HeatWave Hot Takes: The Power of ML and GenAI

In this episode, leFred and Scott welcome Jayant Sharma and Sanjay Jinturkar to the Sakila Studio for an insightful conversation on machine learning and generative AI within HeatWave. Discover how these cutting-edge technologies are integrated, what makes HeatWave unique, and how organizations can leverage its capabilities to unlock new possibilities in data and AI. Tune in for practical insights, real-world use cases, and a closer look at the future of analytics. ------------------------------------------------------------ Episode Transcript: 00:00:00:00 - 00:00:32:01 Welcome to Inside MySQL: Sakila Speaks. A podcast dedicated to all things MySQL. We bring you the latest news from the MySQL team, MySQL project updates and insightful interviews with members of the MySQL community. Sit back and enjoy as your hosts bring you the latest updates on your favorite open source database. Let's get started! 00:00:32:03 - 00:00:54:17 Hello and welcome to Sakila Speaks, the podcast dedicated to MySQL. I am leFred and I'm Scott Stroz. Today for the second episode of season three dedicated on AI. I am pleased to welcome Sanjay Jinturkar. Sorry if I pronounce it badly. No, you did it right. Hi there. Thank you. So Sanjay is the senior director at Oracle based in New Jersey. 00:00:54:19 - 00:01:21:13 He leads product development for it with AutoML and GenAI with a strong focus on integrating these technologies directly into each HeatWave database. And Sanjay has been instrumental in enhancing HeatWave's machine learning and GenAI tool sets, enabling use case like predictive maintenance, fraud detection and intelligent dicument and Q&A. And also we have a second guest today. 00:01:21:13 - 00:01:48:21 It's a Jayant Sharma. Hi, Jayant. Hello. So Jayant Sharma is senior director of product management at Oracle. He has over 20 years of experience in databases, spatial analytics and application development. He's currently focused on the product strategy and design of the Heatwave MySQL managed services offering. Hey Fred. Thank you, both of you for joining us today. So I'm going to dive right in with the question for Jayant. 00:01:48:23 - 00:02:12:14 Why did Oracle decide to integrate machine learning in generative AI capabilities directly into HeatWave? Thank you Scott, first for this opportunity. And yes, we have to start with first, you know, talking about MySQL, right? MySQL is the world's most popular open source database. And what do all of these customers, the thousands of customers that they have, do with it? 00:02:12:16 - 00:02:47:05 They manage a business process. They manage their enterprise, right? Their focus is on what they want to do, why they want to do it, and not so much the how. That's what MySQL makes it easier. And Heatwave is a managed service on MySQL. Okay, so as folks are modernizing their applications, taking advantage of new technology, they want to be able to use new workloads, new analytics, and modernize their business processes, make it more efficient, make it more effective. 00:02:47:07 - 00:03:09:17 In order to do that, they want to do things such as machine learning and use the benefits of generative AI. However, what they want to focus on, as we said, is what they want, why they want to do it and not the how. So they don't want to have to think about. I have all of this data that's potentially a goldmine. 00:03:09:19 - 00:03:40:07 How do I extract nuggets from it, and how do I safely move it and transfer in between the best of breed tools? I want to be able to do things where they are. I want to bring the capabilities, these new capabilities to my data. I don't want to take my data to where those capabilities are exposed, right? That is why we made it possible to do machine learning and GenAI where your gold mine is, where your data is in MySQL in Heatwave. 00:03:40:09 - 00:04:06:07 Awesome. Thank you. So, I would like to ask you to Sanjay, then. How Do the the, machine learning engine in the HeatWave, offer differ from, using external machine learning pipelines with the with the data we have in the database? It differs in a couple of weeks, specifically how the models are built, who builds them and where they are built. 00:04:06:09 - 00:04:46:09 So our pipeline, we provide, automated pipeline, which can take your data in MySQL database or Lakehouse, and then automatically generate the model for you. So it does the, usual tasks of pre-processing, hyperparameter optimization, and, data cleansing, etc. automatically so that the user doesn't have to do that. We would even go ahead and do, explanations for you in certain use cases, given that this is automated, a big side effect of that is users don't need to be experts in machine learning. 00:04:46:11 - 00:05:16:08 What they need to focus on is their business problem, and how that business problem maps onto one of the features that we provide. From there onwards, the pipeline takes over and generates the models for it. And the third piece ...