Hi, I'm Beenish Zia, and this is AI Practitioners Guide
for Beginners, the video series.
In this first episode, I give you
an overview of what the guide covers, basics of AI,
and business considerations of applying AI.
This is a five-part video series.
And each episode gives you a sneak peek
into what's covered in detail for that section in the guide.
You can read the written AI Practitioners
Guide provided in the links.
If you are someone who knows the basics of AI
or has taken courses online for machine learning,
but have always wondered where and how to get started,
the AI Practitioners Guide is a great place to begin.
The AI Practitioners Guide for Beginners
will provide you with a high-level overview of business
and data strategy that a machine learning practitioner needs
to know, followed by a detailed walkthrough of how
to install and validate one of the popular artificial
intelligence frameworks, TensorFlow,
on the Intel Xeon scalable platform.
The guide details the steps for installing and running
TensorFlow framework and examples
in three different ways--
bare metal, containers, or on the cloud.
So you can choose to try one or all three
options of deployment, and then make an educated
decision as to which option makes sense for your business.
Now that we know what is covered in the guide,
let's talk about AI as a whole and look
at the definition of AI.
The term "artificial intelligence"
is continually evolving, but at its core,
AI is about machines mimicking and sometimes exceeding
cognitive functions associated with the human mind.
AI can be thought of as a giant umbrella,
a subset of machine learning.
Machine learning can be defined as machine algorithms whose
performance keeps improving as they're
exposed to more data over time.
A subset of machine learning is Deep Learning, or DL,
where multilayered neural networks learn
from vast amounts of data.
Deep learning is the branch of AI
that has gained huge popularity and adoption in recent years.
The nexus of the AI developments in the near future
is centered around deep learning with other approaches
all playing important roles depending
on the data set, problem, and unique requirements.
The framework and examples provided in the guide
are based on deep learning.
DL comprises of two major pieces--
training and inference.
Training teaches multilayered neural networks,
also known as models.
These make it possible to identify things
like objects or text.
This is done by feeding label data or content into the model.
Once the model is trained, inference
begins using the training model to identify unlabeled content.
Finally, let's cover some basic business considerations for AI.
If you're interested in creating an AI-based application
and applying it to your business,
learning these business considerations
are as important as understanding
the technical side of AI.
The business imperative of AI is firmly rooted in data--
the currency of the future.
By 2020, we expect over 50 billion devices and 200 billion
sensors to join the internet.
And this huge explosion of smart and connected devices
will lead to incalculable volumes
of data being generated.
This data contains extremely valuable insight for business,
operations, and security that affected industrial really
want to extract, analyze, and interpret in real time.
Extracting value from that data requires all the AI tools
at our disposal.
The first step on your AI journey
is to prepare your data.
And for that, when thinking of an AI business model,
it's important to focus on the entire data lifecycle.
You should think about creating, sourcing, transmitting,
ingesting, and cleaning your data before you
can apply AI models to it.
Doing this will help you create a competitive
end-to-end-optimized database solution in the AI space.
Apart from having a solid data strategy,
your first step should be to define the challenges
you're facing across the organization
and prioritizing them based on the business value
and how much it would cost to solve them.
The next steps are to determine which AI approach is
best suited to each problem.
There are a few more steps you will
have to consider and analyze, but the bottom line
is if you think about all these steps in the AI life cycle,
you'll stand a much better chance
of realizing the business value that you originally set out
to deliver.
AI is revolutionizing the world.
And to be a part of it, it's important to understand
the basics of TensorFlow on the Intel Xeon scalable platform.
I hope you'll join me for the entire AI Practitioners
Guide for Beginners, the video series.
Don't forget to check out the complete written
guide on the Intel Developer Zone linked in the description.
Thanks for watching and keep developing.
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