Aliyun PAI
Aliyun PAI (5): Designer vs Model Gallery — When the GUIs Actually Earn Their Keep
PAI-Designer for tabular ML pipelines, Model Gallery for one-click open-source model deploy/fine-tune. The honest decision matrix for when to skip the SDK and let the GUI ship for you.
Aliyun PAI (4): PAI-EAS — Model Serving, Cold Starts, and the TPS Lie
End-to-end PAI-EAS for production: image-based deploy from OSS-mounted weights, the three inference modes, an autoscaler that doesn't blow your budget, and canary releases via service groups. Includes a working vLLM …
Aliyun PAI (3): PAI-DLC — Distributed Training Without the Cluster Pain
Submit a real multi-GPU training job on PAI-DLC, understand the resource pools (Lingjun vs general vs preemptible), and use AIMaster + EasyCKPT so a flaky node doesn't cost you a day.
Aliyun PAI (2): PAI-DSW — Notebooks That Don't Eat Your Weights
Working with PAI-DSW for real: choosing the right GPU image, mounting OSS so you don't lose checkpoints when the instance restarts, and an MNIST notebook drawn from the official Quick Start that you can copy-paste.
Aliyun PAI (1): Platform Overview and the Product Family Map
What Aliyun PAI actually is in 2026, the four-layer architecture from the official docs, the five sub-products you'll touch, and a sane account/workspace setup so the rest of the series can skip the boilerplate.