A deep dive into ML as a DevOps data expert
ML is a subset of AI that involves building systems that can automatically learn and improve from data without being explicitly programmed. ML algorithms are designed to identify patterns and relationships in data, using these patterns to make predictions or take actions.
From a DevOps point of view, ML can be viewed as a software application that can learn and improve over time. This requires a different approach to software development and deployment than traditional applications. In this section, we will discuss how ML works and how it differs from traditional software applications.
How ML works
ML involves several key steps:
- Data collection: The first step in ML is to collect data that can be used to train a model. This data can come from a variety of sources, including sensors, social media, or user interactions.
- Data preprocessing: Once the data is collected, it needs to be preprocessed to ensure that it is in a...
