s wise to budget
time for featur…age pipeline not been in place, it would have slowed down a lot of our modeling work significantly. This is precisely why larger companies are building frameworks to make feature engineering easier (see Uber’s Michelangelo, Netflix’s Delorean, and Airbnb’s Zipline). When planning ML projects, it is always wise to budget time for feature engineering, because training data will not be handed to you on a silver platter.
Building a machine learning product is a multi-faceted and complex task […] machine learning practitioners may need to do during the process: understanding the context, preparing the data, building the model, productionization, and monitoring. […] Certainly, not every machine learning practitioner needs to do all of the above steps, but components of this process will be a part of many machine learning applications.
Fight for your ideas and be persistent: People sometime do not like your ideas or they are just too lazy to follow them. If you are really convinced by your ideas, you should continuously go after them and “fight”. This is sometimes necessary. Architecture decisions with long term goals are often not the easiest …