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Enterprise/Complex6 monthsMarketing & Advertising

Enterprise ML Platform for Marketing Agency

Built a scalable machine learning platform enabling data-driven marketing decisions for Fortune 500 clients, processing 500M+ events per day with real-time campaign optimization.

Machine LearningData PlatformReal-time Processing

The Challenge

A Fortune 500 marketing agency needed to scale their data science capabilities to serve 20+ Fortune 500 clients with real-time marketing optimization.

Pain Points:

  • Model deployment took weeks of manual engineering work
  • No standardized way to serve ML models to client campaigns
  • Data scientists spent 80% of time on infrastructure instead of modeling
  • Unable to process and react to marketing events in real-time
  • Each client project required custom infrastructure setup

The Solution

We built a comprehensive ML platform that standardized model deployment, enabled real-time inference, and allowed data scientists to focus on modeling rather than infrastructure.

Our Approach:

  • Designed and implemented model registry with version control and lineage tracking
  • Built automated deployment pipeline from notebook to production in hours
  • Implemented real-time feature store for sub-100ms model serving
  • Created monitoring and observability framework for model performance
  • Established MLOps best practices and training for the data science team

The Results

The platform transformed how the agency delivered ML-powered marketing campaigns, reducing time-to-market and enabling sophisticated optimization at scale.

Deployment Time
Weeks → Hours
Reduced model deployment from weeks to hours
Event Processing
500M+ per day
Real-time processing of marketing events
Client Campaigns
20+ Fortune 500
Supporting campaigns across major clients
Data Science Productivity
4x increase
DS team spending 80% time on modeling vs 20% before

Technical Stack

Technology Stack:

  • Cloud: AWS (SageMaker, Lambda, ECS)
  • Data Platform: Snowflake, Kafka, Redis
  • ML Tools: MLflow, Feature Store, Model Registry
  • Monitoring: Datadog, custom model drift detection
  • Infrastructure: Terraform, Docker, Kubernetes

Technical Highlights:

  • Real-time feature engineering with sub-100ms latency
  • Automated A/B testing framework for model comparison
  • Built-in model monitoring and drift detection
  • Self-service deployment portal for data scientists
  • Automated training pipelines with hyperparameter optimization

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