CANN for Edge AI Deployment Training Course

This instructor-led live training in Bulgaria (online or onsite) targets intermediate-level telecom professionals, AI engineers, and IoT specialists interested in exploring the role of 5G networks in accelerating Edge AI applications.

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' course offers a forward-looking perspective on integrating 6G wireless technologies with edge computing, IoT ecosystems, and AI-driven data processing to enable intelligent, adaptive, and low-latency infrastructures.

This instructor-led training, available online or onsite, targets intermediate-level IT architects seeking to grasp and design next-generation distributed architectures that leverage the synergy between 6G connectivity and intelligent edge systems.

Upon completing this course, participants will be able to:

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

Course Format

• Interactive lectures and discussions.
• Case studies and applied architecture design exercises.
• Hands-on simulation with 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 Bulgaria (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 series of AI processors engineered for high-efficiency inference and training tasks.

This instructor-led live training, available either online or at your location, targets intermediate-level AI engineers and data scientists seeking to create and optimize neural network models via Huawei’s Ascend platform and the CANN toolkit.

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

• Establishing and configuring the CANN development environment.
• Creating AI applications utilizing MindSpore and CloudMatrix workflows.
• Enhancing performance on Ascend NPUs through custom operators and tiling techniques.
• Deploying models into cloud or edge computing environments.

Course Format

• Engaging lectures combined with interactive discussions.
• Practical application of Huawei Ascend and the CANN toolkit within sample projects.
• Supervised exercises centered on model construction, training, and deployment.

Customization Opportunities

• For customized training tailored to your specific infrastructure or datasets, please reach out to us to arrange a session.

CANN (Compute Architecture for Neural Networks) represents Huawei's AI compute stack, designed for deploying and optimizing AI models on Ascend AI processors.

This instructor-led live training, available online or on-site, targets intermediate-level AI developers and engineers seeking to efficiently deploy trained AI models to Huawei Ascend hardware. The course focuses on utilizing the CANN toolkit alongside tools such as MindSpore, TensorFlow, or PyTorch.

Upon completion, participants will be able to:

• Grasp the CANN architecture and its function within the AI deployment pipeline.
• Convert and adapt models from popular frameworks into Ascend-compatible formats.
• Utilize tools like ATC, OM model conversion, and MindSpore for both cloud and edge inference.
• Troubleshoot deployment issues and optimize 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.

Customization Options

• For a customized training version of this course, please contact us to make arrangements.

This instructor-led, live training conducted in Bulgaria (online or onsite) is tailored for intermediate-level developers, data scientists, and tech enthusiasts aiming to acquire practical skills in deploying AI models on edge devices for diverse applications.

By the conclusion 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 edge deployment.
• Implement practical AI solutions on edge devices.
• Evaluate and improve the performance of edge-deployed models.
• Address ethical and security considerations in Edge AI applications.

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

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

• Grasp the security risks and vulnerabilities associated with Edge AI deployments.
• Apply encryption and authentication techniques to protect data.
• Design resilient Edge AI architectures capable of withstanding cyber threats.
• Utilize secure strategies for deploying AI models in edge environments.

Cambricon MLUs (Machine Learning Units) are specialized AI chips designed to optimize inference and training tasks in both edge computing and data center environments.

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

Upon completing this training, participants will be able to:

• Configure and set up the development environments for BANGPy and Neuware.
• Develop and optimize Python- and C++-based models for Cambricon MLUs.
• Deploy models to edge devices and data centers running the Neuware runtime.
• Integrate ML workflows with acceleration features specific to MLU hardware.

Course Format

• Interactive lectures and discussions.
• Practical, hands-on exercises using BANGPy and Neuware for development and deployment.
• Guided labs focusing on optimization, integration, and testing.

Customization Options

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

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 understand the role of CANN within the model lifecycle, from training through deployment, and its integration with frameworks such as MindSpore, TensorFlow, and PyTorch.

