Contents
What is debugging in machine learning?
You debug your ML model to make the model work. Once your model is working, you optimize the model’s quality for production-readiness. This section covers both the debugging and optimization steps.
What is an example of a machine learning algorithm?
Broadly, there are 3 types of Machine Learning Algorithms Examples of Supervised Learning: Regression, Decision Tree, Random Forest, KNN, Logistic Regression etc.
What is logging in machine learning?
Using machine learning with log analysis tools lets us: Categorize data rapidly: Logs can be seen as textual data, which means that NLP techniques can be applied to gather the same logs in an organized manner, making it possible to search for specific types of logs.
What is testing and debugging?
Testing is the process to find bugs and errors. Debugging is the process to correct the bugs found during testing. It is the process to identify the failure of implemented code. Debugging is done by either programmer or developer. There is no need of design knowledge in the testing process.
How to debug machine learning models to catch issues early?
Capture: The debugger must be able to capture model and optimizer specific information during training. The user must be able to specify granularity (how often) and post processing (applying reductions on tensors) to manage debug data. React: The debugger must be able to monitor and react to changes in the capture data.
When does a machine learning model stop changing?
Millions of parameters or weights in the model are changing in every step during training. It stops changing once training is complete, at which point what bugs you didn’t catch during training are now part of the model. Contrast this with traditional software — you have logic and rules in code that don’t change every time you run it.
Is there a debugger for Amazon SageMaker managed training?
Amazon SageMaker managed training: Debugger is automatically enabled when using Amazon SageMaker to train models, and you don’t need to make any changes to your training scripts.