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AI Net Conference 2019

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Event Information
Event Date: April 11, 2019
Event Location: Paris, France
Event Website:
Description:

The conference addresses the current status of AI and ML deployments in IP/MPLS networks, and answers questions such as where AI and ML are actually being used today, what are the main short and middle term opportunities, and how service providers and major enterprises will transform, both on technical and organizational side, to exploit the full potential of AI and ML.

From Automatic to Autonomous Networks: Machine Learning or Deep Learning?

From automated operations to service design, AI can reduce management overheads and makes operations easier – the challenge lies is understanding exactly how much (and where) to apply AI...

  • How to reach higher levels of autonomicity?
  • What about telemetry and event driven self-reconfiguration?
  • Which methods are used to analyze the visible extracted features?
  • How to implement artificial neural networks?

Renowned experts explain what steps must telcos take to implement AI and ML in network maintenance, optimisation and planning.

Service Providers Expectations and Use Cases

The conference starts with addresses from service providers and POCs reports. Key use cases such as Security, Wireless and SD-WAN are discussed, showing how ML/AI can be used and offer results never achieved with « classic ».

A Round Table on AI Platforms for IP Networks

The session brings together data science platform editors that share their views and experience in dealing with network data, the challenges faced in this domain - and what added-values they are focusing on to help service providers and enterprise to empower their services with data, as well as the openness of their platform to a broader ecosystem (API, SDK, opensource etc.).

Participants come from network vendors, niche players and Opensource platforms.

A Tutorial on Deep Learning, Supervised Learning, LSTM, CNN an Timeseries

This tutorial covers end to end machine learning including deep learning techniques applied to Network Data. The goal is to share a methodology that sketches all the steps from raw data transformation and visualization to model generation and application including performance evaluation. The tutorial is based on real network data.