The talk will highlight how Machine Learning techniques can be used to address different aspects of the operation and control of NFV and propose future OPNFV activities in this area. First, Diego will introduce how Machine Learning is being applied by the CogNet project to address intent-based networking, and discuss the architecture defined there as a potential framework for future ML integration. Glen will demonstrate a policy-based system for automating VNF scaling using performance data collection and analytics with machine learning (ML), based on OPNFV Brahmaputra and the underlying OpenStack telemetry system (Ceilometer), as well as the open-source Apache Kafka, Apache Zookeeper and Apache Spark streaming and MLlib libraries. Available as open-source, it combines predictive and reactive inputs to make the VNF scaling decision and trigger action in the MANO stack. The presentation will provide an overview of the system, demonstrate the VNF auto-scaling use case and discuss how this system will fit into a future OPNFV release.