Building Autonomous Network Operation using Deep Learning and AI
Who is this presentation for?IT Architect, CIO, CISO, Data Scientist, Machine Learning Engineer
The growing adoption of mobility, IoT and cloud significantly increases the impact of network infrastructure on enterprise businesses, and creates new challenges for traditional human-driven network operation. In this talk, we will share 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.
We first introduce the detailed architecture, which includes four components: measurement, detection, orchestration and action. 1) We collect various data (e.g., logs, stats, events) to measure the performance of both infrastructure (e.g., wireless, wired, Internet) and end-user experiences. 2) Based on these metrics, we profile the normal baseline of end-to-end network components, and detect anomalies and incidents in real-time. 3) We apply spatial and temporal analysis to determine the scope of the impact (e.g., user, site, org) and the root cause of the incident. 4) The intelligent system suggests or takes actions automatically.
We also describe the two ML models we built for automated actions. First is the NLP-based machine log analysis solution which automatically identifies the root cause of machine hardware/software defects using syntactic analysis (TFIDF and doc2vec) and topic modeling. Second is a reinforcement learning-based Wi-Fi radio management solution which automatically tunes radio configuration based on environmental and user dynamics and a user experience-based reward mechanism.
Finally, we share our experiences and lessons learned. These include: 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.
Prerequisite knowledgeThis talk covers both the business opportunity and technical details (architecture and models) of autonomous enterprise network operation, so it is a good fit for both IT professionals and data engineering/data science audiences. An in-depth understanding of ML or DL is not required for this talk.
What you'll learn1. The opportunities and challenges of enterprise network operation in the next decade 2. Architecture and components for AI-driven autonomous network operation 3. Two ML examples of using deep learning and reinforcement learning for automated actions 4. Experiences and lessons learned from applying ML and AI to develop SaaS-based enterprise solutions
Dr. Jisheng Wang has 10+ years of experience applying state-of-the-art big data and data science technologies to solve challenging enterprise problems including: security, networking and IoT. He is currently the Head of Data Science at Mist Systems, and leads the development of Marivs – the first AI-driven virtual network assistant that automates the visibility, troubleshooting, reporting and maintenance of enterprise networking.
Before joining Mist, Jisheng worked as the Senior Director of Data Science in the CTO office of Aruba, a Hewlett-Packard Enterprise company since its acquisition of Niara in February 2017. As the Chief Scientist at Niara, Jisheng led the overall innovation and development effort in big data infrastructure and data science. He also invented the industry’s first modular and data-agonistic User and Entity Behavior Analytics (UEBA) solution, which is widely deployed today among global enterprises. Before that, Jisheng was a technical lead in Cisco responsible for various security products.
Jisheng received his Ph.D. in Electric Engineering from Penn State University, and is also a frequent speaker at AI/ML conferences, including: O’Reilly Strata AI, Frontier AI, Spark Summit, Hadoop Summit and BlackHat.
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