Taking AI or machine learning into production needs a good amount of effort, patience, and resources. Though the AI models can help you in different ways, making it into production can give you a really hard time.
So, it is important to know what are research-based ML Models and production-based models, and also what the differences are between these two ways of machine learning model monitoring.
Research-based ML models
Developing the process of machine learning model monitoring for the research-based models is a difficult task, mainly if you are trying to build them from scratch. Several factors play important roles before you can deploy ML models for your production successfully.
Some of them include training data within a short time, data collection, poor quality of data, training data that are non-representative, overfitting of the training data, model development, offline learning of data, and many more.
Research methods in the ML model play an important role because the reliability and accuracy of the results are influenced by the methods of research that are used.
What are the important aspects of research-based machine learning model monitoring?
1. Data is crucial
Before starting the process, it is better to perform several methods of data clearing on each machine learning project. When these are overlooked by the professionals of machine learning, the model may break or give overly optimistic outcomes in its performance. So, you need to consider the things before building your models are:
- Ensure that you have enough data.
- Take time to properly understand the available data.
- Consult with the domain experts.
2. Building reliable and robust models
To complete this step of research-based ML model monitoring, you should go through the tasks like:
- Try out different models
- Do not allow data leakage.
- Optimizing the hyper-parameters
- Evaluate a model several times.
- Use an appropriate test set.
3. Comparing the models
Comparing the models is another important part of research-based ML model monitoring. The important tasks to compare the models are:
- Making correct model comparisons.
- Remember, a bigger number does not mean a better model.
- Carefully consider the combinations of the models.
Production-based ML models
Now, take a look at the process of deploying production-based ML models. The target of developing an ML model is to find a solution for a problem. The model of machine learning can work in the right way when utilized for production and used by the consumers.
So, the important steps in production-based machine learning model monitoring are:
- Frameworks and tooling
For the perfect operation of your model, you need the right tools, frameworks, hardware, and software that can help in the accurate deployment of your ML models. Also, it is important to consider the combination of tools and frameworks before using them.
- Data storage
Another important aspect of production-based ML models is to properly train, evaluate, test, and predict the sets of data.
The differences between research-based ML models and production-based ML models
- The decisions of a production model frequently have to be intelligible to the people of the non-technical field. This is not needed in the case of research-based ML models.
- The production models need to operate at those scales that the research-based ones cannot duplicate.
Therefore, though research-based ML models and production-based ML models have several differences, both of them are important in the proper operation of your company, which depends on machine learning model monitoring.