Telecommunications Case Study

From 6 Months to 1 Month

How the largest telco in Latin America industrialized their ML lifecycle — cutting model deployment time by 84% through a standardized data layer, Feature Store, and end-to-end MLOps platform.

84%

Faster Deployments

6→1

Months to Deploy

Multi

Region Rollout

M+

Customer Predictions

The Challenge

A broken model deployment lifecycle at continental scale

6 months

Average time from model development to production

Duplicated

Feature engineering efforts with inconsistent logic across teams

Siloed

Models couldn't be reused across countries due to naming and data variations

No standard

No unified data layer or governance framework across regions

The Solution

A multi-faceted approach to industrialize the entire machine learning lifecycle.

01

Standardized Data Layer

Unified taxonomy across all countries with consistent naming, classifications, and governance.

02

Feature Store

Cloud-native single source of truth for features, ensuring training/serving consistency.

03

MLOps Platform

Secure development environment with automated CI/CD, environment segregation, and monitoring.

04

Team Enablement

Structured training and cultural shift toward engineering rigor and collaboration.

05

Model Migration

Systematic migration from legacy on-premise infrastructure to cloud-native platform.

06

Multi-Region Architecture

Scalable cloud architecture managing petabytes of data with real-time prediction capabilities.

Implementation Roadmap

A phased approach from data standardization to full platform adoption.

1
Phase 1

Standardized Data Layer

Conducted a comprehensive inventory across all existing ML models, cataloging every feature and its upstream data source. Developed a unified, organization-wide taxonomy with standardized names, classifications, and definitions — eliminating data silos and improving feature discoverability.

  • Full model and feature inventory across all countries
  • Unified taxonomy for data operations and transformations
  • Data quality checks and governance policies
  • Foundation for cross-border feature consistency
2
Phase 2

Feature Store Implementation

Designed, built, and deployed a cloud-native Feature Store as the single source of truth for all features — guaranteeing consistency between training and real-time serving while dramatically reducing training/serving skew.

  • Centralized cataloging of all feature formulas and metadata
  • Low-latency access for batch and real-time predictions
  • Backfilling operations and feature versioning
  • Cross-team and cross-project feature reusability
3
Phase 3

End-to-End MLOps Platform

Engineered a cloud-based MLOps platform providing data scientists with a secure, reproducible environment. Implemented fully automated CI/CD pipelines for model testing, containerization, security scanning, and deployment.

  • Secure model development environment (MDE)
  • Strict dev / staging / production segregation
  • Automated CI/CD with unit, integration, and performance tests
  • Monitoring for data drift, concept drift, and operational health
4
Phase 4

Adoption & Migration

Executed a structured training program to onboard all data scientists, then systematically migrated all legacy on-premise models onto the new cloud-native platform — standardizing deployment, governance, and monitoring.

  • Mandatory training program for all data scientists
  • Best practices for Feature Store and CI/CD adoption
  • Systematic migration of all legacy models
  • Performance and cost optimization on cloud infrastructure

Global Leadership

Leading the transformation across multiple regions and disciplines.

Cloud Architecture

Defined and implemented the scalable, multi-region cloud architecture managing petabytes of data with real-time prediction capabilities.

Team Development

Directed the growth of specialized data engineering and data science teams across North America, Europe, and South America.

System Integration

Managed the staged rollout of the prediction API, ensuring seamless integration with critical enterprise systems.

The Transformation

Before

6 months

Model Deployment Time

Duplicated

Feature Engineering

Siloed

Cross-Country Models

Manual

Deployment Process

After

1 month

Model Deployment Time

Unified

Feature Store (Single Source of Truth)

Portable

Cross-Border Model Reuse

Automated

CI/CD Pipeline with Monitoring

The Results

Model deployment time was drastically reduced by 84%, from an average of six months to just one month. This increased speed, coupled with enhanced portability, allowed other regional operations to easily adopt and test models developed elsewhere.

Establishing a standard taxonomy significantly improved collaboration by ensuring consistent terminology across teams. Within three months of the project's completion, numerous new models were successfully put into production, generating predictions for millions of customers.

The initiative established a foundation of engineering rigor and consistent terminology, rapidly accelerating the delivery of millions of customer predictions and cementing the organization's shift toward a data-driven culture.

Want Results Like These?

Every transformation starts with a conversation. Let's diagnose where your data and AI infrastructure is stuck and build a plan to scale.

View Plans