CANN for Edge AI Deployment Training Course

This live, instructor-led training session, available Thailand (online or onsite), is tailored for intermediate-level telecom experts, AI engineers, and IoT specialists aiming to leverage 5G networks to boost Edge AI application performance.

By the conclusion of this program, learners will be able to:

• Understand the core tenets of 5G technology and its effect on Edge AI.
• Deploy AI models optimized for low-latency tasks in 5G contexts.
• Construct real-time decision systems utilizing Edge AI and 5G connectivity.
• Optimize AI workloads for high-efficiency execution on edge hardware.

This instructor-led, live training in Thailand (online or on-site) is designed for advanced AI practitioners, researchers, and developers aiming to master the latest Edge AI developments, optimize models for edge deployment, and explore specialized industry applications.

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

• Investigate advanced methodologies for developing and optimizing Edge AI models.
• Execute state-of-the-art strategies for deploying AI models across edge devices.
• Leverage specialized tools and frameworks tailored for complex Edge AI applications.
• Enhance the performance and efficiency of Edge AI solutions.
• Discover innovative use cases and track emerging trends in the Edge AI landscape.
• Navigate advanced ethical and security complexities in Edge AI deployments.

This live, instructor-led training (delivered online or onsite) is designed for mid-level developers, data scientists, and technology professionals aiming to acquire practical expertise in deploying AI models on edge devices for diverse applications.

By the end of this program, participants will have the capability to:

• Comprehend the core principles of Edge AI and its associated benefits.
• Establish and configure the edge computing environment.
• Develop, train, and optimize AI models for edge-specific deployment.
• Implement practical AI solutions on edge hardware.
• Evaluate and improve the performance of edge-deployed models.
• Address ethical and security considerations within Edge AI applications.

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

By the end of the session, participants will be capable of:

• Recognizing security risks and vulnerabilities in Edge AI deployments.
• Utilizing encryption and authentication methods for data protection.
• Architecting resilient Edge AI structures to defend against cyber threats.
• Applying secure strategies for AI model deployment in edge environments.

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

This instructor-led, live training (available online or onsite) is tailored for beginner-level AI developers seeking to understand how CANN integrates into the model lifecycle, from training through to deployment, and how it collaborates with frameworks such as MindSpore, TensorFlow, and PyTorch.

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

• Grasp the purpose and underlying architecture of the CANN toolkit.
• Configure a development environment utilizing CANN and MindSpore.
• Convert and deploy a simple AI model onto Ascend hardware.
• Acquire foundational knowledge to support future CANN optimization or integration projects.

Course Format

• Interactive lectures and facilitated discussions.
• Hands-on labs focused on simple model deployment.
• Detailed walkthroughs of the CANN toolchain and its integration points.

Course Customization Options

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

Huawei's AI ecosystem, spanning from the foundational CANN SDK to the advanced MindSpore framework, provides a seamlessly integrated environment for developing and deploying AI solutions, specifically tailored for Ascend hardware.

This live, instructor-led session, available either online or onsite, is designed for technical professionals ranging from beginners to intermediates. It focuses on elucidating how CANN and MindSpore synergize to facilitate AI lifecycle management and inform critical infrastructure decisions.

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

• Grasp the layered architecture underlying Huawei's AI compute stack.
• Recognize the role of CANN in enabling model optimization and hardware-centric deployment.
• Assess the MindSpore framework and its associated toolchain against competing industry solutions.
• Determine the strategic placement of Huawei's AI stack within enterprise or cloud/on-prem settings.

Course Format

• Engaging lectures accompanied by open discussions.
• Real-time system demonstrations and scenario-based walkthroughs.
• Optional guided labs tracing the model flow from MindSpore to CANN.

Customization Availability

• For tailored training requirements regarding this course, please reach out to us to coordinate arrangements.

The CANN SDK (Compute Architecture for Neural Networks) serves as Huawei's core AI computing infrastructure, empowering developers to refine and maximize the performance of neural networks deployed on Ascend AI processors.

This live, instructor-led training (available online or onsite) is designed for advanced AI developers and system engineers seeking to elevate inference performance. It leverages CANN's comprehensive toolset, including the Graph Engine, TIK, and capabilities for custom operator development.

Upon completion, participants will be equipped to:

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

Course Format

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

Customization Opportunities

• Please reach out to us to discuss arranging a customized training experience for this course.

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

Designed as an instructor-led, live training experience available online or onsite, this course is tailored for intermediate-level AI practitioners looking to build, deploy, and optimize vision and language models for production environments using the CANN SDK.

Upon completing this training, participants will be equipped to:

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

Course Format

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

Customization Options

• To arrange a customized version of this training, please get in touch with our team.

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

This instructor-led, live training (online or onsite) is aimed at advanced-level system developers who wish 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 ops 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 this course, please contact us to arrange.

This live, instructor-led training in Thailand (available online or on-site) targets agritech professionals, IoT specialists, and AI engineers ranging from beginner to intermediate levels who are eager to develop and deploy Edge AI solutions for smart farming.

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

• Comprehend the role of Edge AI in precision agriculture.
• Implement AI-based monitoring systems for crops and livestock.
• Build automated irrigation and environmental sensing solutions.
• Improve agricultural efficiency using real-time Edge AI analytics.

This instructor-led, live training in Thailand (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.