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7 Incredible Frameworks for Smooth Integration of AI in App Development

Statista says that the global AI market will cross a whopping $126 billion by 2025. As AI becomes more prevalent globally, many developers seek ways to integrate AI in App development and software systems. 

Notably, most of these integrations revolve around enhancing the user experience of the end consumers. The infographic below gives insights into the top use cases of AI & ML worldwide.

Source: Statista – AI & ML Use Cases

The widespread usage of AI in app development has led to the birth of tons of libraries and frameworks. Thanks to these advanced frameworks, developers can do their tasks without spending too much time on the projects.

Thus you can hire app developers at the usual cost without paying exorbitant prices for AI integration in software development. Many of the most popular AI frameworks have been written in Python, and there are several that are especially useful for app developers.

These are the top 7 frameworks notably used for integrating AI in App Development –

    • Pytorch
    • Google Cloud ML
    • Keras
  • Tensorflow
  • Caffe
  • Theano
  • Microsoft Cognitive Toolkit

Let’s explore these AI frameworks in detail.

Pytorch – Higher Control Over Memory Allocation

pytorch

Source: Nvidia

Developed by Facebook’s research team, PyTorch is a flexible and dynamic tool developer can use to solve various issues. Due to its versatility, it’s been used in such popular applications as Snapchat, Twitter, and Pinterest. While initially intended for computer vision projects, it’s also suitable for text processing and machine learning.

Despite its age, many still consider PyTorch one of today’s best frameworks. It was designed from an imperative programming point of view, making it easy to understand even if you aren’t a Python expert. It has fast execution speeds with minimal requirements.

The PyTorch framework supports CPU and GPU backends and offers more control over memory allocation than TensorFlow.

Pros of using PyTorch for AI in App Development

  • It’s flexible and dynamic.
  • It’s come with a wide range of applications, including text processing and machine learning.
  • This supports both CPU and GPU backends, with more control over memory allocation than TensorFlow.
  • Its execution speeds are fast with minimal requirements.
  • Its flexible and dynamic nature makes it easy to understand even if you aren’t a Python expert.
  • A relatively young framework that is currently considered one of today’s best frameworks.

 Google Cloud ML – Leverage NLP, Speech Recognition, Computer Vision, etc.

Google’s Cloud Machine Learning platform aims to provide a comprehensive suite of machine learning and artificial intelligence application development tools. It includes standard ML tools, like Natural Language Processing, Speech Recognition, and Computer Vision, and components that support more specific needs like distributed processing frameworks, an Image Labeling Toolkit, and custom datasets.

Using Google Cloud for AI in app development lets developers build their custom data pipelines. Google Cloud Machine Learning makes it possible to train models at scale by distributing training across multiple GPUs or CPUs on Google Compute Engine virtual machines or Google Container Engine clusters while providing a simple Python API.

 The top benefits of using Google Cloud ML include the following:

  •  Cloud ML is integrated with Google Cloud Platform, which means it can be easily scaled up or down to meet your needs as they change over time.
  • You can use TensorFlow and other frameworks with Cloud ML, allowing you to use various tools and techniques depending on your needs.
  • The system also supports third-party models like Caffe and ONNX that have been converted to TensorFlow format via a converter tool available on GitHub.
  • You can also convert models yourself using TensorFlow Model Optimizer, which is currently in beta but will soon be generally available.

Keras – Built-in neural network primitives & operations

A good starting point for beginners, Keras is a highly accessible deep learning framework written in Python & capable of running on top of Theano as well as Tensorflow. While it’s useful for training models for deployment at scale, it also serves as a great way to get hands-on experience with deep learning techniques.

Plus, working with neural networks often requires you to optimize code until your algorithm runs as efficiently as possible—which is where Keras’ ability to run on top of either Theano or TensorFlow shines. Keras utilizes an intuitive syntax that makes writing complex neural network layers simple (and even fun!).

Notable benefits:

  • It comes with built-in neural network primitives and operations, such as convolutional layers, recurrent layers, pooling layers, dropout layers, and others.
  • Easy to use API that provides an abstraction layer over TensorFlow or Theano.
  • Supports state-of-the-art deep learning models including: ResNet50, Inception v3, Xception V1 & V2 (and more).
  • An intuitive syntax that makes writing complex neural networks easy and fun!

Tensorflow – High levels of performance 

tensorflow

Source: Systango

TensorFlow is an open-source software library for numerical computation using data flow graphs. The API was created by Google and offers machine learning developers and users a library that allows them to implement AI technology into their projects quickly.

Overall, TensorFlow is likely your best bet when integrating AI in app development because of its scalability. One of the advantages is that it allows flexibility, as it can be adapted to different frameworks. It also provides high levels of performance as well as concurrency.

With Tensorflow, you can easily hire developers or programmers to train AI models on a single machine or multiple devices. Moreover, because Tensorflow comes from Google, you can expect high quality.

