A Grand Failure At MGM
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
Fivetran: The Enterprise Data Platform
Revelate: Finding Success with Data Products
Pivotree and Syndigo: Unlocking Value Through Content Strategy
Unlike OpenAI’s ChatGPT, Pryon is Purpose-Built for the Enterprise
Case study: Fortune 500 Manufacturer Triples Accuracy in IT Support Answers with Pryon
Pryon: The State of AI for the Enterprise
Prophecy: How Low-Code Data Engineering can 10x Productivity on the Data Lakehouse
Nile: IDC’s Key Criteria When Selecting a NaaS Solution
CyberMaxx: Monitoring Cyber Risk and How Offence Fuels Defence
Want a Good Job? UK Students Say You Need a Degree