AI Infrastructure
Business value across the full AI infrastructure.
Bringing an AI model from experimentation to production demands infrastructure built for scale, governance, and operational reliability. ANKASOFT designs the compute, data pipeline, and MLOps foundations that enterprise teams need to operate AI activities with the controls required for regulated industries.
01 - ML Platform Design
- Unified platform for experimentation, training, and serving across teams.
- Reproducible model training with full lineage tracking from data to deployment.
- Experiment management so good ideas don't get lost and bad ones don't get repeated.
- Role-based access so data scientists can work fast without compromising production stability.
02 - GPU Compute Orchestration
- Cost-efficient GPU architectures using spot instances for training, reserved capacity for inference, and on-premise burst capability.
- Distributed training setup
- Cost-aware autoscaling
- On-premise and cloud-hybrid support
03 - Model Serving and Inference
- Low-latency inference endpoints with canary rollouts, A/B testing, and model performance monitoring.
- Drift detection and alerting
- Autoscaling inference capacity
- SLA-aligned endpoint sizing
04 - Data Pipelines and Feature Stores
End-to-end data pipelines with feature stores that eliminate training-serving skew.
- Full data lineage tracking
- Pipeline quality monitoring
- Adherence to specific regulations and governance standards
FAQs before an AI infrastructure engagement.
Data scientists are trained to build models. Infrastructure engineers are trained to run them reliably at scale under real operational conditions. These are different skills. Most teams discover the gap when they try to ship their first model to production and realise the path from notebook to endpoint is not straightforward.
Yes. A discovery conversation is often enough to determine whether GPU compute is actually necessary for your workload or whether optimised CPU inference is sufficient and significantly cheaper. We don't recommend infrastructure you don't need. Over-specified AI infrastructure is a common and expensive mistake.
No. We design AI infrastructure around your existing cloud environment wherever possible. If a specific workload genuinely benefits from a different provider's ML tooling, we will say so and explain why. But consolidation for its own sake is not something we recommend.
Data governance is built into our AI infrastructure design from the start. This includes data residency controls, access logging, anonymisation pipelines, and alignment with KVKK, GDPR, broader European and international frameworks such as the EU AI Act, and the relevant ISO standards where applicable. We have worked with clients in finance and healthcare where these requirements are non-negotiable.