Service
Data Science with PT Cloud Platform Indonesia (PT CPI)
Models only create value when they are reproducible, monitored, and owned. We bridge research notebooks and regulated production environments without cutting corners on data lineage or access control.
Production-minded ML on GCP with Python, Jupyter, scikit-learn, PyTorch, MLflow, and Vertex AI—feature stores, experiment tracking, and MLOps patterns that satisfy risk and compliance reviewers.
PT CPI treats data science as an engineering discipline. Experiments run in tracked environments (MLflow, Vertex AI Experiments); features are sourced from governed pipelines built with Polars, dbt, and BigQuery—not one-off CSV extracts.
We implement training and serving patterns on Vertex AI and GKE where latency and isolation matter, with model cards, bias checks, and approval workflows appropriate to regulated industries. Python remains the lingua franca, with Polars accelerating feature engineering when pandas bottlenecks appear.
Handover includes monitoring for drift, retraining triggers, and integration with your DevSecOps toolchain so model artifacts are scanned and promoted like application releases.
Who this is for
Data science leads, risk teams evaluating ML in banking and FinTech, and product organizations deploying scoring, forecasting, or NLP on Google Cloud.
What we deliver
- Python, Jupyter, and Polars for reproducible feature engineering
- scikit-learn and PyTorch models with MLflow tracking and Vertex AI deployment
- MLOps: CI/CD for models, monitoring, drift detection, and rollback runbooks
- Model governance artifacts for risk, audit, and institutional onboarding
How we engage
- Use-case framing: business outcome, data availability, regulatory constraints, and success metrics.
- Baseline experiment and feature pipeline design on governed GCP data.
- Production path: serving architecture, monitoring, and security review gates.
- Operate: retraining cadence, champion/challenger tests, and knowledge transfer.
Related documentation
Open PT Cloud Platform Indonesia documentation →Related services
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