Huawei HCIA-AI case studies
Huawei HCIA-AI · Put your knowledge to work in context.
The Huawei Certified ICT Associate (HCIA) is the entry level of Huawei's three-tier Career Certification roadmap, which runs from Associate (HCIA) through Professional (HCIP) to Expert (HCIE). The AI track sits in the same scheme as the named Computing, Datacom and other technology tracks, and is aimed at newcomers who want a recognised, vendor-aligned foundation in artificial intelligence before moving on to professional-level study. The Huawei HCIA-AI case studies are 5 cases and about 3,047 words.
1Sizing an On-Premises AI Stack for a Logistics Operator
A 40-warehouse logistics operator in Southeast Asia has a three-year CNY 8 million AI budget and six Java engineers with no machine-learning experience. Customer contracts forbid sending operational data to any public cloud, so training and inference must stay on premises.
641 words, about 3 min2Fraud Model with a 99.4% Accuracy Claim
A payments team trains a fraud classifier on 2.4 million card transactions spanning 18 months, of which 14,400 (0.6%) are confirmed fraud. Features include merchant category, amount, hour, device age, and chargeback flag, which the disputes team populates about 30 days after the transaction.
610 words, about 3 min3Overfitting a Defect Classifier on an Imbalanced Line
A factory deploys a ResNet-50 defect classifier on 9,600 labelled images across eight defect classes; the rarest class has 240 images and the largest 3,100. Training uses Adam at 1e-3, batch 32, 60 epochs, and only horizontal flips.
613 words, about 3 min4Migrating TensorFlow 1.12 Training to Ascend in Six Months
A team maintains 180,000 lines of TensorFlow 1.12 code for a 12-class document classifier, using tf.contrib.slim and custom Python preprocessing. It trains on eight NVIDIA V100 GPUs at 40 ms per batch of 32.
573 words, about 3 min5Edge Video Analytics on Atlas 300I Pro Cards
A city surveillance integrator must run four models (person detection, helmet detection, plate OCR, loitering) on 64 incoming 1080p25 camera streams at the edge. Each channel is sampled at 2 fps per model, so each model must handle 128 inferences per second, and end-to-end latency must stay under 40 ms.
610 words, about 3 min