Modern Artificial Intelligence Frameworks

Mar 13
modern Artificial Intelligence

As technology continues to advance, so does artificial intelligence (AI). In the past, AI was only accessible to large companies with huge budgets, but over the last few years, modern AI frameworks have made AI available to everyone. These frameworks are designed to help developers create and deploy AI models quickly and easily. They provide a range of tools and features that make it easier for developers to build, train, and deploy machine learning models. 

With the help of these AI frameworks, individuals and smaller businesses can now integrate robust AI into their processes without breaking the bank. In this article, we will discuss some of the modern artificial intelligence frameworks that are available and how they are revolutionizing the world of artificial intelligence.

Tensor Flow

Tensor Flow is a framework that is growing in popularity as a means of getting into AI. While not as strong of a competitor as other options, it is one of the easier ones to get into and put to use. It can still provide solid performance for many applications when used to its strengths. 

One of the features that make Tensor Flow a desirable place to start with AI is that it is an open-source program. Google is involved in its development and there is a growing open-source community that offers support for projects. The drawback that makes Tensor Flow not the best option for some situations is the way it processes data. Its processing method can be time-consuming because it takes a less-than-ideal path for analysis and processing. On resource-rich machines, this shouldn’t be much of a problem, though. 

Amazon Machine Learning

Amazon has its own offering available called Amazon Machine Learning that can be used as a stand-alone resource or combined with existing Amazon resources like S3. What makes AML so special is that Amazon went through a lot of effort to provide specialized tools for different machine learning tasks, as well as different levels of user experience. 

Being able to cater to your IT team’s experience level when working with AI makes a big difference in how willing companies will be to adopt the new platform. This also means that companies of any size, even thoughts that cannot support a large, highly skilled AI team, can utilize Amazon Machine Learning to improve their business processes. 

While easier to use for different experience levels, the main drawback to AML is that it is highly data driven to the extent where it hurts the user experience. Much like how early computers only had the command input without a user interface, AML is in a similar state where it does not have the data visualizations needed for most users to be comfortable with every step of their process. This can make it harder for users to oversee what they are doing or collaborate since the way that you interact with AML is not as straightforward as other systems. 


PyTorch is another open-source option that makes it easy for new users to get into working with AI. PyTorch uses Python as a code base, making it easy for Python developers to start using PyTorch. 

What makes PyTorch special is the rapidly-expanding community around it that offers support and development help. There are already a ton of specialized tools and resources dedicated to PyTorch and its users. You can likely find everything that you need to make PyTorch a strong part of your business. 

There are two major potential issues with using PyTorch. The first is the lack of commercial support. There is no one company responsible for maintaining PyTorch, which means that there is no professional support team. However, you do get support from the open-source community, which can be as, if not more, effective than commercial support teams.

The other major potential problem is that PyTorch lacks one major element: AI training code. AI learns and develops by being trained through repetitive tasks and keeping the changes that improve performance. PyTorch does not have a training system built in, which means that users have to write their own training code.

This can be a drawback if you don’t have to staff to develop the code that you need. In fact, it could be a deal-breaker unless you can find a resource that can do it for you. However, this can also be a major advantage if you have the resources to develop the code exactly the way that you want it. You have the chance for a more detailed and controlled training process rather than having to be largely hands-off throughout the process. 

Learn More About AI Development

Working with modern artificial intelligence is becoming the norm in many industries. As these new technologies become available, your company must continue to learn more about AI development to keep up. Learn more about AI from KitelyTech or give us a call at (800) 274-2908 to discuss your AI resource needs.

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