AI Frameworks: Top Types To Adopt in 2025

With each passing day, AI becomes more integrated into our lives, reshaping how we work, play, and interact with the world around us. To harness the full potential of AI, you should dig deep into the foundations on which it's built — AI frameworks. 

What exactly are AI frameworks? Are they needed in AI development? 

Whether it's a complex neural network capable of mimicking human cognition or a simple machine learning (ML) model designed for predictive analytics, AI frameworks are the foundation of every technology.

So, let’s take a look at common AI frameworks today. We’ll also explore risk management frameworks that are specific to AI, a hugely important topic today.

Understanding how AI frameworks work 

AI frameworks are the building blocks for creating sophisticated, intelligent AI systems that can learn, adapt, and evolve. We can define an AI framework as a collection of libraries that simplify developing and implementing AI algorithms. 

AI frameworks streamline the creation and implementation of complex algorithms with their pre-built functions and libraries — so developers can customize AI models for specific tasks rather than building the underlying systems from scratch.

(Learn about Splunk AI.)

These frameworks standardize the development process. This means that regardless of the AI project, you’ll have a consistent set of tools and practices to use. With this uniformity, you can integrate AI functionalities into diverse platforms and applications. 

For example, TensorFlow has extensive libraries that reduce development time — a critical factor that’s a direct reason for its widespread adoption across industries. 

(Related reading: AI-augmented software engineering, adaptive AI & what generative AI means for cybersecurity.)

Top AI frameworks 

When selecting an AI framework, consider factors such as performance, community support, flexibility, and ease of learning to ensure that the framework meets your project's requirements and is compatible with your skill level. We’ll look at both few open-source and proprietary frameworks.

Open-source AI frameworks are flexible, adaptable, and cost-effective. Developers can experiment with various algorithms, adapt to different data types, and integrate with other technologies without issues.

Here are some of the top open-source AI frameworks:

TensorFlow 

Built by Google’s Brain Team, TensorFlow develops machine learning models — useful for tasks like language translation and facial recognition. Think of it as a bridge that turns complex theoretical concepts into practical applications, an invaluable feature. 

Its interactive user interface (UI) works across multiple platforms, so devs can visually track each part of their model's development. This aspect simplifies the complexities of ML algorithms.

To add more, TensorFlow's high adaptability and compatibility with both CPUs and GPUs facilitate model training and building across diverse computing environments. And this flexibility is due to its modularity and operability, so users can tailor the library to meet diverse project requirements. 

PyTorch 

Developed by Meta AI, PyTorch is user-friendly and enables rapid prototyping, a key aspect for efficient model development. It achieves this by minimizing the need for boilerplate code — which is commonly reused code — streamlining the modeling process. 

Recognized for its versatility and swift performance, PyTorch supports dynamic computation graphs so devs can make flexible adjustments during model development. Its automatic differentiation streamlines the creation and training of deep neural networks by enabling real-time code testing and debugging.

Keras 

Keras is a high-level neural network library in Python, built to be intuitive, making it easier for users to understand and work with. The Keras library is commonly paired with TensorFlow, enhancing its functionality thanks to Keras's clear and readable structure. Keras debugs and maintains elegant, easy-to-manage code — leading to less complex, more maintainable codebase. 

It supports a variety of neural network models and comes equipped with a comprehensive library of ready-to-use layers, activation functions, and optimization techniques. These advanced features not only make Keras adaptable and flexible but also an excellent tool for advanced research in neural networks.

Proprietary AI frameworks

In addition to open-source options, there are a few proprietary AI frameworks with advanced features and capabilities. Some of them include the following:

• CNTK (Microsoft Cognitive Toolkit) focuses on large-scale production deployments and supports popular model types such as feed-forward DNNs, convolutional nets, and recurrent networks. It’s available under an open-source license.
• H2O.ai offers an open-source machine-learning platform that provides a range of algorithms for data analysis and predictive modeling. It's scalable and easy to use.
• MxNet is an open-source deep learning framework associated with Amazon focused on large-scale production deployments. 

These open-source and proprietary frameworks let data scientists, developers, and researchers create, train, validate, and deploy AI models. You can choose the right AI framework and tool depending on specific needs for cutting-edge research or efficient production solutions.

Risk frameworks for AI  

Today, you’ve likely already heard about the risk inherent in any AI model. AI risk frameworks help organizations navigate the challenges that occur due to the use of AI in business operations.

Implementing a risk framework specific to AI within your organization can be a smart move because they:

• Improve the overall safety and reliability of AI systems.
• Ensure compliance with relevant regulations and standards.
• Build trust and confidence in AI technology among stakeholders.
• Identify and address potential risks in AI systems before they materialize.

Here’s a few notable risk frameworks for AI.

NIST AI risk management framework (AI RMF)

Developed by the National Institute of Standards and Technology (NIST), the AI RMF is a voluntary framework that emphasizes incorporating trustworthiness into the design, development, and evaluation of AI products, services, and systems. 

The four specific functions of this framework are: 

• Governing 
• Mapping
• Measuring
• Managing
  

(Related reading: AI ethics, risk management frameworks & AI trust, risk and security management.)

ISO/IEC 42001

The International Standards Organization establishes standards across industries and various business and technology practices. Among the latest is ISO 42001, issued in January 2024. 

ISO 42001 a great place to reference for any organization seeking to establish, implement, maintain, and continually improve an Artificial Intelligence Management Systems (AIMS). Designed for "entities providing or utilizing AI-based products or services", this standard helps to ensure the responsible development and use of AI systems.

(Related reading: ISO/IEC 27001 Standard for Information Security.)

AI risk governance: AI at Wharton

This framework, developed by the University of Pennsylvania's Wharton School, focuses on the risks of AI in the financial services industry. It categorizes risks into four main areas: 

• Inadequate governance
• Learning limitations
• Data privacy and security
• Societal impact

Big tech organizations can apply these risk frameworks at different stages of their business operations (when they work with AI) to ensure no risks are involved.

Secure AI Framework (SAIF)

Google is in the risk framework game, too, with their SAIF model that aims to enable a safer ecosystem. Some of the “core elements” of SAIF include:

• Expand strong security foundations to the AI ecosystem
• Extend threat detection and response, effectively bringing AI under an organization’s threat response ecosystem
• Automate defenses to keep pace
• Adapt controls for mitigations and faster feedback loops

Summing up

By adopting AI risk management frameworks, you can better manage the risks associated with AI technology and minimize the potential negative consequences.