Not everyone has to be a Machine Learning expert. It is a complex subject that many people struggle to understand in detail. However, because of the impact it is having across a number of industries, it is important to at least understand its fundamentals. In that sense, understanding the basics of the different Machine Learning frameworks is an important step. These, sometimes referred to as technologies, are very useful to developers when building powerful ML algorithms.
If you are looking to get familiar with the concept of Machine Learning frameworks, then this article is for you. I will discuss what these frameworks are, and I will also address some of the most important ones and how they can be applied to your business.
What is Machine Learning?
We’ve discussed in previous articles what Machine Learning is. In brief, Machine Learning is a subset of Artificial Intelligence that, given an input A, is capable of determining an output B. This technology is sometimes referred to as supervised learning because it can be used by computers to ‘learn’ how to perform a specific task with the help of labeled data. That is, a computer can learn to perform a task if the right input is given to it; that’s why the concept of learning is used to describe this process.
Some of the most common uses of Machine Learning include predictions, recommendations, analysis, and personalization, among others. So, for example, an eCommerce app can use it to recommend a user specific products based on a recent search. Similarly, a FinTech ML algorithm can be used to assess risky operations or analyze customer analytics. Although, in principle, the fundamentals are the same, each specific use case varies depending on the goal.
What Are the Technologies Used in Machine Learning
It is common to hear people refer to such a thing as a Machine Learning technology. However, this is sometimes misleading. There is no clarity as to what specifically an ML technology refers to. It seems sometimes that the term refers to libraries, whilst others it seems to refer to tools like TensorFlow (more on that later), and sometimes even to ML-friendly programming languages like Python.
In general, the term technology refers to specific Machine Learning frameworks. Similar to different ML use cases, the technology being used may vary based on the goal that needs to be achieved. It is not the same to use Machine Learning for data mining purposes as for privacy-related matters or even computer vision algorithms. Different technologies can achieve different purposes and demand different resources in terms of hardware and software.
With cloud-based apps becoming more common, it is in turn becoming more convenient for developers to use cloud technologies to deploy ML features in apps; apps that are heavily dependent on the cloud are known as cloud-based apps. This way, developers can easily implement new features without users having to access their device’s processing power. So, as ML algorithms become more complex and demand more computing power, the cloud comes in very handy.
What is Deep Learning?
Within the existing ML techniques, Deep Learning is the one that generates the greatest hype among experts. Deep Learning is a form of unsupervised learning that uses unlabeled data to determine the output. This means that, through the use of information processing techniques known as neural networks, Deep Learning algorithms can perform complex operations without the need for pre-processed inputs. This takes the idea of computers being able to learn to a whole new level. Although we are still far away from Artificial General Intelligence, computers are surely becoming ‘smarter’ thanks to ML techniques like Deep Learning.
As computers become more powerful, we can expect to see better and more sophisticated neural networks; subsequently, we should also expect a growing dependence on cloud technologies. To some, like AI expert Andrew Ng, these techniques and AI in general are the equivalent of electricity in the XXI century. At first, it might seem like only a few companies will need them, but in the long term, they will become the de facto standard for every single industry. They will reshape our lives in important ways, up to the point where we normalize them, making it hard to imagine life without them.
What is a Machine Learning Framework?
The challenge with different Machine Learning techniques is having valuable and large amounts of information known as Big Data. The better the input given to a Machine Learning algorithm, the better its output will most likely be. However, the output does not only depend on the quality of the data being fed to the algorithm. Other important aspects, like the algorithm itself and the framework being used also matter.
Machine Learning frameworks, also known as ML frameworks, refer to a programming library, also known as a set of packages or modules that are used by developers to build Machine Learning algorithms. Machine Learning frameworks are very useful, as they allow developers to use pre-existing programming resources rather than having to start from scratch. These help save time and also help solve the issue of code standardization. By using the same framework, developers can understand each other’s code and work on the same projects easily. Additionally, because many of these frameworks are open-source, they are constantly updated and serve a variety of purposes.
Machine Learning Frameworks
There are a couple of Machine Learning frameworks that have become developers’ preferred choices. Among these, we find tools like TensorFlow and PyTorch. Both frameworks are open source tools that use the Python programming language to develop powerful ML algorithms. These tools have, in a way, helped establish Python as one of the most popular programming languages to work with Machine Learning.
AWS offers cloud services to work with tools like TensorFlow. Koombea is an AWS partner that can help you throughout this process.
Popular Deep Learning Frameworks
TensorFlow is one of the most popular frameworks to work with Machine Learning. It is highly used by professionals in the field and has a lot of documentation and other resources that help developers become familiar with it.
Within TensorFlow, developers usually work with Keras. Keras is a Deep Learning API that encompasses a library of neural networks that can be used on tools like TensorFlow, but also on other alternatives like Microsoft Cognitive Toolkit and Theano.
Theano is another important Deep Learning library. It is also an optimizing compiler that is highly used for mathematical expressions. It is particularly good at evaluating multi-dimensional arrays. Like many other Deep Learning frameworks, it uses Python as its programming language.
Although it is mostly used by engineers and scientists instead of developers, MATLAB offers a powerful way to build neural networks and other Machine Learning techniques. Aside from that, it can also be used for other applications like computer vision. It works on its own programming language, but it can work using Fortran and C.
Microsoft Cognitive Toolkit (CNTK)
This is a free open-source Deep Learning toolkit developed by Microsoft. Thanks to its capability to easily scale, CNTK offers a powerful commercial potential for companies.
This Deep Learning framework is particularly focused on modularity. Maintained by the Berkeley Vision and Learning Center (BVLC) and its community, the Caffe framework is used for Computer Vision and GPU-related tasks.
Implementing Machine Learning Frameworks
Thanks to some of these powerful Machine Learning frameworks, you can easily implement cutting-edge technologies into your app, helping your business be more productive. As they become more common, companies will face the challenge of being updated in order to offer their clients a superb experience. Apps that fail to consider these technological advancements will risk being left behind.
A common mistake many companies make when implementing AI features in general is to think that, by doing so, they become an AI company. AI and Machine Learning will become like any other technology or tool that companies use, but this does not mean that a company that implements them becomes dedicated to them. Companies that implement them will remain within their existing business, but they will have to learn how to adapt to the new AI-driven environment.
Like with many other new technologies, we are now moving from a stage of early adoption to one where it is becoming normal to have them. The sooner your company starts figuring out how Machine Learning and other AI technologies can help increase your productivity, you will be better prepared to compete in the highly competitive markets of the future. The first thing is to figure out how you can benefit from these new technologies.