Implement practical AI solutions on edge devices. - Evaluate and enhance the performance of models deployed at the edge. - Address ethical and security considerations in Edge AI applications. This training is tailored to align with public sector workflows, governance, and accountability, ensuring that participants are well-equipped to apply these skills effectively for government projects.

This instructor-led, live training (available online or onsite) is designed for advanced-level cybersecurity professionals, AI engineers, and IoT developers who aim to implement robust security measures and resilience strategies for Edge AI systems. By the end of this training, participants will be able to: - Understand the security risks and vulnerabilities associated with Edge AI deployments. - Implement encryption and authentication methods to protect data. - Design resilient Edge AI architectures capable of withstanding cyber threats. - Apply secure deployment strategies for AI models in edge environments, ensuring alignment with public sector workflows and governance for government.

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 Compute Architecture for Neural Networks (CANN) is Huawei’s AI computing toolkit designed for compiling, optimizing, and deploying AI models on Ascend AI processors.

This instructor-led, live training (available online or onsite) is aimed at beginner-level AI developers who wish to understand how CANN fits into the model lifecycle from training to deployment, and how it integrates with frameworks such as MindSpore, TensorFlow, and PyTorch.

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

• Understand the purpose and architecture of the CANN toolkit.
• Set up a development environment with CANN and MindSpore.
• Convert and deploy a simple AI model to Ascend hardware.
• Gain foundational knowledge for future CANN optimization or integration projects, including those for government applications.

Format of the Course

• Interactive lecture and discussion.
• Hands-on labs with simple model deployment.
• Step-by-step walkthrough of the CANN toolchain and integration points.

Course Customization Options

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

Huawei’s AI stack — from the low-level CANN SDK to the high-level MindSpore framework — provides a tightly integrated environment optimized for Ascend hardware, designed to support efficient AI development and deployment. This instructor-led, live training (online or on-site) is aimed at technical professionals at beginner to intermediate levels who wish to gain a comprehensive understanding of how the CANN and MindSpore components work together to facilitate AI lifecycle management and infrastructure decisions. By the end of this training, participants will be able to: - Understand the layered architecture of Huawei’s AI compute stack for government. - Identify how CANN supports model optimization and hardware-level deployment in various environments. - Evaluate the MindSpore framework and toolchain in comparison to industry alternatives. - Position Huawei's AI stack within enterprise or cloud/on-premises environments, ensuring alignment with public sector workflows and governance. **Format of the Course** - Interactive lecture and discussion. - Live system demonstrations and case-based walkthroughs. - Optional guided labs on model flow from MindSpore to CANN. **Course Customization Options** - To request a customized training for this course, please contact us to arrange.

The CANN SDK (Compute Architecture for Neural Networks) is Huawei’s AI computing foundation designed to enable developers to fine-tune and optimize the performance of deployed neural networks on Ascend AI processors. This instructor-led, live training (available online or onsite) is targeted at advanced-level AI developers and system engineers who seek to enhance inference performance using CANN’s comprehensive toolset, which includes the Graph Engine, TIK, and custom operator development. By the end of this training, participants will be able to: - Understand the runtime architecture and performance lifecycle of CANN. - Utilize profiling tools and the Graph Engine for performance analysis and optimization. - Develop and optimize custom operators using TIK and TVM. - Address memory bottlenecks and improve model throughput. **Format of the Course:** - Interactive lecture and discussion. - Hands-on labs with real-time profiling and operator tuning. - Optimization exercises using edge-case deployment examples. **Course Customization Options:** - To request a customized training for government or other specific needs, please contact us to arrange.

The CANN SDK (Compute Architecture for Neural Networks) provides robust deployment and optimization tools for real-time AI applications in computer vision and natural language processing, particularly on Huawei Ascend hardware. This instructor-led, live training (online or onsite) is designed for intermediate-level AI practitioners who aim to build, deploy, and optimize vision and language models using the CANN SDK for production use cases for government. By the end of this training, participants will be able to: - Deploy and optimize computer vision and natural language processing models using CANN and AscendCL. - Utilize CANN tools to convert models and integrate them into live pipelines. - Enhance inference performance for tasks such as detection, classification, and sentiment analysis. - Construct real-time computer vision and natural language processing pipelines suitable for edge or cloud-based deployment scenarios. **Format of the Course** - Interactive lecture and demonstration. - Hands-on laboratory sessions with model deployment and performance profiling. - Live pipeline design using real-world computer vision and natural language processing use cases. **Course Customization Options** - To request a customized training for this course, please contact us to arrange.

CANN TIK (Tensor Instruction Kernel) and Apache TVM facilitate advanced optimization and customization of AI model operators for Huawei Ascend hardware. This instructor-led, live training (online or onsite) is designed for advanced-level system developers who aim to build, deploy, and tune custom operators for AI models using CANN’s TIK programming model and TVM compiler integration. By the end of this training, participants will be able to: - Write and test custom AI operators using the TIK DSL for Ascend processors. - Integrate custom operators into the CANN runtime and execution graph. - Use TVM for operator scheduling, auto-tuning, and benchmarking. - Debug and optimize instruction-level performance for custom computation patterns. **Format of the Course** - Interactive lecture and demonstration. - Hands-on coding of operators using TIK and TVM pipelines. - Testing and tuning on Ascend hardware or simulators. **Course Customization Options** - To request a customized training for government, please contact us to arrange.

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 in [location] (online or onsite) is aimed at beginner to intermediate agritech professionals, IoT specialists, and AI engineers who wish to develop and deploy Edge AI solutions for smart farming for government. 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.