Considerations for Enterprise AI

Let’s talk through some of the challenges that Enterprises will have with AI - from data location to GPU location, to model biases, to data privacy to training vs. execution.SHOW: 748CLOUD NEWS OF THE WEEK - http://bit.ly/cloudcast-cnotwCHECK OUT OUR NEW PODCAST - "CLOUDCAST BASICS"SHOW SPONSORS:Datadog Security Solution: Modern Monitoring and SecurityStart investigating security threats before it affects your customers with a free 14 day Datadog trial. Listeners of The Cloudcast will also receive a free Datadog T-shirt.Find "Breaking Analysis Podcast with Dave Vellante" on Apple, Google and SpotifyKeep up to data with Enterprise Tech with theCUBEAWS Insiders is an edgy, entertaining podcast about the services and future of cloud computing at AWS. Listen to AWS Insiders in your favorite podcast player. Cloudfix HomepageSHOW NOTES:An Interview with Daniel Gross and Nat Friedman on the AI Hype Cycle (Stratechery)ARE THERE EXPECTATIONS OF “OLD AI” vs. “NEW AI”?Are business leaders thinking about unique AI applications and use-cases, or just “ChatGPT-everything”?Formal data scientists vs. citizen data scientists?Will this just be an application, or have an impact on every aspect of a business and the IT industry?WILL ENTERPRISE AI BE DIFFERENT THAN CONSUMER AI? The industry is actively working on a broad set of models that can be used for different use-cases. It's commonly accepted that AI models need to be trained near the sources of data. Many businesses are concerned about including their company data into these public modelsMany businesses will want to deploy tuned models and applications in data center, public cloud and edge environments. New AI applications will be required to meet security, regulatory and compliance standards, like other business applications. FEEDBACK?Email: show at the cloudcast dot netTwitter: @thecloudcastnet