Technical guide details assembling PAI services into end-to-end MLOps pipeline

A new technical guide documents how to assemble PAI services — PAI‑DSW, PAI‑DLC, OSS and PAI‑EAS-into an end‑to‑end MLOps pipeline that bridges notebook experiments and production inference. The guide concentrates on concrete configuration choices and architectural patterns that make model development, distributed training, artifact storage and serving reproducible and auditable for production use.

PAI‑DSW is presented as the managed JupyterLab workspace tuned for ML workflows. The guide recommends choosing instance types by workload: CPU instances for preprocessing and tabular models, and GPUs when deep learning is required, with GPU model selection aligned to your network architecture. DSW includes prebuilt kernels for TensorFlow 2.x, PyTorch, XGBoost and scikit‑learn, and supports registering custom kernels from container images when specific CUDA versions or internal libraries are needed.

To avoid copying large datasets to local disks, the guide advises mounting OSS buckets into DSW instances. Mounting an OSS path into the instance filesystem makes large datasets available at standard file paths without consuming instance disk; the writeup cites a 500 GB image dataset as an example. Mount configuration is set at instance creation and references an OSS bucket path plus a RAM role that grants read access to the bucket.

PAI‑DLC is described as the service that manages distributed training jobs across provisioned clusters. Job definitions specify worker count, GPU type, framework (TensorFlow, PyTorch or MXNet), entry script and OSS paths for inputs and output model artifacts. Clusters are provisioned at job start and released on completion to bound compute costs. The guide also covers resource group selection: use dedicated groups for isolation and predictable scheduling, and shared groups when utilization tradeoffs are acceptable.

The guide recommends using OSS as the single source for both input datasets and output model artifacts so downstream components — specifically PAI‑EAS (Elastic Algorithm Service)—can consume trained models for scalable serving. Routing artifacts through OSS reduces manual transfers and keeps storage consistent across development, training and serving stages. Operational patterns intended to improve reliability and auditability include integrating Git at the DSW instance level for version‑controlled experiment tracking — committing notebooks, training scripts and configuration alongside model metadata — and relying on reproducible environment kernels or containerized custom kernels. Together, these choices aim to eliminate invisible dependency failures and provide rollback and audit trails that are often missing from ad hoc deployments.