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.
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.
A structured advisory process — from discovery and market evaluation to negotiation and post-deployment optimization — tailored to your specific environment and objectives.
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.
We design the MLOps architecture — feature stores, model registry, training pipelines, deployment infrastructure, and monitoring — that provides the operational foundation for reliable, reproducible ML.
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.
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.
These are the dimensions that consistently separate successful deployments from costly ones — and the questions RLM will help you answer before any commitment.
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 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 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.
Models degrade as data distributions change. Evaluate built-in model monitoring capabilities — data drift detection, performance degradation alerting, and automated retraining triggers.
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.
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.
"RLM helped us rationalize our multi-cloud spend and identify over $1.2M in annual savings. Their approach was methodical and unbiased — exactly what we needed."
"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."
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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