Summary
In this chapter, we presented the foundations of ML techniques and explained how they can allow us to scale the observability data analysis to assist and provide educated recommendations. As we noted, this is just a drop in the ocean in the complex and evolving landscape of ML, but we hope it will help you get started and motivate you to dive deeper into the topics you find more interesting.
The use of ML to support network operations – and in concrete, network observability – is just the start of a big revolution that we will see materializing in the next few years, and it will transform how we interact with networks. We’ll see many new solutions ready to use coming from the industry, but we wanted to show you two simple examples to illustrate the basics of how these solutions will work.
The usage of forecasted data to validate reality versus expectations that we used to validate operational challenges has many other applications. You can use a similar...
