Build and Deploy AI/ML Models on Enterprise Cloud Infrastructure
Cloud AI/ML services — AWS SageMaker, Azure Machine Learning, Google Vertex AI — provide the managed infrastructure for training, deploying, and scaling machine learning models without managing the underlying compute. RLM advises on platform selection, cost optimization, and the governance model that keeps ML workloads performant and controlled.
What RLM Delivers on Cloud AI/ML
Cloud ML platforms eliminate the infrastructure barrier to enterprise ML — but selecting the right platform, designing cost-efficient training pipelines, and building the MLOps foundation for reliable model deployment requires expertise that goes beyond the documentation.
How We Approach Cloud AI/ML
Every Cloud AI/ML engagement starts with what you have today and ends with something running in production — with independent evaluation in between.
ML Platform Fit Assessment
We evaluate your ML workloads — training scale, model types, deployment latency requirements, team expertise — against the capabilities and costs of AWS SageMaker, Azure ML, Vertex AI, and Databricks to identify the optimal platform.
MLOps Architecture Design
We design the MLOps architecture — feature stores, model registry, training pipelines, deployment infrastructure, and monitoring — that provides the operational foundation for reliable, reproducible ML.
Cost Optimization for ML Workloads
Training large models is expensive; inference at scale is even more so. We advise on spot/preemptible instance strategies, training job optimization, inference endpoint right-sizing, and the cost governance model for ML workloads.
Model Governance & Compliance
ML models in regulated industries require documentation of training data, model behavior, bias assessment, and change management. We design the model governance framework appropriate for your use cases and compliance requirements.
Cloud AI/ML Evaluation Criteria
What follows is the Cloud AI/ML evaluation checklist we actually use — the criteria that predict outcomes rather than demo well.
GPU Instance Availability & Cost
GPU compute for ML training is expensive and sometimes constrained. Evaluate spot/preemptible GPU availability, on-demand pricing, and Reserved Instance options for your training workload profile.
Managed Service vs. Custom Infrastructure
Managed services (SageMaker, Vertex AI) reduce operational overhead but constrain customization. Evaluate the trade-off based on your team's ML infrastructure expertise and the degree of customization your workloads require.
Feature Store Quality
Feature engineering consistency between training and serving is critical for model performance in production. Evaluate the feature store capabilities — online and offline serving, time-travel, sharing — on each platform.
Model Monitoring & Drift Detection
Models degrade as data distributions change. Evaluate built-in model monitoring capabilities — data drift detection, performance degradation alerting, and automated retraining triggers.
Multi-Framework Support
TensorFlow, PyTorch, Scikit-learn, XGBoost — different teams use different frameworks. Evaluate the breadth of framework support and the operational overhead of managing multiple frameworks on the same platform.
Inference Latency & Throughput
Real-time inference has hard latency requirements. Evaluate inference endpoint performance — p50/p99 latency, throughput under concurrent load — before committing to a platform for latency-sensitive applications.
"Our migration was stalled for months. RLM came in, assessed the gaps, and helped us select a managed services partner that got us across the finish line in 60 days."
We are paid by the provider you choose, which means we have no reason to steer you toward any particular one.
Where This Matters Most
Sector-specific considerations we see repeatedly in cloud and managed services engagements.
A Sample of the Cloud & Managed Services Providers We Evaluate
RLM is vendor neutral. These are among 600+ providers in our evaluation set — inclusion here is not an endorsement, and we are paid by the provider you choose, not by any provider in particular. How that works →
Ready to Get Cloud AI/ML Right?
Start with a no-cost conversation with an RLM cloud advisor — vendor neutral, no agenda, just clarity on the right path forward.
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