Developing your model is an essential part of working on ML projects. And it’s usually a tough challenge.

Every data scientist has to face it, along with difficulties, like losing track of experiments. These difficulties are likely to be both annoying and unobvious, which will make you feel confused from time to time.

That’s why it’s good to streamline the process of managing your ML model, and luckily there are several tools for that. These tools can help with things like:

• Experiment tracking
• Model versioning
• Measuring inference time
• Team collaboration
• Resource monitoring

So it’s common sense and good practice to find and use tools suitable for your projects.

In this article, we’ll explore the landscape of model management tools. I’ll try to show you the variety of tools and highlight what’s good about them.

We’ll cover:

• Criteria for choosing a model management tool
• Model management tools: Neptune, Amazon SageMaker, Azure Machine Learning, Domino Data Science Platform, Google Cloud AI Platform, Metaflow, MLflow

#machine learning model management #machine learning tools