Let’s start with the assumption that you know how to build a reliable machine learning model that predicts well and is fault tolerant. Great! But to truly create value, your model will need to extend beyond its theoretical definitions, and be accessible to its intended end users. What’s the point of having a great model if it’s sitting in your Jupyter notebook without reaching an actual human being who will gain value from it?
Take the example of a business user. This person wants to understand the forecast of one of their products for the next three months, and which of their clients are going to churn. They just want an interface from which to easily access this information. What they don’t need is to understand the mechanisms behind how this information is being generated by your model. Knowing how to build this interface, in addition to creating a model, is a key skill.
Deployment is essentially similar to building an end-to-end software product. Machine Learning is just one part of it. If you have good software engineering skills, you’ll find the rest of the pieces quite familiar and relatable: you’ll know how the frontend and backend works, as well as how to build pipelines and deploy.
Most data scientists, however, don’t have this software engineering experience. They usually are analysts skilled in Python, but that’s where their skills end, and the gap is clear. To successfully deploy a model, you’ll need to put together various skills of a software engineer, a data engineer and a machine learning scientist. Having this combination of skills and mindsets will set you apart from people who just build models.
To understand why effective deployment is important, we needn’t look further than self-driving cars, which are already a reality. These cars are equipped with a bunch of cameras that take live videos and photos to identify the objects in front of it, classifying them into a car, human, dog, or just a wayward piece of paper. If the car can’t identify the objects accurately, it won’t be able to maneuver the right way. In addition, the car is also predicting the distance between itself and the objects. In ML terms, this is a classification and regression problem, and is actually a model.
To build this model, you don’t need a car. All you need is a bunch of photos and a laptop. But to move the model into production, you’ll need to install it into a car’s hardware so that it can make maneuvering decisions in real time – within nanoseconds – accurately. Production models are live and used by real people. The theoretical model you built is just one piece of the larger puzzle. For it to be actually used, your model needs to work with other data pipelines and infrastructures.
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Head of Strategy and Data | Former Head of Data