CANN for Edge AI Deployment Training Course

This instructor-led, live training in Turkey (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.

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.

This forward-looking course examines the convergence of 6G wireless technologies, edge computing, IoT ecosystems, and AI-driven data processing to build intelligent, adaptive, and low-latency infrastructures.

Designed for intermediate IT architects, this instructor-led training (available online or onsite) focuses on understanding and designing next-generation distributed architectures that leverage the combined power of 6G connectivity and intelligent edge systems.

After completing this course, participants will be able to:

• Comprehend how 6G will reshape edge computing and IoT architectures.
• Architect distributed systems optimized for ultra-low latency, high bandwidth, and autonomous operations.
• Implement AI and data analytics at the edge to enable intelligent decision-making.
• Plan scalable, secure, and resilient infrastructures ready for 6G adoption.
• Assess the business and operational models facilitated by the 6G-edge convergence.

Course Format

• Interactive lectures and collaborative discussions.
• Case studies and practical architecture design exercises.
• Hands-on simulations using optional edge or container-based tools.

Customization Options

• To arrange customized training for this course, please contact us to discuss your specific needs.

This instructor-led live training in Turkey (online or onsite) targets 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.

Upon completing this training, participants will be capable of:

• Exploring advanced techniques in Edge AI model development and optimization.
• Implementing cutting-edge strategies for deploying AI models on edge devices.
• Utilizing specialized tools and frameworks for advanced Edge AI applications.
• Optimizing the performance and efficiency of Edge AI solutions.
• Investigating innovative use cases and emerging trends in Edge AI.
• Addressing advanced ethical and security considerations in Edge AI deployments.

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

This instructor-led live training session, available both online and onsite, targets intermediate-level data scientists and AI engineers looking to develop and optimize neural network models utilizing Huawei’s CANN toolkit and Ascend platform.

Upon completing this training, attendees will be equipped to:

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

Course Format

• Practical use of Huawei Ascend and the CANN toolkit within sample applications.
• Interactive discussions and lectures.
• Guided exercises centered on model building, training, and deployment.

Customization Options for the Course

• For tailored training aligned with your specific infrastructure or datasets, please get in touch with us to make arrangements.

CANN (Compute Architecture for Neural Networks) serves as Huawei's comprehensive AI compute stack designed for the efficient deployment and optimization of AI models on Ascend AI processors.

This instructor-led live training session, available either online or onsite, is tailored for intermediate-level AI developers and engineers seeking to efficiently deploy trained AI models onto Huawei Ascend hardware. Participants will utilize the CANN toolkit alongside tools such as MindSpore, TensorFlow, or PyTorch.

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

• Gaining a deep understanding of the CANN architecture and its pivotal role within the AI deployment pipeline.
• Converting and adapting models originating from popular frameworks into formats compatible with Ascend.
• Leveraging tools such as ATC, OM model conversion, and MindSpore for both edge and cloud inference tasks.
• Diagnosing deployment-related issues and optimizing performance specifically on Ascend hardware.

Course Format

• Interactive lectures combined with live demonstrations.
• Practical hands-on lab exercises utilizing CANN tools along with Ascend simulators or physical devices.
• Real-world deployment scenarios based on authentic AI models.

Course Customization Options

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

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

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

• Understand the fundamental principles and benefits of Edge AI.
• 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 Turkey (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:

• Comprehend the security risks and vulnerabilities associated with Edge AI deployments.
• Deploy encryption and authentication techniques to ensure data protection.
• 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 AI processors designed for optimized inference and training in both edge and datacenter environments.

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

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

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

Course Format

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

Course Customization Options

• To request customized training for this course based on your specific Cambricon device model or use case, please contact us to arrange.

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 training, available online or onsite, is tailored for beginner-level AI developers seeking to grasp how CANN integrates into the model lifecycle, spanning from training to deployment, and its interoperability with frameworks such as MindSpore, TensorFlow, and PyTorch.

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

• Comprehending the purpose and architecture 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 projects.

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 for the Course

• For tailored training arrangements, please contact us to discuss your needs.

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

This instructor-led, live training (available online or on-site) targets technical professionals at the beginner to intermediate levels who seek to comprehend how CANN and MindSpore components collaborate to manage the AI lifecycle and inform infrastructure strategies.

Upon completing this training, participants will be equipped 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 in comparison to industry alternatives.
• Position Huawei's AI stack effectively within enterprise or cloud/on-premises environments.

Course Format

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

Course Customization Options

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

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

This instructor-led live training (available online or onsite) is designed for advanced AI developers and system engineers seeking to optimize inference performance through CANN’s sophisticated toolset, including the Graph Engine, TIK, and custom operator development.

Upon completing this training, participants will be able to:

• Grasp CANN's runtime architecture and performance lifecycle.
• 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 enhance model throughput.

Course Format

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

Customization Options

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

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

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

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

• Deploy and optimize CV and NLP models utilizing CANN and AscendCL.
• Leverage CANN tools to convert models and integrate them into operational pipelines.
• Enhance inference performance for tasks such as object detection, classification, and sentiment analysis.
• Construct real-time CV/NLP pipelines tailored for edge or cloud-based deployment scenarios.

Course Format

• Interactive lectures and live demonstrations.
• Practical lab sessions focused on model deployment and performance profiling.
• Real-time pipeline design using practical CV and NLP use cases.

Customization Options

• For customized training arrangements for this course, please contact us directly.

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

This live, instructor-led training (available online or onsite) is designed for advanced system developers looking to build, deploy, and fine-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 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.
• Utilize TVM for operator scheduling, auto-tuning, and benchmarking.
• Debug and optimize instruction-level performance for custom computation patterns.

Course Format

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

Course Customization Options

• For information on requesting customized training for this course, please contact us to make arrangements.

This instructor-led, live training in Turkey (online or on-site) is aimed at 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 Turkey (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.