Welcome to the fourth and final course of this data engineering specialization. You've come a long way in completing the previous three courses, and now it's time to wrap it up with a focus on data modeling, transformation, and serving. I'm here again with Joe Reis, your instructor for this course. Can you say a bit about what's mixed in this final course? Sure thing, thanks Andrew. Well, as you mentioned, this final course is all about data modeling, transformation, and serving data for end-use cases. With your work in the previous three courses, you've built a solid foundation when it comes to things like ingestion, storage, data ops, and orchestration. And with these skills, you'll be well-equipped to build robust data systems. This course is all about delivering value for stakeholders. Remember I said that your job as a data engineer is to get raw data from somewhere, turn it into something useful, and then make it available to end-users? Well, in some sense, you have experience with all those things already, but the focus of this course is really on turning data into something useful and serving it in a way that creates business value. So data is usually meant to reflect the real world, but it also just captures a small slice of this real world. For example, if you operate a website and have clickstream data and data on what users are purchasing, and if additionally you have a mobile app where users are taking actions and all those actions are coming in, and additionally, all of these user actions call a lot of APIs, like maybe a recommended system, a classifier to tag content, and on and on and on. How do you model all of this in a database to capture all the stuff happening in the world? And then, of course, if one day someone in your company decides to run a survey of users so you get information about the demographics and personal interests of users, how do you then add that data in? And then if your company acquires an adjacent business which has its own database of users with different attributes, and some of those users overlap with your database of users, what do you do with these two databases now and make it work together? Exactly. Given the complexity of the real world, your ability to design the right way to represent the data will have a huge impact on what types of questions you can efficiently ask of the data, as well as how accurately your data reflects the real world. Yeah, so one use case that comes up a lot is serving machine learning models to end users. There's a lot of complexities, not just in terms of training the model, but in terms of making that model available to end users so they can get maybe predictions or classifications, or in the case of large language models, outputs from a model. But there's also some complexities involved with this. Maybe you have some experience with deploying machine learning models at scale? Yeah, in fact, in addition to the software architecture, data storage, and so on, challenges of deploying, there's one additional wonderful and annoying thing to deal with, which is data drift and concept drift. So when we build AI models, we train on a data set, and then sometimes the future turns out to be different than the past. And so the data we are training the AI system on is no longer reflective of whatever the world has turned into. And so how do you set up the system to spot when the world has changed, and how do you gather the new data, if needed, to maybe update the model at an appropriate time scale? So beyond the software architecture, software engineering aspects, real-world deployment also has this additional statistical and machine learning complexity to deal with. Absolutely. And so this is where the course, I think, gets very exciting, is because now we're talking about modeling your data, transforming it in such a way that's useful, and then serving it to end-users in a way that they're going to get value. But like you say, there's a lot of complexities involved. It's not just, you're not just one and done. There's a constant cycle of serving. And this kind of goes back to the data engineering life cycle in general. We're having to go back and sometimes revisit other parts of the life cycle, as we indicated earlier in the courses. And so, yeah, this is, in a lot of ways, we've saved the best for last, or maybe at least the most fun stuff for last. So yeah, it should be a lot of fun. I think complexity is fun sometimes. Let's go on to the next video and get started with this final course.