The journey of a machine learning engineer doesn’t stop when a model is deployed in production. It’s like having a garden-you can’t just plant the seeds and walk away, you have to keep an eye on your plants to ensure they grow well in all sorts of weather. Just like traditional gardening tools won’t help much in a hydroponic setup, monitoring machine learning systems isn’t the same as watching over regular software.
So how do you tend to these machine learning models once they’re up and running? What are the “growth signs” you need to look out for? Which tools can make your job easier? In this blog post, you will find comprehensive information on model monitoring, including its stages, focus, helpful tips and tools.
Why are ML model monitoring and management in production necessary?
Machine learning model monitoring and management in production are essential aspects of modern data-driven businesses. When we train ML models on specific data, the goal is to use these models to make accurate predictions when faced with new, unseen data. In the production stage, these models encounter real-world data which can be much more diverse and challenging than the initial training set.
Without adequate model monitoring, we run the risk of our models becoming outdated or misaligned with current data trends, resulting in bad performance. Additionally, model performance can degrade over time due to what is known as “ concept drift,” where the statistical properties of the target variable transform over time. Detecting such shifts early on is crucial to maintaining the effectiveness of ML models.
As for model management, it ensures that our models are up-to-date and performing optimally. It allows us to maintain version control, compare different models, validate model performance, and comply with regulations and ethical guidelines.
One of the major challenges in model management is maintaining a robust infrastructure that allows for continuous integration, continuous delivery, and continuous training of models. As models are updated or retrained, it’s vital to have a system that manages these versions and deploys them without disrupting the existing services.
