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.
Goals
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.
