A curated set of machine learning toolkits.

A curated set of machine learning toolkits.


Figure 1: Machine learning life cycle. Image source gosmar.eu

The machine learning lifecycle consists of a number of steps (see Figure 1). The data preprocessing, training, and validation of the machine learning model are part of the machine learning life cycle. When you are done validating and testing your model, you will want to deploy it in production to serve your customer. Deployment is one of the key components when it comes to productionising the ML models. This is because other important aspects, such as scalability, have to be taken into account. Once you have deployed your model, then you need to monitor it. This is to observe the model degradation that usually occurs due to data drift or concept drift and to continuously optimise your model.

There are tools out there available for deployment and monitoring of the ML models. Some of the curated lists of tools are listed below.

Deployment & Monitoring tools