There is a
lot of hype around machine learning with developers today, and rightfully so.
They say machine learning really is the new artificial intelligence (AI). So
how does this apply to Docker containers? We’ve talked extensively about
machine learning in past articles, and you are probably feeling fairly
confident on your understanding of it at this point. However, to best explain
the use of machine learning combined with Docker, we must first learn the
fundamentals of Docker containers.

 

We know that
Docker containers essentially package software into uniform components for
development, implementation and consumption. Simply explained, containers provide
new ways to construct and implement portable cloud applications. Moreover, it
is now an innovative way to deploy applications that utilize machine learning.
Elaborating slightly more, Aqua Security
states, “Docker is a technology that
allows you to incorporate and store your code and its dependencies into a neat
little package – an image. This image can then be used to spawn an instance of
your application – a container”.

We Will Write a Custom Essay Specifically
For You For Only $13.90/page!


order now

 

 

What is the value proposition for adopting Docker containers?

By now
you’ve heard of Docker containers, or you may be already deploying them in your
environment.  Again, if you are new to
the technology, a minor but cautionary tidbit – using a Docker container does
require a bit of upfront learning. 
However, using them in your deployment is certainly worth the benefits.
Let’s look at just a few good reasons why deploying a Docker container is the
way to go.

 

Intuitive GUI for environment
configuration:  Occasionally your code goes south in
production, however, it is very simple to revert to a previous Docker image.
This guarantees you can rapidly get back to a working state within your
production environment.

 

Environment configurations between teams:
Don’t let software configuration get your down. With Docker’s model of
configuring just once and run anywhere, your coworkers and customers won’t have
to deal with environment setup and put more emphasis on deploying machine
learning models.

 

Dependable implementations: Having less
downtime and errors in production can be commonplace now that both your dev and
production environments are identical.

 

At the end
of the day, you want a faster and more robust deployment for your applications
to multiple environments that can be used by both internal and external
applications. Containers are a recommended option to package your application’s
code and configurations for versioning,
efficiency, reliability, and throughput.

 

 

Unleashing the true power

While this
technology is relatively new, it by far is one of the fastest growing and
developing platforms in the technology space. What’s amazing is the simple
nature of how you can easily make powerful deployments in no time.  Additionally, infusing machine learning into a
Docker container is what really makes this platform so popular, yet
powerful.  In just one example of this, a
customer
can utilize a particular app with a Docker container to search through
millions of profile photos in social media accounts using facial recognition.
They can customize the criteria from picking just the best picture out of
several, ensuring the image is a human face, or eliminating any group photos.
By the nature of this deployment using a Docker container, it streamlines the
work and makes it scalable allowing the business to focus on other initiatives
or objectives.

 

While this
article just gives you a taste of the subject matter, it is important to
highlight a few items so you walk away with a better understanding. When
joining the robust capabilities of containers and the innovative technology of
machine learning, you can make an application much more powerful and communal.
Simply stated, creating machine learning programs that are independent that can
be used on various platforms without the required testing. They can function in
a substantially distributed environment due to being self-contained. These
containers can be in proximity to data that these applications may be analyzing
as well.  Additionally, having the
availability to share the machine learning services that reside inside of these
containers to other external applications without having to move any code is
certainly one of the stronger benefits.