HCIP-AI EI Developer case studies
HCIP-AI EI Developer · Put your knowledge to work in context.
The HCIP-AI EI Developer is a Professional-level credential in Huawei's career certification roadmap, which Huawei describes as running through three tiers: Huawei Certified ICT Associate (HCIA), Huawei Certified ICT Professional (HCIP), and Huawei Certified ICT Expert (HCIE). This page is an independent study guide for candidates preparing for that exam; it is not affiliated with, endorsed by, or operated by Huawei, and it does not reproduce any official exam content. The HCIP-AI EI Developer case studies are 5 cases and about 3,580 words.
1Migrating an 8-Card ResNet-50 Job to Ascend 910 on ModelArts
A retail team is moving a PyTorch ResNet-50 training job from an on-premises 8×V100 server to a ModelArts training job using one node with 8 Ascend 910 NPUs. The dataset is 2.1 million JPEG images (about 280 GB) stored in an OBS bucket, and the job must finish within the platform's 3-day limit.
739 words, about 4 min2One PyTorch Codebase for GPU Workstations and Ascend Production
A computer-vision team maintains a single PyTorch training repository used on developer GPU workstations (RTX 3090) and on a production ModelArts cluster with Ascend 910. The code hard-codes model.cuda() , torch.cuda.amp.autocast() , and DataLoader(num workers=16, pin memory=True) .
682 words, about 3 min3A Defect Classifier with 97.8% Accuracy and 0.14 Recall
A factory has 48,000 images from an optical inspection station; 2.1% show a surface defect. A ResNet-50 binary classifier trained with cross-entropy reaches 97.8% validation accuracy, AUC 0.81, and recall 0.14 at the default 0.5 threshold.
699 words, about 3 min4INT8 Conversion Drops Small-Defect mAP on an Edge Device
A surface-inspection product runs a YOLOv5-s detector on an Atlas 200 DK-class edge device. The input is 1280×720 from a GigE camera at 30 FPS, so the end-to-end budget is 33 ms.
723 words, about 4 min5Truncation at 512 Tokens Hides Late Clauses in Chinese Contract Review
A legal-tech team fine-tunes a Chinese BERT-base model for multi-label clause classification on 120,000 contracts. Documents average 3,000 Chinese characters, and 8% exceed 8,000.
737 words, about 4 min