Solutional Blog
Three Critical Questions That Guide An AI Initiative
AI is transforming network operations, but success starts with asking the right questions. What specific problem will AI solve better than existing tools? And is your network data ready - clean, connected, and real-time? Without clear answers, AI in NetOps risks becoming a costly experiment rather than a strategic advantage.
Becoming a Next-Gen Network Engineer in a Software-Centric World
As automation, cloud, and AI reshape the networking landscape, the role of the network engineer is evolving fast. Introducing the Next-Gen Network Engineer (NGNE) learning path - a practical roadmap to help engineers build modern skills in automation, DevOps, cloud, and observability. Whether you’re just starting or ready to level up, this guide shows how to embrace disruption and thrive in today’s software-driven world.
Designing a Scalable Data Pipeline for AI in Network Operations
Turning network telemetry into AI-driven insight takes more than intuition - it takes a robust data pipeline. In this article read about the architecture behind real-time AI in NetOps, from telemetry ingestion to model inference, and why data engineering is the foundation of any successful AI initiative.
The Importance of Data Pipelines for AI in Network Operations
AI won’t fix your network if the data feeding it is broken. In this post, learn why building a clean, fast, and reliable data pipeline is the first and most critical step for applying machine learning in network operations.
Why Coding Still Matters in the Age of AI Agents
AI agents can crank out network-automation scripts, but you still need coding skills to guide, debug, and extend them, making Python skills as crucial as ever even in an agent-driven future.