MLOps Pr Machine learning operations

Machine learning is an advanced and fast-moving space, however for a field that is so much about technological advancement.

it is often operated in a scarily primitive fashion        

that do I mean by that well as of last year 89% of machine learning and ai models didn't make it into production and this is mostly because companies that venture into machine learning and AI don't have in place a set of best practices that can help them make their machine learning transformation successful.

if we look at machine learning development now it is reminiscent of how software development was performed before the introduction of development operations, because after DevOps got introduced software development was allowed to transform itself improve itself to allow for producing large scale software systems machine learning development at this moment is about to be transformed by a similar set of best practices called MLOps.

MLOps is a set of best practices to improve collaboration and communication between machine learning professionals and operations professionals. it aims to shorten and manage the complete development lifecycle and provide continuous delivery of high-quality predictive services.

how is machine learning operations exactly different from DevOps?

although machine learning operations stand on the foundations that DevOps already built. it differs from DevOps in the same areas where machine learning development differs from traditional software development.

In machine learning development data becomes part of your system through training the data actually in a sense defines your deliverable. This is why in machine learning operations continuous integration is not only about testing your code it is validating your data and its quality. Secondly, Machine learning has a highly experimental nature. Dimensions within machine learning development are the tuning of your hyperparameters, selecting features, and almost every week, new algorithm types come out. Often it is hard to track how did we exactly get to our deliverable because this continuous delivery within machine learning operations is no longer about putting into production a single software package.

it is about bringing forward a complete pipeline one validated orchestrated experiment that then, in turn, can put a prediction service into production thirdly, and lastly, machine learning is subjective to ever-evolving surroundings data changes constantly.

Think about for example if you would make a shirt classification model fashion changes pretty quickly if your model was trained in a certain moment in time, but its surroundings are changing the performance of your model will eventually delay. This is why in machine learning operations a new term is introduced called continuous training. which means that models are automatically retrained and monitored.

# MLOps, Machine learning.