AWS SageMaker

What it is

Amazon's flagship ML platform — managed notebooks, distributed training, hosted endpoints with autoscaling, model monitoring, feature store. The AWS-native answer to AzureML.

How Vaaani uses it

• Hosting endpoints with multi-model deployment for tenant isolation
• Distributed training across spot GPUs to cut cost 70%
• Built-in monitoring for data drift in production
• JumpStart for one-click foundation model deployment

Why it makes the cut

When the customer's stack is on AWS, SageMaker keeps everything in one bill, one IAM model, one VPC.

Sample code

import sagemaker
from sagemaker.huggingface import HuggingFaceModel

model = HuggingFaceModel(
    model_data="s3://vaaani/models/v1.tar.gz",
    role=role, transformers_version="4.37",
    pytorch_version="2.1")
predictor = model.deploy(instance_type="ml.g5.xlarge")