Introducing the Techsplainers by IBM podcast, your new podcast for quick, powerful takes on today’s most important AI and tech topics. Each episode brings you bite-sized learning designed to fit your day, whether you’re driving, exercising, or just curious for something new.
This is just the beginning. Tune in every weekday at 6 AM ET for fresh insights, new voices, and smarter learning.
Introducing the Techsplainers by IBM podcast, your new podcast for quick, powerful takes on today’s most important AI and tech topics. Each episode brings you bite-sized learning designed to fit your day, whether you’re driving, exercising, or just curious for something new.
This is just the beginning. Tune in every weekday at 6 AM ET for fresh insights, new voices, and smarter learning.
This episode of Techsplainers explores AI Model Lifecycle Management, the comprehensive methodology for managing artificial intelligence models throughout their entire lifecycle. We discuss why a structured approach to AI deployment is critical for enterprise success, especially when decisions made by AI systems can significantly impact business outcomes. The podcast outlines the four main stages of the AI pipeline: collect (making data accessible), organize (creating an analytics foundation), analyze (building AI with trust), and infuse (operationalizing AI across business functions). We also examine the essential components of effective AI lifecycle management, including data governance, quality assurance, fairness evaluation, and explainability. The episode concludes by highlighting the key features needed in AI management tools—from ease of model training and deployment at scale to comprehensive monitoring capabilities—using IBM Cloud Pak for Data as an illustrative example of an end-to-end platform designed to increase the throughput of data science activities and accelerate time to value from AI initiatives.
Find more information at https://www.ibm.com/think/podcasts/techsplainers
Narrated by Ian Smalley