When it comes to machine learning, everyone knows that it’s imperative to get the data right, but there’s another item that is just as critical: the machine learning model. Even with the best data, if there are issues with your model, you won’t get the results you expect from your ML projects.
That’s why it’s critical that data scientists and teams understand and track all components of a model through its entire lifecycle.
- The stages of the ML model lifecycle
- Why it’s critical that machine learning teams track their models through the entire lifecycle
- How data scientists can get started understanding and tracking models through every stage of the lifecycle