Multicloud AI & ERP / Cloud AI: Machine Learning & Generative AI
Why it matters
Most ML projects stall between a promising notebook and a reliable service. Without a platform, every team rebuilds the same plumbing, results cannot be reproduced and nobody knows which model version is live.
How it works
The path to production
Data scientists explore in notebooks, then run training jobs on managed compute with every run tracked. Good models go into a model registry with metrics and approvals. Approved versions deploy to endpoints or batch jobs, and monitoring watches for drift, quality and cost.
AWS, Azure and Google Cloud compared
| AWS | Azure | Google Cloud | |
|---|---|---|---|
| Platform | Amazon SageMaker AI | Azure Machine Learning | Gemini Enterprise Agent Platform (formerly Vertex AI) |
| Studio | SageMaker Studio, Unified Studio | Azure ML studio | Colab Enterprise, Workbench |
| Experiment tracking | Managed MLflow | MLflow built in | Experiments, TensorBoard |
| Model registry | SageMaker Model Registry | Azure ML registries | Model Registry |
| Deployment | Real-time, serverless, async, batch | Online and batch endpoints | Online and batch prediction |
What makes each cloud different
AWS
SageMaker AI covers the full lifecycle, and SageMaker Unified Studio brings data, analytics and AI teams into one environment. Managed MLflow tracks experiments and many endpoint types suit every traffic pattern.
Azure
Azure Machine Learning integrates MLflow natively, shares models across workspaces through registries and supports blue/green deployments with traffic splitting. Microsoft Foundry sits on top for generative AI.
Google Cloud
Google renamed Vertex AI to Gemini Enterprise Agent Platform in April 2026; the ML capabilities continue under the new name, with Model Garden, custom training on GPUs and TPUs and model monitoring.
Design decisions that matter
- 01One platform per organization
Shared registry and standards across teams.
- 02Track every experiment
Reproducibility is not optional.
- 03Approval before deploy
A registry gate with metrics and sign-off.
- 04Monitor after deploy
Drift and quality, not just uptime.
Common mistakes we see
- Models deployed from laptops. No lineage, no rollback.
- Every team builds its own stack. Duplicate effort and inconsistent security.
- No monitoring. Models decay silently as data changes.
A practical 30-day plan
- Week 1
Set up the platform workspace, roles and storage.
- Week 2
Move one model's training into tracked jobs.
- Week 3
Register, approve and deploy it through the platform.
- Week 4
Add monitoring and onboard the next team.
How NTS helps
NTS sets up ML platforms on SageMaker, Azure Machine Learning and Agent Platform, with secure workspaces, experiment tracking, registries, deployment pipelines and monitoring, then onboards your teams onto them.
What you get
- An ML platform on SageMaker, Azure ML or Vertex AI
- Shared feature, training and deployment pipelines
- Model registry with approvals
- Secure workspaces for data scientists
Typical outcomes
- Faster path from experiment to production
- Lower ML infrastructure costs
- Full traceability for every model
How we work with you
- 1Assess
A focused review of your ML platform across AWS, Azure and Google Cloud. We look for the gaps we find most often: models deployed from laptops, every team builds its own stack and no monitoring.
- 2Design
A target architecture and roadmap built on the decisions that matter most: one platform per organization, track every experiment, approval before deploy and monitor after deploy.
- 3Build
Hands-on delivery alongside your team. We set up the platform workspace, roles and storage, move one model's training into tracked jobs, register, approve and deploy it through the platform, and finally add monitoring and onboard the next team.
- 4Run and improve
We maintain the platform, add capabilities and onboard new teams and use cases.
Why teams choose NTS for ML platform
- One platform, many teams
We build a shared foundation instead of every team reinventing it.
- Data scientists stay productive
Self-service workspaces with guardrails let them move fast safely.
- Governed by design
Approvals and lineage make every model traceable.
Book a free 30-minute review
Talk to an NTS architect about your ML platform on AWS, Azure and Google Cloud.