CANN for Edge AI Deployment Training Course

This instructor-led, live training in Italy (online or onsite) is designed for intermediate-level telecom professionals, AI engineers, and IoT specialists eager to explore how 5G networks enhance Edge AI applications.

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

• Grasp the fundamentals of 5G technology and its influence on Edge AI.
• Deploy AI models tailored for low-latency applications within 5G environments.
• Implement real-time decision-making systems leveraging Edge AI and 5G connectivity.
• Optimize AI workloads to ensure efficient performance on edge devices.

6G and the Intelligent Edge offers a forward-looking exploration into how 6G wireless technologies integrate with edge computing, IoT ecosystems, and AI-driven data processing to build intelligent, low-latency, and adaptive infrastructures.

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

After completing this course, participants will be able to:

• Grasp how 6G will transform edge computing and IoT architectures.
• Design distributed systems capable of ultra-low latency, high bandwidth, and autonomous operations.
• Integrate AI and data analytics at the edge to enable intelligent decision-making.
• Plan scalable, secure, and resilient infrastructures ready for 6G.
• Evaluate the business and operational models enabled by the convergence of 6G and edge technologies.

Format of the Course

• Interactive lectures and discussions.
• Case studies and applied architecture design exercises.
• Hands-on simulation using optional edge or container tools.

Course Customization Options

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

This instructor-led, live training in Italy (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.

Huawei Ascend comprises a family of AI processors engineered for high-performance inference and training tasks.

This instructor-led live training, available online or onsite, targets intermediate-level AI engineers and data scientists eager to develop and optimize neural network models leveraging Huawei’s Ascend platform alongside the CANN toolkit.

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

• Establish and configure the CANN development environment.
• Create AI applications utilizing MindSpore and CloudMatrix workflows.
• Enhance performance on Ascend NPUs through custom operators and tiling techniques.
• Deploy models within either edge or cloud environments.

Course Format

• Interactive lectures and discussions.
• Practical application of Huawei Ascend and the CANN toolkit within sample projects.
• Guided exercises concentrating on model construction, training, and deployment.

Course Customization Options

• To arrange a tailored training session aligned with your specific infrastructure or datasets, please reach out to us.

CANN (Compute Architecture for Neural Networks) serves as Huawei's AI compute stack, designed to deploy and optimize AI models on Ascend AI processors.

This instructor-led live training, available both online and on-site, targets intermediate-level AI developers and engineers who want to efficiently deploy trained AI models onto Huawei Ascend hardware. The course utilizes the CANN toolkit along with tools such as MindSpore, TensorFlow, or PyTorch.

Upon completing this training, participants will be capable of:

• Understanding the CANN architecture and its function within the AI deployment pipeline.
• Converting and adapting models from popular frameworks into Ascend-compatible formats.
• Utilizing tools like ATC, OM model conversion, and MindSpore for inference on both edge and cloud environments.
• Diagnosing deployment issues and optimizing performance on Ascend hardware.

Course Format

• Interactive lectures and demonstrations.
• Hands-on lab exercises using CANN tools and Ascend simulators or devices.
• Practical deployment scenarios based on real-world AI models.

Course Customization Options

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

This instructor-led live training in Italy (online or onsite) is designed for intermediate developers, data scientists, and technology enthusiasts who want to gain practical skills in deploying AI models on edge devices for diverse applications.

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

• Grasp the core principles of Edge AI and its advantages.
• Configure and set up the edge computing environment.
• Develop, train, and optimize AI models specifically for edge deployment.
• Implement functional AI solutions on edge devices.
• Assess and enhance the performance of models deployed at the edge.
• Tackle ethical and security aspects related to Edge AI applications.

This instructor-led, live training in Italy (online or onsite) is designed for advanced cybersecurity professionals, AI engineers, and IoT developers seeking to implement robust security measures and resilience strategies for Edge AI systems.

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

• Comprehend the security risks and vulnerabilities associated with Edge AI deployments.
• Apply encryption and authentication techniques to protect data.
• Architect resilient Edge AI systems capable of withstanding cyber threats.
• Execute secure AI model deployment strategies within edge environments.

Cambricon MLUs (Machine Learning Units) are specialized artificial intelligence chips designed to optimize both inference and training processes for edge computing and data center environments.

This instructor-led live training, available either online or on-site, is designed for intermediate-level developers aiming to build and deploy AI models utilizing the BANGPy framework and Neuware SDK on Cambricon MLU hardware.

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

• Establish and configure development environments for both BANGPy and Neuware.
• Create and optimize models based on Python and C++ specifically for Cambricon MLUs.
• Deploy models to edge devices and data centers operating on the Neuware runtime.
• Integrate machine learning workflows with acceleration features specific to MLUs.

Course Format

• Engaging lectures combined with interactive discussions.
• Practical, hands-on experience with BANGPy and Neuware for development and deployment.
• Guided exercises emphasizing optimization, integration, and testing.

