It’s Alive: Guiding Your Deep Learning Project to Production

In the field of machine learning, getting from point A to point B is never a totally straightforward journey. While designing a deep learning algorithm, there are a plethora of factors that will change over time and affect the way your potential product operates. The path to production will very likely involve all kinds of detours, backtracking, and changes on the fly. Luckily, there are ways to keep track of these changes and ensure that your project is ready for the changing variables that come along; the field is called machine learning for a reason, after all. Here are a few ways to teach your machines to adjust algorithms as they go.

Tinker and Test Until You Find the Best Version

With all the datasets you will be feeding to your machine learning model, there is going to be a veritable cornucopia of information coming your way. You must keep tabs on all the resulting changes and the performance trade-offs, as new and different information is given to your deep learning model. This will allow you to test the model over and over again until you find the best possible versions of it and figure out which variable changes affect which aspects of your model’s performance. This means you’ll be doing a lot of cataloging and recording, but the record-keeping will be worth it once you go to production.

Be Ready for Any Number and Kinds of Failures

All that testing and recording ensures you will have a decent number of working models saved in case of failures – and with all the new data coming into your model, there will be plenty of failures. These failures will be for a variety of reasons, and you must catalog them as you revert to older versions of your model and see how they respond to the same data. Sniffing out these failures and preparing to overcome them is a vital part of bringing your model to production, and you will want to have the strongest possible backup model ready in case your production model encounters a failure. Preparedness is crucial.

Multiple File Types Increase Machine Learning’s Complexity

Typically, machine learning models are written in several different code languages, and this makes for a more complicated challenge of record-keeping. Couple this multiple-language challenge with the many and varied datasets you use to test your models, and keeping track of everything accurately becomes a Herculean task. Saving each and every model iteration is important, and logging which datasets responded to which changes – and how – will be critical to success. Once again, preparedness is the key to ensuring a smoother route to production.

Once Production Begins, Things Still Move

Production does not stop your work. As your model heads into production, you may have to make changes to overcome failures or unforeseen alterations to real-world information. Going all out with changes will overwhelm the systems and your ability to track the changes. This means a gradual rollout of changes is the best strategy to ensure proper tracking of new machine learning and product quality. Take your time and keep your models as smooth as possible, and production will be a success.

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