AWS SageMaker

Not every AI problem is an LLM problem. When a client needs a predictive maintenance model, a classifier tuned to their specific document taxonomy, or a recommendation engine trained on proprietary signals, SageMaker is our default. It handles the parts of ML that are genuinely operational — distributed training jobs, model registries, A/B-tested endpoints, drift monitoring — without us writing the orchestration from scratch. We use it most often inside AWS-hosted environments where the data already lives in S3 and the security perimeter is already drawn — that's where SageMaker's integration story actually saves time.

What you get

Distributed training jobs without writing Kubernetes manifests or managing GPU fleets

Model registry tracks every version, training run, and evaluation metric — auditable in one place

Built-in A/B testing across endpoint variants — production traffic splitting without separate routing infrastructure

Model drift monitoring + automated retraining triggers — closes the production-ML feedback loop

Data already in S3 trains models without copying or reformatting — significant time saver for AWS-resident clients

Real examples

Predictive maintenance for industrial equipment

Illustrative scenario: an Australian manufacturer wants to predict equipment failure 72 hours in advance. Sensor data streams to S3, SageMaker trains an LSTM model on the historical failure data, deployed model endpoint scores live sensor data, alerts go to maintenance team. End-to-end pipeline shipped in 8 weeks.

Document classification for compliance triage

Illustrative scenario: a financial services firm needs to triage 50K+ documents per month into compliance categories with high consistency. Fine-tuned distilBERT on SageMaker, deployed as a real-time endpoint, integrated into their existing document workflow. Manual review time reduced by 70%.

MLOps for an existing ML team

Illustrative scenario: a SaaS company has data scientists shipping ML models from notebooks but no production discipline. We implement SageMaker Pipelines, the Model Registry, and Model Monitor across their existing models — bringing them into a single deployment, versioning, and observability story without disrupting model development.

Common questions

SageMaker vs Databricks vs Vertex AI?

Default to whichever cloud the data is already in. SageMaker for AWS-resident clients, Vertex AI for GCP, Databricks if the team is already running heavy data engineering there. The capabilities are roughly equivalent for the projects we run — the deciding factor is operational fit with existing infrastructure, not feature checklists.

Do you use SageMaker for LLM workloads?

Sparingly. SageMaker's strength is custom-model training and serving. For LLM workloads we typically use the model providers directly (Anthropic, OpenAI) via the Vercel AI Gateway or AWS Bedrock. SageMaker JumpStart can host open-source LLMs (Llama, Mistral) on dedicated infrastructure when data residency or fine-tuning requirements demand it.

How do you control SageMaker costs?

Three levers. One, train on spot instances where the job can tolerate interruption — savings of 50-70%. Two, use serverless inference endpoints for low-traffic models instead of always-on dedicated instances. Three, set up Model Monitor to detect drift early, so we're not retraining unnecessarily or shipping bad models that need emergency rollback.

Can we own the models — or are they locked into SageMaker?

Models live as artifacts in S3 (in formats like .pkl, .onnx, .pt) and can be deployed anywhere that runs the corresponding inference stack. We've migrated models from SageMaker to plain ECS containers and on-prem Kubernetes when the cost or security shape demanded it.

What's the typical SageMaker engagement shape?

Two patterns. One is a fresh-build: 8-12 weeks from data assessment to a production model endpoint with monitoring. Two is MLOps overhaul: 6-8 weeks adding SageMaker Pipelines, Registry, and Monitor to a team's existing models without disrupting their development workflow. Most engagements include training the in-house team to operate it after we hand over.

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