871: NoSQL Is Ideal for AI Applications, with MongoDB’s Richmond Alake

Agentic AI, AI success strategies, and why flexibility will be so important to keep up with the AI market: Jon Krohn talks to Richmond Alake about the NoSQL database MongoDB, including why it’s a great addition to your toolkit for developing (agentic) AI applications, with a look under the hood at its native vector database. Richmond also talks about why he expects multi-agent AI architectures to go mainstream in 2025.  Additional materials: www.superdatascience.com/871 This episode is brought to you by the Dell AI Factory with NVIDIA and by ODSC, the Open Data Science Conference. Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information. In this episode you will learn: (04:10) How Richmond became a Staff Developer Advocate (07:40) How NoSQL database differs from a relational database (16:50) The advantages of working with the cloud-based MongoDB Atlas (32:26) Richmond’s predictions for agentic AI (40:38) How to create an effective AI strategy

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The latest machine learning, A.I., and data career topics from across both academia and industry are brought to you by host Dr. Jon Krohn on the Super Data Science Podcast. As the quantity of data on our planet doubles every couple of years and with this trend set to continue for decades to come, there's an unprecedented opportunity for you to make a meaningful impact in your lifetime. In conversation with the biggest names in the data science industry, Jon cuts through hype to fuel that professional impact. Whether you're curious about getting started in a data career or you're a deep technical expert, whether you'd like to understand what A.I. is or you'd like to integrate more data-driven processes into your business, we have inspiring guests and lighthearted conversation for you to enjoy. We cover tools, techniques, and implementation tricks across data collection, databases, analytics, predictive modeling, visualization, software engineering, real-world applications, commercialization, and entrepreneurship − everything you need to crush it with data science.