machine learning tools

Machine learning model monitoring refers to tracking and understanding your production models’ performance from a science and operation point of view. In other words, it’s the tracking of a Machine learning model during production so that you can fix any potential issues that may negatively impact your business. This article is going to look at the lifecycle and benefits of monitoring machine learning models in production.

Phases of the machine learning monitoring model

  • Model Building

This initial stage entails identifying the problem, preparing data, feature engineering, and early programming.

  • Model Evaluation and trial

This phase involves hyperparameter tuning, feature selection, comparison of the various algorithms, and their effectiveness at solving the production problem.

  • Productionize Model

Productionize model involves developing a deployable code. The production model comprises grade codes that are of a different framework or programming language.

  • Testing:

A test is conducted to ensure the code is functional with similar results as in the Model evaluation and trial step.

  • Deployment:

The deployment phase involves launching your monitoring machine learning model to get accurate forecasts, then getting the model into production, where it can start adding value by serving predictions. The artifacts used are APIs which accessing the model.

  • Monitoring and Observability:

Monitoring and observability is the last machine learning phase that ensures everything runs smoothly and according to plan.

Circumstances where monitoring is recommended

Monitoring should be done after a model replacement, tweak, or when you make a new deployment. Whatever the case, monitoring plays a pivotal role as it typically determines whether a model change is 100% effective.

Monitoring, in a nutshell, is all about risk management. This particular method is ideal for increasing the level of trust and reassurance in the functionality of a system after a change or upgrade.

After the system features have been tested and work accordingly, the monitoring system acts as a storage, visual, and automated response initiator.

Categories of machine learning monitoring model

There are different classifications of monitoring which include metrics, logs, and distribution traces. These particular models have the sole purpose of reducing volume while simultaneously increasing productivity.

Benefits of monitoring machine learning models in production

  • Monitoring is essential in that it unveils errors in the machine learning model’s initial phase, which saves you time and money.
  • It prevents inaccuracies that may arise when the model training isn’t a representation of actual data. In other words, the production environment and the live system don’t correspond.
  • Monitoring machine learning models helps keep your system in check by eliminating models with errors or stale ones that no longer add value to your business.
  • In the end, an ideal monitoring machine learning model in production helps prevent bankruptcy and saves your time, mainly if you’re operating in a controlled environment.

Conclusion

Hopefully, our article was informative when talking about monitoring machine learning models in production, and it’s importance. Indeed, the profitability of a business lies with a proper

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