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Overview

Rotavision integrates with AWS for data access, model monitoring, and LLM routing.

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S3 Integration

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Direct Access

Rotavision can read data directly from S3: 
from rotavision import Rotavision

client = Rotavision()

# Analyze data from S3
result = client.vishwas.analyze(
    model_id="my-model",
    dataset={
        "data_url": "s3://my-bucket/predictions.parquet",
        "aws_credentials": {
            "access_key_id": "AKIA...",
            "secret_access_key": "...",
            "region": "ap-south-1"
        }
    }
)
For production, use IAM roles:
  1. Create an IAM role with S3 read access
  2. Add Rotavision’s AWS account as trusted entity
  3. Configure in Rotavision dashboard
# No credentials needed - uses assumed role
result = client.vishwas.analyze(
    model_id="my-model",
    dataset={
        "data_url": "s3://my-bucket/predictions.parquet"
    }
)

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SageMaker Integration

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Monitor SageMaker Endpoints

from rotavision.integrations.aws import SageMakerMonitor

# Create monitor for SageMaker endpoint
monitor = SageMakerMonitor(
    endpoint_name="my-sagemaker-endpoint",
    rotavision_api_key="rv_live_...",
    aws_region="ap-south-1"
)

# Automatically logs inferences to Guardian
monitor.start()

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SageMaker Pipeline Integration

Add Rotavision to your SageMaker Pipeline: 
from sagemaker.workflow.steps import ProcessingStep
from rotavision.integrations.aws import RotavisionProcessor

# Fairness analysis step
fairness_step = ProcessingStep(
    name="FairnessAnalysis",
    processor=RotavisionProcessor(
        api_key="rv_live_...",
        role=role,
        instance_type="ml.m5.xlarge"
    ),
    inputs=[
        ProcessingInput(source=predictions_uri, destination="/opt/ml/processing/input")
    ],
    code="analyze_fairness.py"
)

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Bedrock Integration

Route Sankalp requests to AWS Bedrock: 
# Sankalp automatically routes to Bedrock for supported models
response = client.sankalp.proxy(
    model="claude-3-sonnet",  # Routes to Bedrock
    messages=[{"role": "user", "content": "Hello"}],
    routing={
        "provider_preference": ["bedrock", "anthropic"],
        "data_residency": "india"
    }
)

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Configure Bedrock Access

In Rotavision dashboard or via API: 
client.integrations.configure(
    provider="aws_bedrock",
    config={
        "access_key_id": "AKIA...",
        "secret_access_key": "...",
        "region": "us-east-1"  # Bedrock region
    }
)

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CloudWatch Integration

Export Rotavision metrics to CloudWatch: 
from rotavision.integrations.aws import CloudWatchExporter

exporter = CloudWatchExporter(
    namespace="Rotavision/ML",
    region="ap-south-1"
)

# Metrics automatically pushed to CloudWatch
monitor = client.guardian.create_monitor(
    model_id="my-model",
    metrics=["prediction_drift", "latency_p99"],
    exporters=[exporter]
)

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Terraform Module

Deploy Rotavision integration with Terraform: 
module "rotavision" {
  source = "rotavision/integration/aws"
  version = "1.0.0"

  rotavision_api_key = var.rotavision_api_key

  # S3 buckets to grant access
  s3_buckets = [
    "my-ml-data-bucket",
    "my-predictions-bucket"
  ]

  # SageMaker endpoints to monitor
  sagemaker_endpoints = [
    "prod-recommendation-endpoint",
    "prod-fraud-detection-endpoint"
  ]
}

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IAM Policies

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Minimal S3 Policy

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "s3:GetObject",
        "s3:ListBucket"
      ],
      "Resource": [
        "arn:aws:s3:::my-bucket",
        "arn:aws:s3:::my-bucket/*"
      ]
    }
  ]
}

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SageMaker Monitoring Policy

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "sagemaker:InvokeEndpoint",
        "sagemaker:DescribeEndpoint",
        "logs:CreateLogGroup",
        "logs:CreateLogStream",
        "logs:PutLogEvents"
      ],
      "Resource": "*"
    }
  ]
}