ML academics vs ML production

The Machine learning used in academics/research is quite different from the ML used in Production applications for real usage by end users. Here is a description on what are the differences.

Requirements

In academics, the need is to build the next SOTA (State Of The Art) model.
A 0.1% gain above exiting SOTA is considered exceptional.
In Production, there is no fixed requirement across all the stakeholders, the Sales team, Product team, Engineering manager, etc. have different requirements.

ML Lifecycle priority

In academics, GPU/TPU machines with high throughput which can train faster are required.
In production low latency fast Inference/prediction is required. The users need to be shown the recommendations, the ads fast. A slight delay can reduce the clickthrough rate and thereby revenue drastically.

Data

In academics, mostly there is a benchmark static dataset on top of which models are built.
In production, data is constantly getting generated by the users and may have bias.
Working with shifting datasets make it a challenge.

Bias and Fairness

In academics, in front of achieving the SOTA model goal, fairness takes a low priority.
In production, the fairness of the ML model can not be ignored.

Interpretability

In academics why the model predicts the result is often not a priority.
In production, explainability is of greater priority on why the model makes this decision and the model should be more than a black box.

We discussed how ML in research is different from ML in production across the following categories: