What if your AI models ran faster, trained smarter, and deployed seamlessly from edge to cloud? Huawei Ascend makes that possible.
These instructor-led courses unlock the potential of Ascend AI processors, guiding engineers through hands-on projects with MindSpore, model compression, and chip-level optimization.
Courses are delivered via online live training through an interactive remote desktop, or as onsite live training in Katowice with labs focused on real production workloads.
Onsite sessions can be hosted at your facilities in Katowice or arranged at a NobleProg corporate training center in Katowice to support collaborative, team-based learning.
Also known as Ascend AI, Huawei Ascend 910, or Ascend Computing, this training helps you move beyond generic AI hardware and into purpose-built performance.
NobleProg – Your Local Training Provider
Katowice
Centrum Szkoleniowe Moniuszki 7, Moniuszki 7, Katowice, Poland, 40-004
The training room is located in the heart of Katowice. Its attractive location in the city center guarantees easy access for all participants. The main railway station is just 500 meters away from our venue, and bus and tram stops are only 100 meters away. Additionally, it has excellent connections to the outbound route towards the A4 motorway to Krakow - Wroclaw and Pyrzowice Airport, making it an ideal venue for participants arriving from various parts of Poland and abroad.
Huawei Ascend is a family of AI processors designed for high-performance inference and training.
This instructor-led, live training (online or onsite) is aimed at intermediate-level AI engineers and data scientists who wish to develop and optimize neural network models using Huawei’s Ascend platform and the CANN toolkit.
By the end of this training, participants will be able to:
Set up and configure the CANN development environment.
Develop AI applications using MindSpore and CloudMatrix workflows.
Optimize performance on Ascend NPUs using custom operators and tiling.
Deploy models to edge or cloud environments.
Format of the Course
Interactive lecture and discussion.
Hands-on use of Huawei Ascend and CANN toolkit in sample applications.
Guided exercises focused on model building, training, and deployment.
Course Customization Options
To request a customized training for this course based on your infrastructure or datasets, please contact us to arrange.
Huawei’s AI stack — from the low-level CANN SDK to the high-level MindSpore framework — offers a tightly integrated AI development and deployment environment optimized for Ascend hardware.
This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level technical professionals who wish to understand how the CANN and MindSpore components work together to support AI lifecycle management and infrastructure decisions.
By the end of this training, participants will be able to:
Understand the layered architecture of Huawei’s AI compute stack.
Identify how CANN supports model optimization and hardware-level deployment.
Evaluate the MindSpore framework and toolchain in relation to industry alternatives.
Position Huawei's AI stack within enterprise or cloud/on-prem environments.
Format of the Course
Interactive lecture and discussion.
Live system demos and case-based walkthroughs.
Optional guided labs on model flow from MindSpore to CANN.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
The CANN SDK (Compute Architecture for Neural Networks) provides powerful deployment and optimization tools for real-time AI applications in computer vision and NLP, especially on Huawei Ascend hardware.
This instructor-led, live training (online or onsite) is aimed at intermediate-level AI practitioners who wish to build, deploy, and optimize vision and language models using the CANN SDK for production use cases.
By the end of this training, participants will be able to:
Deploy and optimize CV and NLP models using CANN and AscendCL.
Use CANN tools to convert models and integrate them into live pipelines.
Optimize inference performance for tasks like detection, classification, and sentiment analysis.
Build real-time CV/NLP pipelines for edge or cloud-based deployment scenarios.
Format of the Course
Interactive lecture and demonstration.
Hands-on lab with model deployment and performance profiling.
Live pipeline design using real CV and NLP use cases.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
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.
CloudMatrix is Huawei’s unified AI development and deployment platform designed to support scalable, production-grade inference pipelines.
This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level AI professionals who wish to deploy and monitor AI models using the CloudMatrix platform with CANN and MindSpore integration.
By the end of this training, participants will be able to:
Use CloudMatrix for model packaging, deployment, and serving.
Convert and optimize models for Ascend chipsets.
Set up pipelines for real-time and batch inference tasks.
Monitor deployments and tune performance in production settings.
Format of the Course
Interactive lecture and discussion.
Hands-on use of CloudMatrix with real deployment scenarios.
Guided exercises focused on conversion, optimization, and scaling.
Course Customization Options
To request a customized training for this course based on your AI infrastructure or cloud environment, please contact us to arrange.
Huawei's Ascend CANN toolkit enables powerful AI inference on edge devices such as the Ascend 310. CANN provides essential tools for compiling, optimizing, and deploying models where compute and memory are constrained.
