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Machine learning has been widely accepted as the future of technology – and with good reason. Artificial intelligence and machine learning are both used by nearly every industry for numerous purposes. Businesses use both AI and ML for training and inference of deep neural networks, to help power nearly every layer of their delivery pipelines.
Machine learning is used in the retail, medical, machine, software, support, and finance industries. In fact, there’s hardly a business sector that ML and AI haven’t touched. What’s more, ML and AI don’t only save you money, but also make your delivery chains and support systems work more efficiently.
Machine learning and artificial intelligence have found their way into technologies such as:
There’s very little you can’t do with machine learning and artificial intelligence.
But how does a business go about implementing ML and AI? You probably assume this technology is above the ability of your developers and teams. You might be surprised at how accessible it is. One reason this is so is because of TensorFlow.
Google has become the greatest champion of machine learning. The company knew it had the most powerful ML infrastructure in the world and wanted to develop the means to share that platform. For that, the Google developers created an open-source library of tools that enable developers to build deep learning into applications, platforms, and other products. That’s how TensorFlow was born.
First let's discuss what TensorFlow does and what it is, as it can be a bit confusing. Let's use Android phone photography as an example. When you use your phone to take a photo, you might snap an image of a location. When you do, you might discover that Google Photos is capable of identifying a landmark in that image (say the Golden Gate Bridge). How does Google Photos pull that off? Through TensorFlow.
TensorFlow offers a collection of libraries and modules that enable the construction and execution of what is called TensorFlow computations. These computations are expressed in stateful data flow graphs which make it possible for Google Photos to accurately identify landmarks.
Originally, Google developed what was called DistBelief, which worked on a positive reinforcement model. How this worked was simple: A machine was shown an image and was then asked to identify the image. An incorrect guess would lead to an adjustment so it could better recognize the image.
The next phase in that project was TensorFlow, which improved on the concept by employing layers of data, called Nodes. As the machine dove deeper into the layers of nodes, it could be asked (and answer) more and more complex questions about an image. One layer might require the machine to recognize a general shape, such as "round." A deeper layer would then require the machine to recognize a more specific shape, such as an eye. This process (from input through data layers to output) is called a tensor, which is where the name TensorFlow comes from.
Thanks to TensorFlow, devices continue to become smarter and smarter. And with the constant rise and assimilation of Big Data, the need for ML and AI has become critical for big businesses, as data is the driving force behind innovation and market expansion.
Thanks to ML and AI, businesses are now better able to use massive amounts of data to train their software to work more efficiently and effectively.
For your company to make use of TensorFlow, your developers are going to need to know a few languages. Those languages include:
Your engineers don't have to know every one of those languages, but they will certainly need to know Python, as it’s the most commonly used programming language for TensorFlow.
Those engineers will also need to know how to use:
The good news is that ML and AI are incredibly popular these days, and TensorFlow leads the charge, so you shouldn't have any problem finding strong developers who can effectively work with TensorFlow. Hiring them, though, will come with a significant cost, so keep that in mind.
If your business is serious about remaining relevant and being able to keep up with the competition, you're going to have to take machine learning and artificial intelligence seriously. To do that, you're going to need to find the best TensorFlow developers on the market, so your apps and services can benefit from deep learning.
TensorFlow is an open-source library that helps developers build and train machine learning models.
A TensorBoard makes it possible for creators to visualize the graphs and plot quantitative metrics about a graph with additional data.
Servables are objects that clients use to perform a computation.
Developed by Mozilla, Deep Speech is a TensorFlow implementation motivated by Baidu’s Deep Speech architecture.
Sources are modules that find and provide servables.
Supervised learning requires labeled data, whereas unsupervised learning does not.
We are looking for a highly capable TensorFlow developer to optimize our machine learning systems. You will be evaluating existing machine learning (ML) processes, performing statistical analysis to resolve data set problems, and enhancing the accuracy of our AI software's predictive automation capabilities.
To ensure success as a machine learning engineer, you should demonstrate solid data science knowledge and experience in a related ML role. A first-class machine learning engineer will be someone whose expertise translates into the enhanced performance of predictive automation software.
TensorFlow was developed by Google and has become the framework to use for machine learning. In fact, TensorFlow has become the de facto standard for today’s market. And because TensorFlow is free, and runs on both CPUs and GPUs, it’s even more attractive to more companies looking to leverage machine learning.
But don’t think you can just throw TensorFlow at your current developers and have them start implementing machine learning right away. This tool can be very challenging. Developers will need to have a solid understanding of both Python and C, which are the 2 languages that have stable and official TensorFlow APIs.
Although this full-blown machine learning research and production tool can be a bit intimidating at first, it’s possible to create simple predictions on data sets. But to get the most out of TensorFlow, you’re going to need experience.
What can you do with TensorFlow? This machine learning framework can be applied to use cases like:
To find out more about TensorFlow check out What can TensorFlow Do for Your Company?
PyTorch was created by Facebook AI Research (FAIR) to serve as a leading competitor to TensorFlow. PyTorch immediately took off and has become one of the most popular machine learning frameworks on the market. When deciding on an ML framework, the choice generally comes down to either TensorFlow or PyTorch.
Like TensorFlow, PyTorch can run on both CPUs and GPUs. However, where TensorFlow can make it possible to deploy a new model with incredible speed, PyTorch offers far more customization by following a traditional Object-Oriented Programming approach.
PyTorch also has some of the fastest training times of all machine learning frameworks. Although the speed might seem insignificant on a project-by-project basis when those projects scale to enterprise proportions (such as when using massive data), the speed becomes consequential.
In order for your developers to make use of PyTorch, they’ll need to understand Python. Once up to speed, your developers can use PyTorch for such things as:
PyTorch is primarily used for research, which makes it a great fit for businesses needing deep dives into data, which can result in detailed analysis.
Some of the benefits of PyTorch include:
Keras is built on top of TensorFlow and provides a Python interface to make machine learning modeling a bit easier. By simplifying a number of the steps (such as offering all-in-one models), Keras can work with the same code on either CPUs or GPUs. Keras also includes several commonly-used neural-network building blocks, such as:
All of this put together makes Keras considerably easier to use for working with both text and images. Of course, Keras isn’t limited to neural networks and can work with convolution, recurrent neural networks, dropout, batch normalization, and pooling. Keras can be used for machine learning models on iOS, Android, the web, or even within a Java Virtual Machine.
Keras was created using 4 guiding principles:
Because Keras is a minimalist Python library (that can run on top of TensorFlow), it’s important that your developers have a deep understanding of Python and TensorFlow, as well as how deep learning research works (and how it can be applied to your business).
To learn more about Keras, make sure to read the official guides.
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