1. Key Takeaways

Owned by Lei Huang
Last updated: May 25, 2021
1 min read

  • The industry needs to deliver new services quickly and efficiently.

  • A shared understanding of intelligent networking needed to optimize interoperability.

  • Some AI/ML is in place, but more research and development needed to establish best practices.

  • The Open Source community can play a key role in furthering the development of frameworks and best practices.

  • Start simple – Learn to trust your data and results

  • Address the low hanging fruit first -- Anomalies, Forecasts, Statistical Models

    • Creating the data lakes is difficult

    • Within a company across business units

  • Security and privacy concerns hamper data gathering efforts

  • Need to create cross industry anonymized data. Creating a shared data model to test the algorithms

Be prepared for bias and unintended or unexpected results if the AI/ML system is too much of a black box.