ML life cycle
The ML life cycle encompasses the end-to-end process of developing and deploying ML models. It involves several stages, each with its own set of tasks and considerations. Understanding the ML life cycle is crucial for building robust and successful ML solutions. In this section, we will explore the key stages of the ML life cycle:
- Problem definition: The first stage of the ML life cycle is problem definition. It involves clearly defining the problem you want to solve and understanding the goals and objectives of your ML project. This stage requires collaboration between domain experts and data scientists to identify the problem, define success metrics, and establish the scope of the project.
- Data acquisition and understanding: Once the problem has been defined, the next step is to acquire the necessary data for training and evaluation. Data acquisition may involve collecting data from various sources, such as databases, APIs, or external datasets. It is important...
