Building autonomous network operation using deep learning and AI
Who is this presentation for?
- IT architects, CIOs, CISOs, data scientists, and machine learning engineers
The growing adoption of mobility, IoT, and the cloud significantly increases the impact of network infrastructure on enterprise businesses and creates new challenges for traditional human-driven network operation. You’ll learn empirical experiences of using ML/DL and AI to build the first autonomous enterprise network operation solution that provides visibility, troubleshooting, reporting, and maintenance of an enterprise network.
Jisheng Wang explores detailed architecture with an emphasis on measurement, detection, orchestration and action. The process is to collect various data (e.g., logs, stats, and events) to measure the performance of both infrastructure (e.g., wireless, wired, and internet) and end-user experiences. Based on these metrics, the normal baseline of end-to-end network components, detected anomalies, and real-time incidents is profiled. Spatial and temporal analysis is applied to determine the scope of the impact (e.g., user, site, or organization) and the root cause of the incident. The intelligent system then suggests or takes actions automatically.
He also describes the two ML models built for automated actions. The natural language processing (NLP)-based machine log analysis solution automatically identifies the root cause of machine hardware or software defects using syntactic analysis (term frequency-inverse document frequency (TFIDF) and Doc2vec) and topic modeling. The other is a reinforcement learning-based WiFi radio management solution that automatically tunes radio configuration based on environmental and user dynamics, as well as a user experience-based reward mechanism.
Jisheng shares his experiences and the lessons he learned, including how to use visualization, model interpretation, and feedback to keep humans in the loop while developing machine intelligence; how to build human trust in the step-by-step process of automation, augmentation, and autonomy; and how to accelerate knowledge learning and sharing across enterprises in a SaaS environment without compromising individuals’ privacy information.
What you'll learn
- Understand the opportunities and challenges of enterprise network operation in the next decade
- Learn about architecture and components for AI-driven autonomous network operation
- See two ML examples of using deep learning and reinforcement learning for automated actions
- Hear experiences and lessons learned from applying ML and AI to develop SaaS-based enterprise solutions
Jisheng Weng is the head of data science at Mist. He has over 10 years of extensive experience applying state-of-the-art big data and data science technologies to solving challenging enterprise problems, including security, networking, and IoT. He also leads the development of Marivs—the first AI-driven virtual network assistant that automates visibility, troubleshooting, reporting, and maintenance of enterprise networking. Previously, Jisheng was the senior director of data science in the CTO office of Aruba, a Hewlett Packard Enterprise company, since its acquisition of Niara, where he had been the chief scientist and led the overall innovation and development effort in big data infrastructure and data science, and invented the industry-first modular and data-agonistic user and entity behavior analytics (UEBA) solution which is widely deployed today among global enterprises; and a technical lead in Cisco over various security products. He earned his PhD in electric engineering from Penn State University, and is a frequent speaker at different AI/ML conferences, including the O’Reilly Strata AI Conference, Frontier AI, Spark Summit, Hadoop Summit, and BlackHat.
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