Cambricon MLU Development with BANGPy and Neuware Training Course

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

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

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

6G and the Intelligent Edge is a forward-looking course that explores the integration of 6G wireless technologies with edge computing, IoT ecosystems, and AI-driven data processing to support intelligent, low-latency, and adaptive infrastructures.

This instructor-led, live training (online or onsite) is aimed at intermediate-level IT architects who wish to understand and design next-generation distributed architectures leveraging the synergy of 6G connectivity and intelligent edge systems.

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

• Grasp 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 for intelligent decision-making.
• Plan scalable, secure, and resilient 6G-ready edge infrastructures.
• Evaluate business and operational models enabled by 6G-edge convergence.

Format of the Course

• 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 Australia (online or onsite) is aimed at advanced-level AI practitioners, researchers, and developers who wish to master the latest advancements in Edge AI, optimise their AI models for edge deployment, and explore specialised applications across various industries.

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

• Explore advanced techniques in Edge AI model development and optimisation.
• Implement cutting-edge strategies for deploying AI models on edge devices.
• Utilise specialised tools and frameworks for advanced Edge AI applications.
• Optimise the performance and efficiency of Edge AI solutions.
• Investigate innovative use cases and emerging trends in Edge AI.
• Address advanced ethical and security considerations in Edge AI deployments.

This instructor-led live training in Australia (online or onsite) is aimed at intermediate-level developers, data scientists, and tech enthusiasts who wish to gain practical skills in deploying AI models on edge devices for various applications.

By the end 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 optimise 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 Australia (online or onsite) is designed for advanced 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.
• Implement encryption and authentication techniques to protect data.
• Design resilient Edge AI architectures capable of withstanding cyber threats.
• Apply secure AI model deployment strategies within edge environments.

Huawei's Ascend CANN toolkit empowers robust AI inference on edge devices like the Ascend 310. It supplies crucial tools for compiling, optimising, and deploying models in environments where compute power and memory are limited.

This instructor-led live training (available online or onsite) is designed for intermediate-level AI developers and integrators keen to deploy and optimise models on Ascend edge devices using the CANN toolchain.

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

• Prepare and convert AI models for the Ascend 310 using CANN tools.
• Construct lightweight inference pipelines using MindSpore Lite and AscendCL.
• Optimise model performance for constrained compute and memory environments.
• Deploy and monitor AI applications in real-world edge scenarios.

Course Format

• Interactive lectures and demonstrations.
• Hands-on lab exercises featuring edge-specific models and scenarios.
• Live deployment examples on virtual or physical edge hardware.

Course Customisation Options

• To request a customised training course, please contact us to arrange.

Indigenous GPU architectures in China, including Huawei Ascend, Biren, and Cambricon MLUs, provide viable alternatives to CUDA, specifically designed for the domestic artificial intelligence and high-performance computing (HPC) sectors.

This instructor-led, live training session (available online or onsite) targets advanced GPU developers and infrastructure specialists seeking to migrate and optimise existing CUDA applications for deployment on Chinese hardware platforms.

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

• Evaluating the compatibility of current CUDA workloads with domestic chip alternatives.
• Translating CUDA codebases into Huawei CANN, Biren SDK, and Cambricon BANGPy environments.
• Comparing performance metrics and identifying key areas for optimisation across different platforms.
• Navigating practical challenges associated with cross-architecture support and deployment.

Course Delivery Format

• Interactive lectures and discussions.
• Practical labs involving code translation and performance benchmarking.
• Guided exercises focusing on multi-GPU adaptation strategies.

Customisation Options

• To arrange customised training for this course tailored to your specific platform or CUDA project, please contact us.

This instructor-led, live training in Australia (online or onsite) is designed for beginner to intermediate agritech professionals, IoT specialists, and AI engineers looking to develop and deploy Edge AI solutions for smart farming.

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

• Grasp the role of Edge AI in precision agriculture.
• Implement AI-driven monitoring systems for crops and livestock.
• Develop automated irrigation and environmental sensing solutions.
• Enhance agricultural efficiency through real-time Edge AI analytics.

This instructor-led, live training in Australia (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.

This instructor-led, live training in Australia (online or onsite) is designed for intermediate-level developers and IT professionals who want to gain a comprehensive understanding of Edge AI, covering everything from concept to practical implementation, including setup and deployment.

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

• Understand the fundamental concepts of Edge AI.
• Set up and configure Edge AI environments.
• Develop, train, and optimise Edge AI models.
• Deploy and manage Edge AI applications.
• Integrate Edge AI with existing systems and workflows.
• Address ethical considerations and best practices in Edge AI implementation.

This instructor-led, live training in Australia (online or onsite) targets intermediate to advanced computer vision engineers, AI developers, and IoT professionals looking to implement and optimise computer vision models for real-time processing on edge devices.

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

• Grasp the fundamentals of Edge AI and its applications within computer vision.
• Deploy optimised deep learning models on edge devices for real-time image and video analysis.
• Utilise frameworks such as TensorFlow Lite, OpenVINO, and NVIDIA Jetson SDK for model deployment.
• Optimise AI models for performance, power efficiency, and low-latency inference.

This instructor-led, live training in Australia (online or onsite) targets intermediate-level finance professionals, fintech developers, and AI specialists looking to implement Edge AI solutions in financial services.

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

• Understand the role of Edge AI in financial services.
• Implement fraud detection systems using Edge AI.
• Enhance customer service through AI-driven solutions.
• Apply Edge AI for risk management and decision-making.
• Deploy and manage Edge AI solutions in financial environments.

This instructor-led, live training in Australia (online or onsite) is designed for intermediate-level healthcare professionals, biomedical engineers, and AI developers keen to leverage Edge AI for innovative healthcare solutions.

Upon completing this training, participants will be able to:

• Grasp the role and benefits of Edge AI in healthcare.
• Develop and deploy AI models on edge devices for healthcare applications.
• Implement Edge AI solutions in wearable devices and diagnostic tools.
• Design and deploy patient monitoring systems using Edge AI.
• Address ethical and regulatory considerations in healthcare AI applications.

This instructor-led, live training in Australia (online or onsite) is aimed at intermediate-level industrial engineers, manufacturing professionals, and AI developers who wish to implement Edge AI solutions in industrial automation.

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

• Understand the role of Edge AI in industrial automation.
• Implement predictive maintenance solutions using Edge AI.
• Apply AI techniques for quality control in manufacturing processes.
• Optimize industrial processes using Edge AI.
• Deploy and manage Edge AI solutions in industrial environments.

Ascend, Biren, and Cambricon are prominent AI hardware platforms in China, each providing distinct acceleration and profiling tools for production-scale AI workloads.

This instructor-led, live training (available online or onsite) is designed for advanced-level AI infrastructure and performance engineers who seek to optimise model inference and training workflows across multiple Chinese AI chip platforms.

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

• Benchmark models on Ascend, Biren, and Cambricon platforms.
• Identify system bottlenecks and memory/compute inefficiencies.
• Apply graph-level, kernel-level, and operator-level optimisations.
• Tune deployment pipelines to enhance throughput and reduce latency.

Course Format

• Interactive lectures and discussions.
• Practical application of profiling and optimisation tools on each platform.
• Guided exercises focused on real-world tuning scenarios.

Course Customisation Options

• To request a bespoke training session tailored to your performance environment or model type, please contact us to arrange.