Case Study
Enterprise Agentic Machine Learning Platform
Built for Odine -- a global telecommunications and enterprise systems company -- to transform how enterprise teams approach ML development through autonomous AI agents.
Telecom enterprises need powerful ML capabilities but face significant barriers to adoption.
Traditional ML development requires weeks of coding, testing, and iteration -- creating bottlenecks that delay time-to-value for business-critical ML initiatives.
Enterprise telecom data is highly sensitive. Cloud-based ML platforms create unacceptable risk through data exposure to third-party services and external API calls.
Hiring and retaining specialised ML engineers is costly and competitive. Domain experts with business knowledge often lack the technical ML skills to build production models.
An enterprise-grade Agentic ML platform powered by locally-deployed Qwen 2.5 Coder language models (7B-32B parameters) that generates production-ready ML code through natural language interaction.
Securely ingest enterprise datasets into the on-premise platform environment.
Interact with AI agents via natural language to define ML objectives and constraints.
Autonomous agents generate, validate, and optimise production-ready ML code.
Real-time monitoring with automatic corrective actions when issues are detected.
Comprehensive analytics dashboard with pipeline logs and model performance metrics.
Measurable outcomes delivered for Odine's enterprise ML operations.
Development Timeline
ML pipeline development reduced from weeks of manual coding to minutes of natural language interaction.
Pipeline Runs
Support for up to 6 concurrent ML pipeline executions for maximum throughput and team productivity.
On-Premise
Complete on-premise deployment with zero data exposure to external services or cloud APIs.
ML Integration
Native integration with scikit-learn, XGBoost, TensorFlow, and PyTorch for comprehensive ML coverage.
The platform features an intelligent recovery mechanism that continuously monitors pipeline execution in real time. When errors or performance degradation are detected, the system automatically applies corrective actions -- including code regeneration, parameter tuning, and fallback strategies -- without human intervention.
A comprehensive analytics interface provides full visibility into ML operations. Teams can track pipeline performance, debug issues, and measure the effectiveness of the agentic system through interactive visualisations and detailed logs.
GIS Analytics delivered an enterprise-grade ML platform that fundamentally changed how our teams approach machine learning development. The on-premise deployment was critical for our data security requirements.
Odine Labs
Telecommunications & Enterprise Systems
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