The Top 7 Frameworks for AI in App Development

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With the rise of artificial intelligence in the tech world, many companies have set out to integrate AI into their applications to enhance their consumer experience and boost sales. The catch? It’s not easy, and it’s a difficult skill to master. Fortunately, there are several top-notch frameworks out there that can make the task less daunting, allowing you to devote more time to working on your project’s core features instead of trying to figure out how AI will improve them. Here are 7 frameworks that are worth checking out if you want your AI in app development to go smoothly.

1) Apple CoreML

Apple’s new CoreML framework offers impressive machine learning features, like Vision. While you may need a dedicated development team to implement it, you can use CoreML to help your app better interpret pictures and recognize objects. You could also use these algorithms to make smart image editing tools or other imaging services inside your app. For example, you could use vision data from an iPhone camera to improve an app’s ability to spot specific objects (like trees or cats) in pictures being taken by users and then add those pics automatically into a matching database.

2) Google TensorFlow

TensorFlow is Google’s open source software library for machine learning. It can be used both in production and research. It was originally developed by researchers and engineers working on the Google Brain Team within Google’s Machine Intelligence research organization for internal use before being released as an open-source project in 2015. TensorFlow offers APIs that allow programmers to easily express a machine learning model and then enables them to deploy it across mobile, desktop, server, cloud, or device platforms with just a few lines of code, simplifying machine learning at scale.

3) Microsoft CNTK

Despite its popularity, Microsoft’s framework has received some criticism from app developers and from Google engineers. Some say it requires dedicated development teams while others believe it’s not capable of performing certain tasks well. A good thing to keep in mind when choosing a framework is that many have their strengths and weaknesses, but no single tool can do everything. Depending on your industry, tools like CNTK could be a good option or they might not fit your needs at all. In most cases, having options is better than being forced to go with one framework that might not fit your business model as well as you’d hoped.

4) Facebook PyTorch

Facebook released PyTorch recently and has now turned it into a dedicated development team. PyTorch is one of those frameworks that’s open source, but has Facebook’s level of resources behind it, so you know that you have a dedicated development team working to maintain, improve, and add to it over time. With NLP especially still in its infancy, there is a need for new tools, which means using something like PyTorch could be useful down the line when building your next chatbot or app with AIs. So far, Facebook says about 1,000 developers are using PyTorch per day and more than 20 companies are collaborating with Facebook on projects related to PyTorch.

5) Amazon SageMaker

Amazon SageMaker is a machine learning platform that supports deep learning and other types of models. A benefit of using Amazon SageMaker is that it is easy to use without previous experience or expertise in building or training models. Amazon SageMaker includes built-in, high-level libraries and also provides access to low-level libraries, allowing users to use their preferred toolchain and data science environment. This platform also offers prebuilt algorithms and model templates that users can select when building their own models. At present, Amazon SageMaker supports TensorFlow; however, soon users will be able to run PyTorch code as well.

6) IBM Watson

Designed by IBM, Watson is a machine learning framework that is useful for tasks such as classifying images and speech. It’s based on a concept known as cognitive computing, which attempts to mimic human behavior and thought processes by gathering data and creating algorithms that can quickly make sense of it. Its main components are its Text-to-Speech system (which helps you determine whether an incoming message should be treated as spam), Question Answering system (which can generate answers to specific questions using natural language processing techniques) and Personality Insights (which helps you develop personal profiles). The only drawback: Watson doesn’t come cheap; it costs $50 per month or $100 per month depending on your needs.

7) Xiaomi MiAi

This framework is built by Xiaomi and uses an artificial intelligence to help create virtual chatbots. It can understand a wide range of languages and understands context, which allows it to be used effectively as a text-based chatbot that mimics conversations with humans. It’s most popular use is within customer service, but it can also be used in mobile banking. When combined with a mobile payment system, it could allow customers to pay bills and shop online without ever having to interact with a human representative. The machine learning allows MiAi to get smarter as more data comes through, so you don’t have to hire app developers over time when you want your chatbot technology to evolve with your needs.

Conclusion

The 7 frameworks listed above are all worth learning if you’re looking to integrate artificial intelligence into your app. And, because we’re talking about a constantly-changing field, I wouldn’t be surprised if other frameworks emerge in coming years. If so, feel free to let me know about them and I’ll add them to my list! Meanwhile, please remember that successful AI integration doesn’t have to be difficult—it just takes planning and execution of a plan. When you put together your plan and hire app developers who can deliver quality work, there’s no telling what you might accomplish. Good luck!

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