The three critical steps involved in deployment of machine learning algorithm and exposing it to real world are :

Define a goal based on a metric : Decide if you want human level intelligence or an acceptable one as this decision will affect time and engineering cost of your system. Also define a metric to measure performance of your model.

Build the system : Build a minimum viable system without worrying much about accuracy. Then build an incremental strategy to improve your system by solving problems you face in each iteration.

Refine the system with more data : Initial metric values are not the indicators of real life, your data and users might change , so regularly monitor the system performance. Update it with new data and fine tune the model accordingly.

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