DevOps for Machine Learning

DevOps for Machine Learning

Machine Learning DevOps

Infrastructure meant for traditional application development is not suitable and optimized for Machine learning operations. The mere experimental nature of the data scientist’s workflow together with the high degree viability of their computational requirements make it a daunting task to put together a DevOps machine language pipeline(MLOPS).

MLOPS is all about flexibility and experimentation ensuring a stable and streamlined ML model. However, most modern data scientists lack the maturity and the proper tools to effectively implement ML practices. This guide will help you address some of the most common hurdles of an MLOP pipeline and solutions to overcome them in the future.


An efficient MLOPs pipeline consists of:

** Proper version tracking and control
** Continuous training
** Services infrastructures that are scalable in the future
** 24x7 alert monitoring system

Deploying a production model isn’t as easy as it seems. Although there are similarities with traditional software development there is an inherent difference in how engineers and scientists think. 

In a nutshell, an effective MLOPs pipeline should follow the below guidelines:

·        Infrastructure scaling

·        Effortless team collaboration

·        Offering reproducibility

A typical machine learning service includes hosting an EC2 and specifying a designated AMI template. AMIs help capture exact details like libraries, operating systems, applications and other necessary information crucial to the development of your machine language.

However, the production is often bottlenecked due to common errors such as:

·        Recovering previous work of scientists that have left

·        Comparing results various models where you should be concentrating on your own development

·        Reproducing more results when you haven’t finished analyzing what you have in store

·        Tracing the original data

·        Work duplication across teams

Machine learning is a nightmare if not executed properly and here are a few ways you can optimize your own pipeline:

1.     Looking for trends

Data analysis machine learning is maddening if you don’t know what you’re looking for.

The best way to look at data is to look for trends rather than results. At a glance, the data you’re searching for will produce a pattern in the ocean of information. Scanning the ocean with sonar is not possible, instead, you can focus on a particular region. You don’t search for a whale, you look for its breeding ground where it's known to frequent.

2.     Corrections across data sets

Data trends are a result of interactions across multiple measures. They are hard to pin down but with the proper learning tools you can pry out the correlations, leading to faster and more efficient learning procedures.

3.     A fresh perspective

Data scientists have a fixed set of variables from which they extrapolate information such as bug fix metrics, delivery velocity, system integration and so on. However, there are a few unorthodox places to look for as well and they greatly vary across various interfaces.

Instead of fixed you can look for bugs found. The possibilities are limitless and all you need to do is to find you the best fishing spot for your preferred fish.

Data generated by continuous integration or DevOps is a streamlined process and applying that to machine learning will take time. But work is being done and developments are being made to optimize the process. It’s only a matter of time before this too becomes a naturally streamlined and fully optimized process.



My Name is Ankur Jain and I am currently working as Automation Test Architect.I am ISTQB Certified Test Manager,Certified UI Path RPA Developer as well as Certified Scrum Master with total 12 years of working experience with lot of big banking clients around the globe.I love to Design Automation Testing Frameworks with Selenium,Appium,Protractor,Cucumber,Rest-Assured, Katalon Studio and currently exploring lot in Dev-OPS as well. I am currently staying in Mumbai, Maharashtra. Please Connect with me through Contact Us page of this website.

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