Here are the top benefits of using TensorFlow:

  • It’s fast and scalable, making it an excellent option for large datasets.
  • It can be used to develop machine learning models across multiple platforms and languages, including C++, Java, Python, and GoLang.
  • This offers a high level of flexibility.
  • This allow developers to integrate it into the existing code base and use it with other frameworks like TensorFlow Lite or TensorFlow Mobile (for Android).
  • Depending on the project needs, coders can train models on a single machine or across multiple devices to increase performance or scalability.

 Caffe – Diverse options for customization

Caffe was developed by Berkeley Vision and Learning Center and used a convolutional neural network design to optimize the speed of images. It may be an older framework, but that doesn’t mean it isn’t powerful. If you are looking for something simple with many options for customization, then Caffe is worth your time.

Caffe offers many AI-based features like image recognition, image segmentation, and object detection. It’s also great for image-based deep learning models, including classification, detection, and segmentation.

It has a lot of flexibility concerning its deep learning models as well. The framework is flexible enough to support research into other areas, including natural language processing (NLP) and reinforcement learning (RL). It’s also worth noting that Caffe is an open-source framework developed by Berkeley Vision and Learning Center.

Other benefits include the following:

  • It is highly flexible and can be used with many neural network architectures, including convolutional networks, recurrent networks, long short term memory (LSTM) networks, and others.
  • It has a large community that continues to grow thanks to its open-source nature.
  • This has many contributors, which allows it to be updated as new features are added or bugs are fixed. It also makes it easier to find support if you have any questions or need help with your project.
  •  Caffe can run on multiple GPUs at once, which means you can train your models faster than ever before!

Theano –  Easier to share & understand code 

theano

Source: GitHub

If you want to put your deep learning algorithms on a CPU or GPU, you can use Theano. Theano is open-source software developed by a team of researchers at the Université de Montréal. It’s not just an algorithm; it’s a complete framework that implements mathematical expressions. If you choose Theano, it might be easier to share and understand code with other users.

Plus, it offers excellent performance when processing images, video, or audio data. In machine learning and neural networks, Theano is one of our most popular frameworks for integrating AI in app development.

However, that’s not all Theano also comes with a deep learning toolkit called Lasagne, making it easier to build deep learning models.

Advantages of using Theano are:

  • This is straightforward to use and has a simple syntax.
  • It’s allow you to efficiently define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays.
  • This supports GPU computation using CUDA or OpenCL.
  • Coders can use it on multiple platforms, including Windows, Linux, Mac OS X, iOS, and Android.

Microsoft Cognitive Toolkit – Fully-integrated platform for deep learning

 

While Microsoft’s Cognitive Toolkit isn’t an off-the-shelf product like some of its competitors, it may be the most flexible framework. It’s written in C++, which could be an issue if you’re not a developer yourself, but Windows is also making moves to open source it.

It boasts convolutional neural networks (CNNs), long short-term memory units (LSTMs), and recurrent neural networks (RNNs). These features make it especially useful for learning and robotics applications. 

Microsoft Cognitive Toolkit is a fully-integrated platform for deep learning that requires fewer prerequisites (Python) than some of its competitors. Its flexibility means programmers can use it to create intelligent apps; or with open source projects like Apache Kafka and Apache Spark. You can stream your processing needs into more advanced models that can generate predictions.

It’s also a plug-and-play solution capable of running artificial intelligence at an enterprise scale by providing core infrastructure services such as data ingestion, model training, and execution service across multiple devices or backends.

Significant Benefits:

  • It is compatible with many different deep learning frameworks, making it a good choice if you plan to use more than one of them.
  • Integrated into other open-source projects, like Apache Kafka and Apache Spark, which many enterprises use for streaming data processing needs into more advanced models that can generate predictions.
  • This has a complete model deployment service that can run artificial intelligence across multiple devices or backends at an enterprise scale.
  • It’s compatible with Python, C++, and C# so that developers can use it with a wide range of applications, including Windows 10 and Windows Server applications.
  • This is designed to help companies improve productivity and accuracy through computer vision, speech recognition, and natural language processing (NLP).

 

Choose as Per Project Requirements

 

With each new advancement or innovation, like machine learning, it’s essential to evaluate how you may use it in business processes and whether it’s worth integrating into systems.

One challenge is not all frameworks are easy to use — choosing a poor framework will make your job more complex instead of simpler. Moreover, it’s essential to weigh the project requirements before choosing a framework for integrating AI in App development.

Some frameworks require a Python programming background, while others may be more suitable for developers with Java or C++. Once you know your requirements and how much time you can devote to integration, choosing a framework that fits your current project needs is more accessible.

If you are not sure, then connecting with a dedicated development team can prove beneficial as they will help you choose a framework that is best suited for your project.

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