Cloud AI Modernisation

Cloud AI modernisation is the process of upgrading your existing AI infrastructure and workflows to leverage current cloud-native services, architectures, and models. This typically includes migrating from monolithic ML pipelines to modular microservices, adopting managed GPU instances for training and inference, implementing retrieval-augmented generation for knowledge-intensive tasks, and establishing MLOps practices for continuous model deployment. The goal is better performance, lower cost, and faster iteration cycles.

We design workload-specific cloud allocation rather than running everything everywhere. Training might leverage AWS P5 instances for cost efficiency, while inference runs on Azure for proximity to enterprise users, and data processing uses GCP BigQuery for analytics. We use Kubernetes-based orchestration with KServe or Ray Serve to maintain portability across providers. The key is avoiding vendor lock-in on the model serving layer while strategically using managed services where they provide genuine advantages.

Retrieval-Augmented Generation combines large language models with your proprietary data. Instead of fine-tuning a model on every document, RAG retrieves relevant passages from a vector database at query time and feeds them to the model as context. This means the model always references your latest data without retraining, hallucinations are reduced because answers are grounded in actual documents, and you maintain full control over what information the model can access. For enterprises with constantly evolving knowledge bases, RAG is typically the fastest path to production-grade AI.

We start with an audit of your existing pipeline — models, data flows, dependencies, and integrations. Then we define a target architecture that preserves what works while replacing bottlenecks. Migration typically happens in phases: first, we containerise existing models for deployment flexibility; second, we modernise the data pipeline with streaming ingestion; third, we replace custom training loops with managed services where appropriate. Each phase delivers standalone value so you see improvements incrementally rather than waiting for a big-bang cutover.

Cost optimisation happens at multiple layers. At the infrastructure level, we use spot and preemptible instances for training, right-size GPU allocations, and implement auto-scaling that scales to zero during off-hours. At the model level, we distil large models into smaller, cheaper variants for routine tasks and route only complex queries to expensive foundation models. At the pipeline level, we cache embeddings, batch inference requests, and deduplicate redundant processing. Clients typically see 40 to 70 percent cost reductions within the first quarter.

We separate the model serving layer from cloud-specific infrastructure using open standards. Models are packaged in ONNX or standard container formats. Orchestration uses Kubernetes rather than proprietary services. Data pipelines use Apache Spark or Beam for portability. Where we do use managed services like Azure OpenAI or Amazon Bedrock, we abstract them behind a unified API gateway so switching providers requires changing configuration, not rewriting application code. This gives you leverage in vendor negotiations and flexibility as the market evolves.

We design for horizontal scaling from day one. Inference services auto-scale based on request queue depth, not just CPU utilisation, which prevents latency spikes during traffic bursts. We implement request batching to maximise GPU utilisation, asynchronous processing queues for non-real-time workloads, and global load balancing for multi-region deployments. For training workloads, we use distributed training across multiple nodes with gradient synchronisation optimised for your specific model architecture.

Yes, and this is how we approach most engagements. We deploy the modernised pipeline alongside your existing system, route a percentage of traffic to the new stack for validation, and gradually increase that percentage as confidence grows. This blue-green or canary approach means zero downtime and easy rollback. Your existing integrations continue working through the same API contracts while the underlying implementation improves. We have migrated production systems serving millions of daily requests without any user-facing disruption.

Every deployment includes comprehensive observability: model performance metrics like latency, throughput, and accuracy drift; infrastructure metrics for GPU utilisation, memory, and network; business metrics tracking user satisfaction and task completion rates; and cost dashboards showing spend per model, per team, and per use case. We integrate with your existing monitoring stack — Datadog, Grafana, CloudWatch, or Azure Monitor — and set up alerting for anomalies that indicate model degradation, data drift, or cost overruns before they become incidents.

We redesign data pipelines to support both batch and real-time processing. Typically this means implementing a lakehouse architecture with Delta Lake or Apache Iceberg for unified storage, streaming ingestion with Kafka or Kinesis for real-time data, and orchestration with Airflow or Dagster for batch workflows. We pay special attention to data quality — implementing validation, lineage tracking, and automated testing — because model quality is directly bounded by data quality. The result is a pipeline that delivers clean, fresh data to your AI models continuously.