This course focuses on the skills necessary to implement a knowledge-mining solution with a focus on the Cognitive Search solution. The course will walk through how to create a Cognitive Search solution and how to set up the process for importing data. Once the data sources have been set up properly, the course will teach you how to create a search index and then how to configure it to provide the best results possible.
- Create a Cognitive Search solution
- Import from data sources
- Create, configure, and test indexes
- Configure AutoComplete and AutoSuggest
- Improve results based on relevance
- Implement synonyms
- Developers who want to include full-text search in their applications
- Data engineers focused on providing better accessibility to organizational data
- AI engineers that provide AI combined with search functionality in their solutions
To get the most out of this course, you should:
- Have a strong understanding of data sources and how data will be needed by users consuming a Cognitive Search solution
- Be able to use REST-based APIs and SDKs to build knowledge-mining solutions on Azure
Hi there, welcome to the Implementing a Cognitive Search Solution course. My name is Brian Harrison. And I'm a Principal Cloud Architect and have been involved in the cloud in many ways pretty much since Amazon started it 10 to 15 years ago. Here are some of the ways that you can get in touch with me if you should have an interest after having learned more within the scope of this course.
Now, who exactly should attend this course? This course is suitable for developers who will be including full-text search in their applications, data engineers who are focused on providing better accessibility to organizational data that exist in many different kinds of data sources, as well as AI engineers that will be providing AI combined with search functionality within their overall solutions.
The course objectives are very simple. The course will focus on the skills necessary to implement a knowledge mining solution with a focus on the cognitive search solution within Azure. The course will walk through how to create a cognitive search service and how to set up the process for the importing of data. And then lastly, the course will teach you how to properly set up a search index and then how to configure and provide the best results possible from that index.
Some of the prerequisites. Candidates for the course should have a strong understanding of data sources and how data will be needed by users consuming a cognitive search solution. Candidates should also be able to use REST-based APIs and SDKs to build knowledge mining solutions on Azure.
If you should happen to have any feedback at any point while watching this course or after, please feel free to email firstname.lastname@example.org.
Now, what exactly is a cognitive search solution? The Azure Cognitive Search, formerly known as Azure Search, is a cloud search service that gives developers APIs and tools for building a rich service experience over private, heterogenous content in web, mobile, and enterprise applications. Now, that's a mouthful. What exactly does it mean? It means that you are taking and giving access to your data within the scope of your web, mobile, and enterprise applications through a search capability.
Now, many of us have used Google Search. That is a starting point of understanding for what a cognitive search solution might look like. The difference is instead of public data, we're talking about your organizational data. Search is foundational to any app that surfaces content to users, with common scenarios including catalog or document search, e-commerce site search, or knowledge mining for data science. The APIs and architecture of cognitive search simplify the task of adding sophisticated information retrieval to any solution. This means that you're gonna start with your data. You're going to determine how you want that data to be made available to your users, what type of an application. And then based on the types of configurations, the types of AI capabilities that can be added to Azure Cognitive Search, you're gonna provide a search solution.
Now, just as a quick example of what this would look like, on the far left, you have the content that you're gonna be bringing, whether that be relational or non-relational. You're gonna be creating a index of that data through a indexing engine, creating indexes and other structures. And then having that sit behind a REST-based API query engine that then is going to be requested against and provide responses back to your application, whether it's mobile, whether it's web, whatever it might happen to be.
So hopefully, this gives you a good introduction. Let's go ahead and jump into the main meat of the content and start to talk about how to create your Azure Cognitive Search.
Brian has been working in the Cloud space for more than a decade as both a Cloud Architect and Cloud Engineer. He has experience building Application Development, Infrastructure, and AI-based architectures using many different OSS and Non-OSS based technologies. In addition to his work at Cloud Academy, he is always trying to educate customers about how to get started in the cloud with his many blogs and videos. He is currently working as a Lead Azure Engineer in the Public Sector space.