Real World AI: A Practical Guide for Responsible Machine Learning by Alyssa Simpson Rochwerger & Wilson Pang

Author:Alyssa Simpson Rochwerger & Wilson Pang
Language: eng
Format: epub
Publisher: Lioncrest Publishing
Published: 2021-02-13T00:27:58+00:00

Pulling the Pieces Together

All of these concerns—availability, coverage, provenance, quality, security—will need to be considered as you develop your data pipeline. Every step of the pipeline will need to be consistent, repeatable, and accurate. A well-thought-out, well-documented, repeatable pipeline will go a long way toward the success of the model in production long-term.

Those new to AI commonly believe that the hard part is building the model. In practice, however, data preparation and the building of the pipeline is very often a much bigger investment of time, resources, energy, and skillsets. Without a repeatable and scalable pipeline, the best-designed model won’t be able to be used in production long enough to be useful. Data isn’t a problem you can solve once and be done with. You’ll have to get your data pipeline up and running and keep feeding it data for as long as your model is in production.

Once you’re satisfied that you’ll be able to sort out getting the right data to train your machine learning system, you’ll need to start building the teams that will actually do it.

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