AI Frameworks: Choosing the Right One for Your Project

The AI framework you choose provides the foundation for the machine learning applications you design, from predictive models to generative applications. Your framework allows you to work with prebuilt tools, libraries, and workflows that enhance the development process and make your creations more efficient and scalable. 

By choosing the framework that best suits your needs, you can enhance the productivity and performance of your designs. Explore different types of AI frameworks, how to decide between open-source and paid options, and factors to consider when choosing the right framework for you.

What are AI frameworks?

An AI framework is a type of tool that helps you build and train artificial intelligence (AI) systems more easily. Depending on what you want to design, an AI framework can provide pre-made building blocks (like templates and libraries) to help you develop your algorithm faster without having to write each line from scratch. 

This framework system helps create a consistent development environment so you can standardize your projects. This uniformity helps you integrate your AI functions across different types of platforms and applications and simplifies your creation and implementation process.

Open-source vs. proprietary AI frameworks

Open-source AI frameworks are free to use and often prioritize transparency, evolution, and community support. While innovative and more customizable, these models can have security risks and limited direct user support. Proprietary frameworks, on the other hand, are managed environments that often have higher security measures, enterprise-level support, and specific built-in tools. These models prioritize customer support and user-friendliness but have lower transparency and require financial investments. 

If you’re focused more on experimentation and research, such as in academic or start-up environments, you might go for an open-source framework for flexibility. If you’re part of a large-scale organization that requires more security and support, proprietary frameworks may offer more features you’re interested in.

How do AI frameworks work?

Technically speaking, an AI framework is a development environment that allows you to select libraries, tools, and application programming interfaces (APIs) that help you develop your machine learning and deep learning models. 

These frameworks reduce the need to design each component from scratch and give you reusable building blocks for common processes, such as data preprocessing, model architecture, and integration. For example, you could use TensorFlow (an AI framework) to build a neural network with only a few lines of code, thanks to built-in functions for data preparation, verification, node layering, and more.

What are the frames in AI?

Frames in AI refer to structures that hold knowledge about a concept, like an object or situation. A “framework” in this context refers to software tools and environments that you can use to build and deploy AI models. 

Comparison of leading open-source AI frameworks

Choosing an open-source AI framework can be beneficial if you want to prioritize community support, flexibility, and a lower financial burden. To decide which AI framework best fits your priorities and projects, consider the following leading AI frameworks and the type of user they suit. 

TensorFlow 

If you’re working with complicated machine learning applications that prioritize high performance and scalability, TensorFlow may be the right choice for you. TensorFlow is an open-source framework created by Google, and it has a large number of built-in functions that help you develop and implement your machine learning and deep learning models. The workflow is generally divided into preprocessing, model building, and model training steps, each of which has available building blocks. 

TensorFlow tends to have the most impact when working with large volumes of unstructured data, such as media information like photos, videos, and audio. You can use TensorFlow for natural language processing (NLP) and image identification tasks, and it’s highly customizable to your needs.

PyTorch

If you’re working in development and prioritize the ability to quickly alter and fine-tune your model, PyTorch offers the flexibility and development control you might be looking for. PyTorch excels in applications prioritizing experimentation, which makes it popular for deep learning development in areas like computer vision and reinforcement learning. It has rapid debugging and user-friendly model creation features, so you can try out new ideas and quickly troubleshoot along the way.

PyTorch has a strong community support system, meaning you’ll have an abundance of resources at your fingertips to explore new concepts and see how other developers have overcome challenges. You can access pre-training models and tutorials, helping you build new skills as you become comfortable with the framework.

Keras

If you’re new to deep learning, Keras is a high-level neural network development tool designed to provide a simple, straightforward interface for quickly designing deep learning models. Keras has a clear coding structure and simple building blocks, meaning you can stack ready-to-use layers to build models for applications like NLP, large language models (LLMs), time-series forecasting, and recommendation systems.

One thing to note is that Keras actually needs support from a backend framework, such as TensorFlow or PyTorch. This allows these frameworks to provide the computational infrastructure needed to power your model, while Keras provides the interface you can use to define your algorithm. 

Can you work with multiple frameworks?

Yes, you can work between multiple AI frameworks. If your priority is framework interoperability, learning Open Neural Network Exchange (ONNX) can give you and your team a leg up. Rather than training models itself, ONNX allows you to bridge your developments between frameworks like PyTorch and TensorFlow. This allows you to work in a production environment where team members may prefer different frameworks or you need to deploy it in multiple environments.

Comparison of leading proprietary AI frameworks

If you are looking for more support and security—especially if you are implementing an AI framework within an organization or enterprise—proprietary frameworks may give you what you’re looking for. A few to consider include:

Amazon SageMaker

If your team is comfortable with Amazon Web Services (AWS) environments and wants end-to-end automation support, Amazon SageMaker is a great option. This platform offers fine-grained control over training infrastructure and has built-in tools like Amazon Q Developer (for discovering data) and Amazon SageMaker Catalog (for access control functions). 

You can use this unified platform for SQL analytics, data processing, model development, and generative AI development in addition to security and data governance functions. You can also use SageMaker alongside open-source frameworks like TensorFlow and PyTorch, and SageMaker will wrap them and their functionalities into a secure, enterprise-ready interface.

Azure Machine Learning

If you’re looking for a low-code option that is accessible to beginners, consider Azure Machine Learning. This platform offers a drag-and-drop interface that makes development and versioning simple for team members without a strong technical background. Plus, it integrates easily with Microsoft services, and you can find a range of beginner-friendly tutorials to help you learn basic skills.

Azure Machine Learning offers built-in tools for data preparation, feature storage, AI infrastructure, automated machine learning, and responsible AI development. In addition, you have access to the Azure AI model catalog, where you can choose from a range of foundation packages to help you build more complex applications without starting from scratch.

Challenges in AI framework implementation

Implementing AI frameworks often requires buy-in from your team or organization. In some cases, if you’re looking at proprietary options, the initial cost can deter you or your team from prioritizing AI framework implementation. In this case, you may opt to start with open-source options or smaller-scale projects that allow you to “test out” the new system before moving high volumes of data into the system. This also helps you troubleshoot how the new systems will work with your existing workflows.

Outside of financial concerns, you may experience challenges related to integration with existing organizational systems, lack of trust in the new software (e.g., concerns about privacy), or resistance to changing the status quo. Overcoming these barriers may take time, but you can take steps toward fostering a more innovative environment by celebrating small steps, prioritizing experimentation, communicating new ideas across departments, and inspiring teams with exciting projects aligned with team interests.

How to choose the right AI framework for you

It’s exciting to have so many available options, but figuring out which one to start with requires consideration of your project goals, team experience, and infrastructure preferences. Consider this quick guide to match your priorities with the most suitable framework.

• If you prioritize performance and scalability: Choose TensorFlow.

• If you prioritize flexibility and hands-on experimentation: Choose PyTorch.

• If you’re new to neural network development: Start with Keras.

• If you want to work across multiple AI frameworks: Learn ONNX.

• If you prioritize security and end-to-end model support: Choose Amazon SageMaker.

• If you prefer a low-code environment: Choose Azure Machine Learning.

Learn more about AI frameworks on Coursera

AI frameworks allow you to streamline your machine learning model development with exciting built-in tools and a wide network of support resources. To learn more about deep learning and what types of models you can build with these frameworks, consider the Deep Learning Specialization by DeepLearning.AI. You’ll have exposure to a wide range of model types, including CNNs, RNNs, NLP models, and deep learning applications.