AI News, Transitioning from Academic Machine Learning to AI in Industry

Transitioning from Academic Machine Learning to AI in Industry

When machine learning and deep learning is employed to solve business problems, you must design systems that consider the overall business operations.

Questions to ask for each project: Jupyter notebooks, while wildly popular for rapidly prototyping deep learning models, are not meant to be deployed in production.

For this reason, academics should push themselves to build structured ML modules that both use best practices and demonstrate you can build solutions that others can use.

Action Items: Academics often run code to find and eliminate errors in an ad hoc manner, but building AI products requires a shift towards using a testing framework to systematically check if systems are functioning correctly.

Action Items: No matter what company you join, you will have to access their often large data stores to provide the training and testing data you need for your experiments and model building.

To demonstrate industry know-how, academics should show that they can (1) query from large datasets and (2) construct more efficient datasets for deep learning training.

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