Production-Ready Framework for Telecom AI/ML Solutions
Telecom ML Framework is a production-ready framework for building AI/ML solutions to real-world telecom challenges, emphasizing domain expertise and practical problem-solving. It provides 6 production-ready ML project templates covering the most common telecom AI/ML use cases with complete technical specifications.
The framework includes domain-informed data generators embedding real telecom physics (SINR, QoE, congestion patterns), unified technical standards ensuring consistency across projects (dependencies, plotting, interpretability), and portfolio documentation demonstrating domain expertise and ML problem-solving approach.
This is a FRAMEWORK, not an implementation. It serves as both a project template generator for rapid ML project creation and a portfolio documentation hub showcasing telecom domain expertise applied to ML. All templates enforce SHAP-compatible versions and unified plotting standards.
Telecom professionals transitioning to AI/ML need structured project templates. Data scientists entering telecom domain need problem framing guidance. ML engineers building telecom analytics solutions need domain-informed data generators. Portfolio builders need to demonstrate end-to-end ML thinking.
Building production ML solutions requires proper problem framing, domain expertise, and technical standards. Off-the-shelf synthetic data lacks telecom physics. Inconsistent project structures hinder collaboration. Missing interpretability prevents business adoption.
A framework architecture: (1) 6 Use Case Specifications with problem framing, data requirements, model architectures, (2) Project Template with Python package structure, notebook templates, data generators, (3) Domain-Informed Data Generators embedding telecom physics, (4) Unified Technical Standards (SHAP compatibility, plotting, testing), (5) Portfolio Documentation demonstrating domain expertise.
Complete specs for: Churn Prediction, Root Cause Analysis, Anomaly Detection, QoE Prediction, Capacity Forecasting, Network Optimization. Each includes problem framing, ML approach, key challenges, and outputs.
Python package structure with config.py, data_generator.py, features.py, models.py. Includes notebook templates, test templates, and pyproject.toml with SHAP-compatible dependencies.
Hand-crafted generators embedding real telecom physics: SINR, Shannon capacity, congestion patterns. Control data quality and realism while maintaining interpretability.
Enforces Python 3.11+, uv dependency management, SHAP-compatible versions (numpy<2.0, xgboost<2.0), unified Seaborn plotting, pytest testing, Ruff linting.
Python 3.11+ for modern language features. uv for fast, deterministic dependency management. SHAP-compatible versions ensure interpretability. Seaborn with context switching for notebook vs presentation. Domain-informed generators demonstrate expertise vs generic synthetic data.
Deep dive into the technical implementation with annotated code examples
View Technical DetailsFraming business problems as well-defined ML tasks
Created detailed use case specifications with problem framing, ML approach, key challenges, and expected outputs. Each spec includes forbidden data (temporal leakage prevention) and label definitions.
Generating realistic telecom data with proper physics
Built hand-crafted data generators embedding real telecom physics: SINR calculations, Shannon capacity, congestion patterns. Every data point has clear causal story, maintaining interpretability.
Ensuring consistency across ML projects
Enforced unified technical standards: Python 3.11+, uv dependency management, SHAP-compatible versions, unified Seaborn plotting, pytest testing. Template provides consistent structure.
Preventing temporal leakage in time-series problems
Specifications explicitly define forbidden data (future information). Template includes temporal cross-validation guidance. Data generators respect temporal ordering.
Created comprehensive ML framework with 6 fully-specified use cases covering most common telecom ML problems. Production-ready templates enable rapid project creation. Domain-informed generators demonstrate expertise. Unified standards ensure consistency. Portfolio documentation showcases end-to-end ML thinking.