SageMaker
Train, tune, and deploy ML models with automated pipelines.
AWS Machine Learning — Build, Train, Deploy ML
A suite of AWS services covering the full ML lifecycle: training (SageMaker), generative AI (Bedrock), and large-scale inference deployment.
ML/AI at enterprise scale
AWS provides a full range of ML services from low-level (GPU EC2 training) to high-level (SageMaker pipelines, Bedrock foundation models).
NEAX advises on ML/AI architecture integrated with PLM/MES data, suited to quality prediction, predictive maintenance, and generative design.
200+
ML services
Foundation
Models via Bedrock
Multi-GPU
Training scale
Why choose AWS Machine Learning?
Bedrock
Access foundation models (Claude, Llama, Titan) via a managed API.
GPU & Trainium
Cost-optimized GPU/Trainium compute infrastructure for training.
Enterprise security
VPC, encryption, audit trail, and fine-grained IAM.
Capabilities of AWS Machine Learning
SageMaker Studio
A Jupyter IDE for managing experiments and models.
Bedrock
Foundation model API: Claude, Llama, Titan, Cohere.
SageMaker Pipelines
Automated MLOps from data to deployment.
Vector Database
OpenSearch and Aurora pgvector for RAG.
Inference Endpoints
Deploy serverless or dedicated endpoints per SLA.
Customer testimonials
Vietnamese manufacturer
NEAX deployed predictive quality based on SageMaker + MES data.
Enterprise chatbot
A RAG chatbot on Bedrock + Aurora pgvector for internal knowledge.
Frequently asked questions
How does it compare to Google Vertex / Azure ML?
AWS has the broadest ecosystem (compute, storage, network). NEAX advises on cloud choice based on customer needs.
What's the approximate cost?
It depends on workload — NEAX supports a small POC first, then estimates scaling costs stage by stage.
Discuss AWS Machine Learning with a NEAX expert
NEAX is a PTC implementation partner in Vietnam — roadmap consulting, business-driven configuration, training, and operational support.