Training and Serving Custom Models on SAP AI Core

What you will learn

• Explain why training on SAP AI Core is an Argo-based workflow execution, not a managed 'train' button, and what a language-agnostic training contract requires from a data-science team
• Walk a training workflow end to end — choose an instance, define the template, list scenarios, create configurations, start training, retrieve logs — and name the object each step produces
• Choose between training from scratch, fine-tuning or continuing an imported model, and packaging an already-trained model for serving only, for a stated client need
• Configure a serving template's KServe InferenceService spec — predictor, STORAGE_URI, minReplicas, maxReplicas, containerConcurrency — for a real traffic pattern
• Use artifact signatures and training schedules to make repeated training runs auditable and automated retraining safe
• Diagnose the three most common training-and-serving failures from their symptoms alone: missing globalName, missing default object store secret, broken registry or artifact reference

Module overview

Who this is for. “SAP AI Core — Resource Groups, Scenarios, Executables” gave you SAP AI Core's object model and its resource-group boundaries. This module puts them to work on the case that model was built for: a client whose need is not "call an LLM through orchestration" but "train our own model on our own data, and serve it as a production endpoint" — a fraud score, a delivery-risk prediction, a demand forecast, or a fine-tuned model that a hyperscaler catalogue does not cover. This is SAP AI Core's own train-and-serve capability, language-agnostic and independent of the generative AI hub, and it is where a data-science background stops being optional.

Prerequisites

• SAP AI Core — Resource Groups, Scenarios, Executables — this module builds directly on its object model
• Working knowledge of at least one ML framework (scikit-learn, PyTorch, or similar) and comfort writing a Dockerfile
• Optional for hands-on work: a trial SAP AI Core tenant with a registered Git repository and Docker registry secret

Outcomes

• Stand up a complete training-to-serving pipeline for a custom model on SAP AI Core, from workflow template to a live inference endpoint
• Choose the right onboarding pattern (train from scratch, fine-tune, or serve-only) for a stated client model and justify it
• Size a deployment's scaling parameters against a stated traffic profile and defend the cost/latency trade-off
• Triage a broken training or serving setup to its root object (artifact, secret, registry, quota) from the symptom alone