"""
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W E B
Cloud Infrastructure • Data Pipelines • AI Engineering
"""
from cloud import AWS, Lambda, S3, DynamoDB
from data import Pipelines, ETL, Analytics
from ai import MachineLearning, ComputerVision, Inference
from backend import APIs, Serverless, Microservices
from devops import CICD, Infrastructure, Automation
@dataclass
class Developer:
"""Cloud & AI Engineer based at Revolve Web"""
name: str
focus: List[str]
mission: str
developer = Developer(
name="Revolve Web",
focus=[
"Cloud infrastructure",
"Data pipelines",
"AI engineering",
],
mission="Design & build scalable AWS systems that transform raw data into intelligent products"
)
class AWSServices:
"""Designing secure, scalable, cost-efficient cloud platforms"""
compute = [Lambda EC2 ECS Step Functions API Gateway]
storage = [S3 DynamoDB Aurora ElastiCache]
messaging = [SQS Firehose EventBridge]
networking = [VPC CloudFront Route53]
security = [Cognito IAM Secrets Manager]
observability = [CloudWatch CloudTrail X-Ray]
class DataAnalytics:
"""Building ingestion pipelines, analytics layers, query systems"""
stack = [
Athena Big Data ETL Pipelines Automation
]
def transform(self, df: pd.DataFrame) -> pd.DataFrame:
"""ETL transformation pipeline"""
return df.pipe(self.clean).pipe(self.enrich)
class AIEngineering:
"""Training, deploying, optimizing ML models for production"""
platform = [SageMaker Bedrock Model Training]
models = [Random Forest Feature Engineering Training Pipelines]
computer_vision = [YOLO SAM Object Detection]
deployment = [Real-time Inference Batch Processing]
def train(self, X, y) -> Model:
"""Feature engineering + model training pipeline"""
features = self.engineer_features(X)
return RandomForestClassifier().fit(features, y)
def detect(self, image) -> List["Detection"]:
"""Run YOLO/SAM inference pipeline"""
return self.model.predict(image)
class InternalTooling:
"""Building web tools for ML ops and internal workflows"""
annotation = [Labeling Tools Training Data QA Workflows]
dashboards = [Ops Dashboards Monitoring Alerting]
admin = [Admin Panels Internal Apps Backoffice]
"""
Automation-heavy architectures:
• Serverless APIs with auto-scaling
• Scheduled data pipelines
• AI model training workflows
• Production inference systems
Recent work includes:
→ Computer-vision pipelines for real-time image detection
→ Analytics platforms over high-volume device data
→ Scalable SaaS backends
→ Web-based annotation tools for ML training data
→ Ops dashboards for infrastructure monitoring
"""
if __name__ == "__main__":
print(developer)