CANN for Edge AI Deployment Training Course

This instructor-led, live training in India (online or onsite) is designed for intermediate-level telecom professionals, AI engineers, and IoT specialists who wish to explore how 5G networks accelerate 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 how 6G wireless technologies merge with edge computing, IoT ecosystems, and AI-driven data processing to create intelligent, adaptive infrastructures with ultra-low latency.

This instructor-led live training, available both online and onsite, is tailored for intermediate-level IT architects aiming to grasp and design next-generation distributed architectures that harness the synergy between 6G connectivity and intelligent edge systems.

After completing this course, participants will be equipped to:

• Comprehend how 6G will reshape 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 facilitate intelligent decision-making.
• Plan scalable, secure, and resilient edge infrastructures ready for 6G.
• Evaluate the business and operational models empowered by the convergence of 6G and edge technologies.

Course Format

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

Customization Options

• To request a customized training session for this course, please get in touch with us to arrange it.

This instructor-led live training in India (online or on-site) is designed for advanced-level AI practitioners, researchers, and developers who aim to master the latest Edge AI advancements, optimize their models for edge deployment, and explore specialized applications across various sectors.

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

• Investigate advanced methods for developing and optimizing Edge AI models.
• Apply cutting-edge strategies for deploying AI models on edge devices.
• Leverage specialized tools and frameworks for complex Edge AI applications.
• Enhance the performance and efficiency of Edge AI solutions.
• Explore innovative use cases and emerging trends in the Edge AI landscape.
• Tackle advanced ethical and security challenges associated with Edge AI deployments.

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

This instructor-led live training, available in online or onsite formats, targets intermediate AI engineers and data scientists seeking to develop and optimise 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.
• Construct AI applications utilizing MindSpore and CloudMatrix workflows.
• Enhance performance on Ascend NPUs through custom operators and tiling techniques.
• Deploy models across edge or cloud infrastructure.

Course Format

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

Customisation Options

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

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

This instructor-led, live training (available online or onsite) is tailored for intermediate-level AI developers and engineers who aim to efficiently deploy trained AI models onto Huawei Ascend hardware using the CANN toolkit along with tools such as MindSpore, TensorFlow, or PyTorch.

Upon completing this training, participants will be able to:

• Gain insight into the CANN architecture and its significance within the AI deployment pipeline.
• Convert and adapt models from widely-used frameworks into formats compatible with Ascend.
• Utilize tools like ATC, OM model conversion, and MindSpore for inference tasks in both edge and cloud environments.
• Troubleshoot deployment challenges and optimize performance on Ascend hardware.

Course Format

• Interactive lectures accompanied by live demonstrations.
• Practical lab exercises employing CANN tools along with Ascend simulators or physical devices.
• Real-world deployment scenarios based on actual AI models.

Customization Options

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

This instructor-led live training in India (online or onsite) targets intermediate-level developers, data scientists, and tech enthusiasts seeking practical skills in deploying AI models on edge devices for diverse applications.

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

• Comprehend the core principles of Edge AI and its advantages.
• Establish and configure the edge computing environment.
• Develop, train, and optimize AI models specifically for edge deployment.
• Deploy practical AI solutions on edge devices.
• Assess and enhance the performance of models deployed on the edge.
• Tackle ethical and security issues inherent in Edge AI applications.

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

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

• Grasp 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.
• Deploy secure AI models within edge environments.

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

This instructor-led live training, available either online or on-site, targets intermediate-level developers who aim to construct and deploy AI models leveraging the BANGPy framework and Neuware SDK on Cambricon MLU hardware.

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

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

Course Format

• Engaging lectures paired with interactive discussions.
• Practical, hands-on sessions involving development and deployment with BANGPy and Neuware.
• Guided exercises concentrating on optimization, integration, and testing.

Customization Options for the Course

• If you require customized training tailored to your specific Cambricon device model or use case, please get in touch with us to make the necessary arrangements.

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

This instructor-led live training, available either online or onsite, targets beginner-level AI developers keen on understanding CANN's role within the model lifecycle—from training to deployment—and its interaction with frameworks such as MindSpore, TensorFlow, and PyTorch.

Upon completing this training, participants will be able to:

• Grasp the purpose and architecture of the CANN toolkit.
• Configure a development environment integrating CANN and MindSpore.
• Convert and deploy a basic AI model onto Ascend hardware.
• Build foundational knowledge for future CANN optimization or integration initiatives.

Course Format

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

Course Customization Options

• To arrange customized training for this course, please get in touch with us.

Huawei's AI stack, spanning from the low-level CANN SDK to the high-level MindSpore framework, provides a closely integrated 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 at beginner to intermediate levels who wish to comprehend how the CANN and MindSpore components collaborate to facilitate AI lifecycle management and inform infrastructure decisions.

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

• Grasp the layered architecture of Huawei's AI compute stack.
• Recognise how CANN aids in model optimization and hardware-level deployment.
• Assess the MindSpore framework and its toolchain in comparison to industry alternatives.
• 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 covering the model flow from MindSpore to CANN.

Course Customization Options

• For those seeking a customized training version of this course, please reach out to us to make arrangements.

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

This instructor-led training session, available both online and onsite, is designed for advanced-level AI developers and system engineers who aim to enhance inference performance by leveraging CANN’s sophisticated toolset. Key components include the Graph Engine, TIK, and custom operator development.

Upon completing this training, participants will be equipped to:

• Gain a comprehensive understanding of CANN’s runtime architecture and performance lifecycle.
• Utilise profiling tools and the Graph Engine for thorough performance analysis and optimisation.
• Develop and optimise custom operators using TIK and TVM.
• Address memory bottlenecks and significantly improve model throughput.

Course Format

• Engaging interactive lectures and discussions.
• Practical hands-on labs featuring real-time profiling and operator tuning.
• Optimisation exercises utilising edge-case deployment examples.

Customisation Options

• To arrange a bespoke training session for this course, please reach out to us for further assistance.

The CANN SDK (Compute Architecture for Neural Networks) delivers robust deployment and optimization capabilities for real-time AI applications in computer vision and NLP, particularly on Huawei Ascend hardware.

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

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

• Deploy and optimize CV and NLP models leveraging CANN and AscendCL.
• Utilize CANN utilities to convert models and seamlessly integrate them into active pipelines.
• Enhance inference performance for tasks such as detection, classification, and sentiment analysis.
• Construct real-time CV/NLP pipelines suitable for edge or cloud-based deployment environments.

Course Format

• Interactive lectures combined with practical demonstrations.
• Hands-on labs focusing on model deployment and performance profiling.
• Live pipeline design exercises utilizing real-world CV and NLP use cases.

Customization Options

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

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, available both online and onsite, is designed for advanced-level system developers looking to build, deploy, and tune custom operators for AI models by leveraging CANN’s TIK programming model and TVM compiler integration.

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.
• Employing TVM for operator scheduling, auto-tuning, and benchmarking.
• Debugging and optimizing instruction-level performance for custom computational patterns.

Course Format

• Interactive lectures and demonstrations.
• Practical coding exercises for operators using TIK and TVM pipelines.
• Testing and tuning on Ascend hardware or simulators.

Course Customization Options

• For inquiries regarding customized training for this course, please get in touch with us to arrange it.

This instructor-led, live training in India (online or onsite) is designed for 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 India (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.