Obtain a high-level overview of business and data strategy that a machine learning practitioner needs to know. Follow detailed instructions on how to install and validate one of the popular artificial intelligence frameworks, TensorFlow*, on the Intel® Xeon® Scalable platform.
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 walk-through of how to install and validate one of the popular artificial intelligence frameworks, TensorFlow*, on the Intel® Xeon® cSalable platform [sic].
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 [sic] two major pieces–: raining 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 [sic] 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 life cycle. 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. [sic]
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.
Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.
Notice revision #20110804