This instructor-led, live training (online or onsite) is aimed at intermediate-level AI developers and integrators who wish to deploy and optimize 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 Ascend 310 using CANN tools.
Build lightweight inference pipelines using MindSpore Lite and AscendCL.
Optimize model performance for limited compute and memory environments.
Deploy and monitor AI applications in real-world edge use cases.
Format of the Course
Interactive lecture and demonstration.
Hands-on lab work with edge-specific models and scenarios.
Live deployment examples on virtual or physical edge hardware.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
CANN (Compute Architecture for Neural Networks) is Huawei’s AI computing toolkit used to compile, optimize, and deploy AI models on Ascend AI processors.
This instructor-led, live training (online or onsite) is aimed at beginner-level AI developers who wish to understand how CANN fits into the model lifecycle from training to deployment, and how it works with frameworks like MindSpore, TensorFlow, and PyTorch.
By the end of this training, participants will be able to:
Understand the purpose and architecture of the CANN toolkit.
Set up a development environment with CANN and MindSpore.
Convert and deploy a simple AI model to Ascend hardware.
Gain foundational knowledge for future CANN optimization or integration projects.
Format of the Course
Interactive lecture and discussion.
Hands-on labs with simple model deployment.
Step-by-step walkthrough of the CANN toolchain and integration points.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
Ascend, Biren, and Cambricon are leading AI hardware platforms in China, each offering unique acceleration and profiling tools for production-scale AI workloads.
This instructor-led, live training (online or onsite) is aimed at advanced-level AI infrastructure and performance engineers who wish to optimize model inference and training workflows across multiple Chinese AI chip platforms.
By the end 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 optimizations.
Tune deployment pipelines to improve throughput and latency.
Format of the Course
Interactive lecture and discussion.
Hands-on use of profiling and optimization tools on each platform.
Guided exercises focused on practical tuning scenarios.
Course Customization Options
To request a customized training for this course based on your performance environment or model type, please contact us to arrange.
CANN SDK (Compute Architecture for Neural Networks) is Huawei’s AI compute foundation that allows developers to fine-tune and optimize the performance of deployed neural networks on Ascend AI processors.
This instructor-led, live training (online or onsite) is aimed at advanced-level AI developers and system engineers who wish to optimize inference performance using CANN’s advanced toolset, including the Graph Engine, TIK, and custom operator development.
By the end of this training, participants will be able to:
Understand CANN's runtime architecture and performance lifecycle.
Use profiling tools and Graph Engine for performance analysis and optimization.
Create and optimize custom operators using TIK and TVM.
Resolve memory bottlenecks and improve model throughput.
Format of the Course
Interactive lecture and discussion.
Hands-on labs with real-time profiling and operator tuning.
Optimization exercises using edge-case deployment examples.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
Chinese GPU architectures such as Huawei Ascend, Biren, and Cambricon MLUs offer CUDA alternatives tailored for local AI and HPC markets.
This instructor-led, live training (online or onsite) is aimed at advanced-level GPU programmers and infrastructure specialists who wish to migrate and optimize existing CUDA applications for deployment on Chinese hardware platforms.
By the end of this training, participants will be able to:
Evaluate compatibility of existing CUDA workloads with Chinese chip alternatives.
Port CUDA codebases to Huawei CANN, Biren SDK, and Cambricon BANGPy environments.
Compare performance and identify optimization points across platforms.
Address practical challenges in cross-architecture support and deployment.
Format of the Course
Interactive lecture and discussion.
Hands-on code translation and performance comparison labs.
Guided exercises focused on multi-GPU adaptation strategies.
Course Customization Options
To request a customized training for this course based on your platform or CUDA project, please contact us to arrange.
CANN (Compute Architecture for Neural Networks) is Huawei’s AI compute stack for deploying and optimizing AI models on Ascend AI processors.
This instructor-led, live training (online or onsite) is aimed at intermediate-level AI developers and engineers who wish to deploy trained AI models efficiently to Huawei Ascend hardware using the CANN toolkit and tools such as MindSpore, TensorFlow, or PyTorch.
By the end of this training, participants will be able to:
Understand the CANN architecture and its role in the AI deployment pipeline.
Convert and adapt models from popular frameworks to Ascend-compatible formats.
Use tools like ATC, OM model conversion, and MindSpore for edge and cloud inference.
Diagnose deployment issues and optimize performance on Ascend hardware.
Format of the Course
Interactive lecture and demonstration.
Hands-on lab work using CANN tools and Ascend simulators or devices.
Practical deployment scenarios based on real-world AI models.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
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