What is model deployment and why should I learn about it?

Data Science, AI and ML
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Once you have made the complete data science project, it is time for the intended user/ stakeholder to reap the benefits of the predictive power of your machine learning model. In simple words, this is model deployment. This is one of the most important steps from a business point of view but also the least taught one.

Let us take an example here. An insurance company has initiated a data science project which uses Vehicle images from accidents to assess the extent of the damage. The data science team works day and night to develop a model that has a near-perfect F1 score. After months of hard work, they have the model ready and the stakeholders love its performance but what after that?

Remember that the end-user, in this case, are the insurance agents and this model needs to be used by multiple people at the same time who are NOT data scientists. Therefore they’ll not be running a Jupyter or Colab notebook on GPUs. This is where you need a complete process of model deployment.

This task is usually done by machine learning engineers but it varies according to the organization you are working in. Even if it is not the job requirement of your company, it is very important to know the basics of model deployment and why it is necessary.