API World + AI DevWorld

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 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 main components: measurement, detection, orchestration and automated/suggested actions. Then we dive deeper into the example of anomaly detection where we use unsupervised confident deep multivariate models to automatically baseline and detect the user-impacting connectivity issues inside enterprise network together with other contextual information from scope and root cause analysis to make the AI results interpretable and actionable for end users. In addition, we share some results of the anomaly detection models in practice across thousands of sites. We also describe an anomaly detection example that we use deep learning models to automatically baseline and detect the user-impacting connectivity issues inside enterprise network, together with other contextual information from scope and root cause analysis to make the AI results interpretable and actionable for end users.

At last, the speakers will discuss some common challenges and best practices people encounter while developing ML/AI-driven enterprise applications, including:
1. Building the cloud-native analytics infrastructure for agility, scalability and visibility
2. Leveraging visualization, model interpretation, and feedback to keep humans in the loop while developing machine intelligence;
3. Building human trust in the step-by-step process of automation, augmentation and autonomy
4. Accelerating knowledge learning and sharing across enterprises in a SaaS environment without compromising individual privacy.