Data and AI platform

MLOps and Data Platform Foundation on OCI

Connected MLflow, Object Storage, Data Flow/Spark, run metadata, and artifacts into a reproducible data/ML path on OCI.

Case-study indexHome

Role

Platform builder

Stack

MLflow / Data Flow

Outcome

AI-ready base

Problem

Data and ML work risked one-off setup: unclear dataset locations, untracked runs, private execution gaps, and unmanaged artifacts.

Action

Connected dataset storage, execution jobs, run tracking, artifact versioning, logging, and readiness checks across OCI services.

Result

AI-ready workflows with controlled execution, traceable artifacts, and operational checks before broader adoption.

Evidence

Experiment lifecycle diagram and readiness checklist covering dataset location, run metadata, artifact versioning, and operational signals.

key decisions

  • Separate datasets, runtime images, experiments, and model artifacts as first-class platform concerns.
  • Keep execution private and observable before scaling adoption.
  • Use repeatable delivery patterns instead of one-off notebook or job setup.

evidence examples

representative example

Experiment and artifact lifecycle

Lifecycle for reproducible data and ML work.

representative example

MLOps readiness checklist

Checks before treating a data or ML path as platform-ready.

  • Dataset location defined
  • Run metadata captured
  • Artifacts versioned
  • Operational signals reviewed

portable standard

OCI is the proven reference platform here; the MLOps operating model can be adapted by mapping storage, compute, experiment tracking, and CI/CD primitives in another provider.

OCI Data ScienceMLflowData FlowSparkMySQLTerraform