From the course: AI Data Pipelines with Spring
Introducing AI data pipelines
From the course: AI Data Pipelines with Spring
Introducing AI data pipelines
- [Instructor] To help introduce AI data pipelines, let's go through a scenario so that you can understand what it is, but also why it is important. Now, imagine you are a customer, you're having some challenges with the system. You log into the system to submit your feedback to describe the issue, and the system connects you with a customer representative. As soon as you are connected, the awesome customer representative is asking you who you are and what is your challenge. Now, you know you already provided this information, so quietly and politely you are wondering, why don't they know? Maybe this information is not readily available to the representative. They may be using a system, which I'll refer to as the target system, but your customer information and your feedback is in a totally different system, which I'll call the source system. Maybe you're wondering, well, how do I get the information from the source to the target system? A data pipeline is a category of design patterns. Focus on the movement of information. A data pipeline acts as a bridge to share information. Data pipelines consist of multiple steps such as reading information from the source system, processing the data, such as performing any needed transformation, filtering, and validation, before writing that data to make it available to the target system. You can infuse artificial intelligence into the data pipeline to allow the representative to quickly determine what's going on. Now, I just threw out that term artificial intelligence, and you may be wondering, what exactly is artificial intelligence or AI? Artificial intelligence is technology. It emulates human intelligence. It appears to hear, see, understand, and learn like a human brain. It's not only able to save the customer information into the target system, but it's also able to do things like summarize the feedback. Suppose this feedback is really long where it seems like an unhappy customer submitted pages and pages of intimate details. Artificial intelligence can create a quick summary so the representative can quickly determine what's the real issue. Artificial intelligence can also determine whether it's a good or not so good feedback, which is basically the sentiment of the feedback. Artificial intelligence can also look at two sets of customer details to quickly determine if this is the same customer. Doing this in an automated fashion will be much faster than a manual review. Now, I know this sounds like a lot, but don't worry. We'll walk through these examples step by step. In the next section, I'll discuss building data pipelines using Spring for Java applications.
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