How Telcos Can Arm Themselves to be Competetive
Many organisations are struggling to get their machine learning and AI projects off the ground. The reason? There are no shortcuts to achieving machine learning capabilities. ML is solving hugely complex problems and in order to do that you've got to be prepared to experiment - and yes, maybe even fail sometimes.
In this podcast, Jeff Fletcher, Senior Director of Product Management at Cloudera, explains why we need to rethink how we operationalise ML within the enterprise. Jeff starts by talking about what technology types apply to ML and what people should keep in mind about the operational side when testing. Also, he outlines the boundaries of machine learning, AutoML, and artificial intelligence and how cloud fits into this process. Finally, Jeff explains the importance of feeding and caring for how the data is trained.
Meet the panel
Keyfactor: Why You Need to Care About Machine Identity
Intermedia: Cloud Migration is the Key to Enterprise Communications
Radware: Bot Mitigation is Key for Application Protection
Informatica: How Shifts in the Cloud are Changing the Game
Sifflet: Observability and the Future of Data Engineering
Safe Software: Low-Code/No-Code in Data Engineering
Egnyte: Keeping Up With The Next Generation of Data Privacy
Genestack: Why Data Management is the Key to Success in Life Sciences
Informatica: 4 Powerful Reasons Why Your Organization Benefits with Data Sharing
ManageEngine: Thinking Beyond ITSM and Achieving Enterprise Automation