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. Let's recap what we've learned here in our Cognitive Search Solution course. We first started off by understanding what the actual search service was, how to create it within the Azure portal and what some of the configuration options were. Once we were able to do that, we then started to look at how to import data into our search service so that we could actually make it searchable and make it part of the larger picture which is the index. And this is where we really dove deep into how to create, how to configure and how to test your search index to validate that the data was in fact ingesting correctly.
Now, this requires both an understanding of the index as well as an indexer. And just as a quick review here, this should be a page inside of the Azure portal and specifically within your Azure index that you should now have a good understanding of, understand what all of the attributes are with respect to the fields, and then understand how the data can get mapped into those fields so that you can actually perform your queries appropriately.
Once we had an index set up, it was then our goal to figure out how to provide the best query results possible. And in some instances that required us talking about how to provide autocomplete functionality and autosuggest functionality based on specific fields in your index. But what happens when the query results are maybe not providing the best results that you think they should be? That's where we looked at some of the different options for improving relevance to your search queries. And then lastly, how to provide synonym maps that would be made available within the search service as a whole so that you could actually search for the word dog and get documents back that also contained the word canine, puppy, and potentially others, depending upon your synonym map.
The most important pieces though that I talked about in this course were two things. One, understand your data. It's the only way that you're going to be able to create a valid index that is gonna provide search results that are relevant to your application usage. Secondly, is that the search service cannot be completely configured, created, modified, and updated using the Azure portal. You will have to learn the Azure APIs. And for that, I have provided you with a set of examples that you can find here on my GitHub repo.
I hope you found some value in the course. And hopefully, I'll see you again soon.
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.