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

• Comprehend the purpose and architectural design of the CANN toolkit.
• Configure a development environment utilizing CANN and MindSpore.
• Convert and deploy a basic AI model onto Ascend hardware.
• Acquire foundational knowledge to support future CANN optimization or integration initiatives.

Course Format

• Interactive lectures and discussions.
• Practical hands-on labs featuring simple model deployment.
• Step-by-step guidance through the CANN toolchain and integration points.

Customization Options

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

Huawei’s AI ecosystem — spanning from the low-level CANN SDK to the high-level MindSpore framework — delivers a cohesive environment for developing and deploying AI solutions, specifically optimized for Ascend hardware.

This instructor-led live training (available online or onsite) is designed for technical professionals ranging from beginner to intermediate skill levels who want to understand how CANN and MindSpore interact to facilitate AI lifecycle management and inform infrastructure choices.

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

• Comprehend the layered structure of Huawei’s AI compute architecture.
• Recognize how CANN facilitates model optimization and hardware-level implementation.
• Assess the MindSpore framework and its toolchain against industry standards.
• Place Huawei's AI stack within the context of enterprise or cloud/on-premises environments.

Course Format

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

Course 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 refine and optimize the performance of neural networks deployed on Ascend AI processors.

This instructor-led training session, available online or onsite, targets advanced AI developers and system engineers eager to maximize inference performance utilizing CANN’s sophisticated toolkit. Key components include the Graph Engine, TIK, and custom operator development.

Upon completing this training, participants will be equipped to:

• Comprehend CANN's runtime architecture and its performance lifecycle.
• Leverage profiling tools and the Graph Engine for thorough performance analysis and optimization.
• Develop and optimize custom operators utilizing TIK and TVM.
• Address memory bottlenecks and enhance model throughput.

Course Format

• Engaging lectures paired with interactive discussions.
• Practical labs featuring real-time profiling and operator tuning.
• Optimization exercises based on edge-case deployment scenarios.

Customization Options

• To request a customized version of this course, please contact us to make arrangements.

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

This instructor-led live training, available online or onsite, targets intermediate-level AI professionals looking to build, deploy, and optimize vision and language models using the CANN SDK for production scenarios.

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

• Deploying and optimizing CV and NLP models via CANN and AscendCL.
• Utilizing CANN tools to convert models and integrate them into active pipelines.
• Enhancing inference performance for tasks such as detection, classification, and sentiment analysis.
• Developing real-time CV/NLP pipelines suitable for edge or cloud deployment environments.

Course Format

• Interactive lectures paired with live demonstrations.
• Practical labs focused on model deployment and performance profiling.
• Designing live pipelines using real-world CV and NLP use cases.

Customization Options

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

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

This instructor-led, live training session, available online or on-site, is designed for experienced system developers who aim to create, deploy, and refine custom operators for AI models utilizing CANN’s TIK programming model and TVM compiler integration.

Upon completing this training, participants will be capable of:

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

Course Format

• Interactive lectures and demonstrations.
• Practical coding exercises involving operators within TIK and TVM pipelines.
• Testing and tuning on Ascend hardware or in simulator environments.

Customization Options for the Course

• For customized training requests for this course, please reach out to us to arrange details.

This instructor-led, live training in Bulgaria (online or onsite) targets beginner to intermediate-level agritech professionals, IoT specialists, and AI engineers who wish 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.

This instructor-led, live training in Bulgaria (online or onsite) is aimed at intermediate-level robotics engineers, autonomous vehicle developers, and AI researchers who wish to leverage Edge AI for innovative autonomous system solutions.

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

• Understand the role and benefits of Edge AI in autonomous systems.
• Develop and deploy AI models for real-time processing on edge devices.
• Implement Edge AI solutions in autonomous vehicles, drones, and robotics.
• Design and optimize control systems using Edge AI.
• Address ethical and regulatory considerations in autonomous AI applications.