Singapore's AI vendors span GPU/HPC infrastructure, MLOps platforms, and applied ML consultancies — and buyers often conflate them. This checklist helps you separate genuine production-grade ML from a polished demo, and pin down where your data, models, and IP actually sit.
How to evaluate an AI / ML compute & infrastructure provider in Singapore
- Name the workflow before the model — predictive analytics, computer vision, NLP/LLM apps, or raw GPU/HPC capacity each need a different vendor, so scope the bottleneck (compute, talent, or production engineering) before you shortlist.
- Pin down GPU capacity and scheduling in writing: ask whether they hold reserved cloud quota (AWS/Azure SG-region) or on-prem GPUs versus competing for spot instances, and how queue priority and burst limits are handled at your peak.
- Confirm data residency and PDPA posture — where training data, embeddings, and model weights physically sit, what leaves Singapore, and how personal data is classified — and ask which IMDA AI Verify principles they can attest to for regulated use cases.
- Probe MLOps maturity as a hard requirement: model versioning, an evaluation harness, drift and bias monitoring, and human-in-the-loop fallback — a team without these is a research lab, not a production vendor.
- Get model governance and IP ownership in the contract: who owns custom-trained models, fine-tuning artefacts, and prompts; what training-data provenance they warrant; and whether your data is ever used to train shared models.
- Treat AI Singapore (100E/AIAP), PSG, and EDG alignment as a signal to verify by date with the issuing body, not a guarantee — funding should improve ROI on a sound business case, not rescue a weak one.
Verify for Nebula Data
- Confirm key details directly with the vendor — this listing isn't vendor-managed yet.
- Ask for two recent Singapore client references you can speak with.
- Ask for a written scope of services before comparing quotes.
- Request evidence of relevant certifications and their current validity.
Questions to ask
- Can you share two Singapore or Southeast Asia production systems running in front of paying users for at least six months, with their uptime, cost per transaction, and model-refresh cadence?
- Where do our data, embeddings, and model weights reside, and what exactly crosses out of Singapore?
- Who owns the trained models, fine-tuning artefacts, and prompts after the engagement, and is our data ever used to train models shared with other clients?
- What happens at production when the model is uncertain or drifts — what are your eval, monitoring, and human-fallback controls?