Enterprises are faced with overwhelming challenges trying to manage their hybrid and multi-cloud networks. AIOps platforms have emerged as a potential solution to help network operators manage their complex environments. At Selector, we believe that bringing network and AI/ML expertise together is critical to bridging the gap between network teams and AI teams to deliver solutions to these challenges. This presentation will cover how Selector brings a data-centric approach to AI/ML and the Selector AI/ML stack, which powers its network-aware, closed-loop AIOps platform. Surya Nimmagadda, Chief Data Scientist at Selector, highlighted his company's unique blend of deep networking knowledge and AI/ML proficiency. This combination addresses a significant disconnect in network operations where specialized network teams often lack AI skills, and AI teams lack crucial network context. Selector aims to fill this void by providing network-specific observability, emphasizing that direct customer collaboration and feedback are paramount, as "without the feedback, there is no AI."

Selector's foundational philosophy is a "data-centric approach" to AI/ML, prioritizing meticulous data curation and cleaning over constant model iteration. The company believes that once data is properly prepared and understood—likened to refining "oil"—models can perform exceptionally well. The platform supports flexible deployment options, including Selector's public cloud, on-premises behind corporate firewalls for data security, or hybrid models, all leveraging a Kubernetes-based architecture. This allows for rapid, near real-time insights, with processing times often less than five to ten minutes, which is critical for dynamic network environments. Selector maintains strict data privacy, ensuring that each customer's data is housed in a dedicated instance, preventing cross-customer data leakage and tailoring models specifically to that customer's environment rather than relying on broad anonymization.

The Selector AI/ML stack is structured in four layers: Ingest, Enrichment, Network Intelligence, and Agent TKI. The Ingest layer is designed to handle millions of diverse data points per minute from various sources—metrics, logs, events, and unstructured data—across multi-vendor environments, using push, pull, API, or message bus mechanisms. The Enrichment layer, considered Selector's "secret sauce," automatically cleans, normalizes, and contextualizes this raw data through a declarative ETL (Extract, Transform, Load) system, establishing crucial network relationships and metadata. The Network Intelligence layer then employs traditional, explainable machine learning models like statistical and regression analysis for metrics, and natural language processing (NLP) for logs. This layer establishes baselines, identifies anomalies, and translates disparate log messages (e.g., "link down" across different vendors) into unified, contextualized events, often correlating hundreds of individual anomalies into a single, actionable insight. Finally, the Agent TKI layer utilizes these highly refined and correlated insights to interact with Large Language Models (LLMs), generating actionable recommendations, automating responses, and reducing operational fatigue by transforming complex data into clear, concise guidance for network operators.

Presented by Surya Nimmagadda, Chief Data Scientist. Recorded live at AI Field Day 8 in San Jose, California on May 13, 2026. Watch the entire presentation at https://techfieldday.com/appearance/selector-ai-presents-at-ai-field-day-8/or visit https://TechFieldDay.com/event/aifd8/ or https://Selector.AI for more information.