Customization Options

• To arrange customized training tailored to your specific Cambricon device model or use case, please contact us.

CANN (Compute Architecture for Neural Networks) is Huawei’s AI computing toolkit designed to compile, optimize, and deploy AI models on Ascend AI processors.

This instructor-led live training, available online or onsite, targets beginner-level AI developers seeking to comprehend how CANN integrates into the model lifecycle—from training through to deployment—and how it interoperates with popular frameworks such as MindSpore, TensorFlow, and PyTorch.

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

• Grasping the purpose and architectural design of the CANN toolkit.
• Establishing a development environment utilizing CANN and MindSpore.
• Converting and deploying a simple AI model to Ascend hardware.
• Acquiring foundational knowledge to support future CANN optimization or integration initiatives.

Course Format

• Interactive lectures and discussions.
• Practical hands-on labs focused on simple model deployment.
• A step-by-step walkthrough of the CANN toolchain and its integration points.

Customization Options

• To request customized training for this course, please reach out to us to arrange details.

Huawei’s AI stack—spanning from the low-level CANN SDK to the high-level MindSpore framework—provides a tightly integrated environment for AI development and deployment, specifically optimized for Ascend hardware.

This instructor-led live training (available online or onsite) targets technical professionals at the beginner to intermediate level who aim to grasp how CANN and MindSpore components collaborate to facilitate AI lifecycle management and infrastructure planning.

Upon completing this training, participants will be able to:

• Comprehend the layered architecture of Huawei’s AI compute stack.
• Recognize how CANN facilitates model optimization and hardware-level deployment.
• Assess the MindSpore framework and its toolchain against industry alternatives.
• Position Huawei's AI stack effectively within enterprise or cloud/on-premise environments.

Course Format

• Interactive lectures and discussions.
• Live system demonstrations and case-based walkthroughs.
• Optional guided labs exploring the model workflow from MindSpore to CANN.

Customization Options

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

The CANN SDK (Compute Architecture for Neural Networks) serves as Huawei’s foundational AI compute platform, empowering developers to fine-tune and enhance the performance of neural networks deployed on Ascend AI processors.

This instructor-led live training, available online or onsite, targets advanced AI developers and system engineers seeking to maximize inference performance by leveraging CANN’s advanced tools, such as the Graph Engine, TIK, and custom operator development capabilities.

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

• Gaining insight into CANN's runtime architecture and performance lifecycle.
• Utilizing profiling tools and the Graph Engine to analyze and optimize performance.
• Developing and optimizing custom operators using TIK and TVM.
• Addressing memory bottlenecks and increasing model throughput.

Course Format

• Interactive lectures and discussions.
• Practical labs involving real-time profiling and operator tuning.
• Optimization exercises based on real-world edge-case deployment scenarios.

Customization Options

• For customized training arrangements, please contact us directly.

The CANN SDK (Compute Architecture for Neural Networks) offers robust tools for deploying and optimizing real-time AI solutions in computer vision and natural language processing, particularly on Huawei Ascend hardware.

This instructor-led live training, available either online or on-site, is designed for intermediate-level AI professionals looking to construct, deploy, and refine vision and language models using the CANN SDK for production environments.

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

• Deploy and optimize computer vision (CV) and NLP models utilizing CANN and AscendCL.
• Leverage CANN utilities to convert models and embed them into live processing pipelines.
• Enhance inference performance for applications such as object detection, classification, and sentiment analysis.
• Create real-time CV/NLP pipelines suitable for both edge and cloud deployment scenarios.

Course Format

• Engaging lectures combined with practical demonstrations.
• Practical hands-on labs focused on model deployment and performance profiling.
• Real-time pipeline design using authentic CV and NLP use cases.

Customization Options

• For personalized training needs regarding this course, please reach out to us to arrange.

CANN TIK (Tensor Instruction Kernel) and Apache TVM facilitate the advanced optimization and customization of AI model operators for Huawei Ascend hardware.

This instructor-led, live training (available online or onsite) is designed for advanced-level system developers who want to build, deploy, and fine-tune custom operators for AI models using CANN’s TIK programming model and its integration with the TVM compiler.

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

• Writing and testing custom AI operators utilizing the TIK DSL for Ascend processors.
• Integrating custom operations into the CANN runtime and execution graph.
• Leveraging TVM for operator scheduling, auto-tuning, and benchmarking.
• Debugging and optimizing instruction-level performance for custom computation patterns.

Course Format

• Interactive lectures and demonstrations.
• Hands-on coding of operators using TIK and TVM pipelines.
• Testing and tuning on Ascend hardware or simulators.

Course Customization Options

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

Combine the transformative power of AI with the agility of edge computing in this comprehensive course. 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 to optimize AI performance for real-time processing and decision-making at the edge.

This instructor-led, live training in Italy (online or onsite) is designed for beginner to intermediate-level agritech professionals, IoT specialists, and AI engineers looking 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.