Machine learning and ML model monitoring is helping many organizations and businesses to function properly with their numerous applications and also use cases. However, machine learning projects also come with a high rate of failure.
The reason is developing accurate ML models includes many experiments or trials and errors. Tracking those experiments and their issues in a structured way is essential for the success of any ML project.
Thus, you need to know the best ML model monitoring tools that are required for successful experiment tracking.
What is experiment tracking?
Experiment tracking is the method of keeping track of the metadata regarding different experiments during the development process of a machine learning model. Metadata and experiment usually mean:
- Different versions or types of training data
- Different types of ML models.
- The codes that are required for model development.
- Model hyper-parameters like the right size of a network that is neutral.
What is the implementation procedure of ML experiment tracking?
If you do not go through many experiments, you can record all the related information of different experiments on spreadsheets manually for your future reference.
However, in the case of machine learning projects, you need to track numerous variables, and those variables also have complex relations to them. Hence, manually tracking those experiments can be really challenging and time-consuming as well.
Fortunately, there are some ML model monitoring tools that can help you in tracking the experiments regarding machine learning. Those tools help in several ways like:
- Those tools can be integrated with several frameworks of model training.
- Those can offer a place to store various machine learning projects and also their experiments.
- Those tools come with a user-friendly UI that helps in searching and comparing experiments.
- Those ML Model Monitoring Tools can register all the crucial information regarding the experiments automatically.
- Those tools allow you to track the consumption of hardware for various experiments.
The best practices and tools for ML experiment tracking
To get the most out of ML tracking, you are required to define:
- Evaluation of the metrics like explainability, accuracy, etc.
- The actual objective of the experiment.
- The variables of experiments such as hyper-parameters, different models, databases, and many more.
For instance, if you are thinking about increasing the accuracy of the model, you need to determine the metrics of the accuracy and hypothesize, “If you use the ‘X’ model, it will offer more accuracy than another model ‘Y.
Trying out several things and choosing the best one can be counterproductive if one cannot decide what can make an experiment successful.
The best ML model monitoring tools for experiment tracking
There are various tools for successful experiment tracking. Such as:
- Qualdo
With the help of this particular tool, you can easily and continuously monitor the performance metrics of your ML model in Google, Azure, and also AWS. With Qualdo, you can also enjoy the magic of notifications and alerts while tracking and destroying all the issues of your ML models.
- Neptune
Neptune is another popular ML model monitoring tool that helps in the model registry, experiment tracking, and also model monitoring to make all your collaboration tasks really easy.
- Comet
This is a well-known ML platform that helps its users to track, explain, compare, and also optimize several models and experiments across the entire life cycle of the model. You can also change codes, register databases, and compare the history of different models with Comet.
Thus, these best practices and ML model monitoring tools can help you in successful ML experiment tracking.
