The Puzzle Pitfalls of Building ML Pipelines

The Puzzle Pitfalls of Building ML Pipelines

In an ideal world, machine learning pipelines would build themselves. As it sits though, this tedious process currently falls on the shoulders of data scientists and engineers. As noted in “The Gotchas of ML/NLP”, the key to successfully building machine learning pipelines that produce accurate downstream models resides in finding the optimal combination of technology pieces and fitting them together into a smooth end result. 

Limitations to Manually Building ML Pipelines

Building machine learning pipelines is like putting together a puzzle. Only the puzzle is really 5 different 1,000-piece puzzles that have been mixed together. And the pieces have been scattered throughout your neighborhood and you have to go on a scavenger hunt to find all the pieces. And you ask your neighbor Gary if he’s seen any pieces that might work with your puzzle because Gary seems like a puzzle type of person, you know, so he rummages around his garage and finds a puzzle piece that he says will work great, even though it looks to be from the 1970s and there’s no way it’s one of the pieces you’re looking for but you take it to be nice because Gary invites you over for grill and poker nights sometimes and you don’t want to be rude. You throw the puzzle piece away when you get home. 

You’ve spent weeks – maybe even months – finding all the pieces and you’re ready to put them together. You separate them out carefully and begin piecing. Sometimes you get impatient and smash pieces together that don’t really fit together and hope they stick. And sometimes you get to the end of one of the puzzles and are missing a piece, so you craft your own, lower-quality piece out of cardboard and try to draw a cohesive picture on the piece but you’re not really that good at drawing, so your duck looks like a cat (or was it a cow?) and you hope no one will notice how ugly the finished product looks. Lucky for you, you found a new puzzle piece that would better complete the puzzle than your makeshift cardboard piece, but when you pull the old cardboard piece out the whole puzzle falls apart. Since, you know, that’s how physics works.

Manually building machine learning pipelines often looks the same as that ugly puzzle that you just put together – elements are pulled from different sources, shoved together, sometimes with some glue coding thrown in, and in the end it sort of works but is bound to fall apart as soon as you go to add a new element that was supposed to make it work better. On top of that, you’ve spent the better part of a month creating this monstrosity – time that you could have spent doing something with the results had you not had to waste so much time building, training, and tuning the model. 

You could have someone build a black-box pipeline for you, of course, but then you: 1) don’t know what all is actually happening underneath the hood and 2) don’t get the satisfaction of seeing your puzzle – er, pipeline – come to life.  

Remove the Barrier to ML

Solutions like Jaxon become invaluable resources for building accurate ML models in a short amount of time. They carefully construct the puzzle for you in a matter of days with the optimal combination of elements, all while leveraging your input as you see fit. 

For example, at each stage of the process Jaxon is automating steps that are traditionally performed by humans, dramatically improving upon the time usually spent finding the pieces and putting them together. By using Jaxon, humans are able to impart the best of their knowledge and intuition around a dataset or problem while allowing the platform to perform all the repetitive grunt work that is often a year (or more!)-long barrier to implementing a machine learning project. 

On top of that, Jaxon integrates the latest state-of-the-art techniques as they become available and discerns if they are the right fit for your use case (just because it’s a new piece doesn’t mean it’s the right fit). That way, instead of having to hunt down the latest tech and piecemeal it into the already existing pipeline and hoping it works, you can sit back and know with confidence that you’re already leveraging the latest, greatest, and – most importantly – right solution(s) for your needs.

– Carly Stithem, Director of Marketing