How do you productionize your ML model?

Anton Knight
Anton KnightAnswered

The general Machine Learning model deployment procedure may be broken down into four steps:

1. Create and develop a model in a training environment.

How can you productionize machine learning models? To launch an ML application, you must first create a model.

ML teams typically develop many ML models for a specific project, with only a handful making it to the deployment phase. These models are often constructed in an offline training environment (supervised or unsupervised) and are provided with training examples as part of the design.

2. Optimize and test the code.

After constructing a model, ensure that the code is of sufficient quality to be deployed. If it isn’t, it must be cleaned and optimized before re-testing. Repeat if necessary.

This not only assures that the ML model will work in a real setting, but also allows others in the business to understand how the model was constructed.

3. Deployment of containers

Containerization is an important element for productionize of ML models. Machine Learning developers should containerize their models before deploying them. Containers are the ideal setting for deployment since they are easy to organize.

4. Regular monitoring and upkeep.

Continuous monitoring, upkeep, and governance are critical for effective ML model deployment. Simply proving that the model works in a real scenario is insufficient; continual monitoring helps to assure that the model will be helpful in the long term.

Beyond the building of ML models, it is critical for ML teams to design mechanisms for efficient monitoring and optimization in order to keep models in the best possible shape. Issues such as inefficiencies and bias may be discovered and corrected after continuous monitoring systems have been developed and executed. Based on the ML model, it could also be able to retrain it with fresh data on a regular basis to keep the model from drifting too far from the current data.

Testing. CI/CD. Monitoring.

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