What Is Keras? Your 2026 Guide

Key takeaways

Keras is a platform that simplifies the complexities associated with deep neural networks, allowing for the faster creation of models.

• Using Keras, you can more easily create neural networks, as the platform allows more experimentation and overcomes the challenges common to machine learning and deep learning. 

• Alternatives to Keras include TensorFlow, PyTorch, and MXNet.

• You can use Keras in various industries, including technology, education, financial services, and health care. 

Learn more about using Keras, types of Keras models, and alternatives to the platform. If you’re ready to build your skills, consider enrolling in the IBM AI Engineering Professional Certificate. You’ll have the chance to learn how to build deep learning models and neural networks using Keras, PyTorch, and TensorFlow. By the end, you’ll have earned a career credential to display on your resume or LinkedIn profile. 

What is Keras, and why is it used for neural networks?

Keras is a framework used for research and development within deep learning. You can also use it to create guides for developers and documentation throughout a project.

Keras simplifies the complexity of creating deep neural networks, providing a user-friendly application programming interface (API) that solves many challenges within deep learning. This productive, high-level API enables users to experiment more freely and overcome many of the challenges within machine learning and deep learning. 

You can deploy Keras using various platforms, including iOS and Android. You can deploy this platform-agnostic framework using Node.js, Python runtime, and others, which gives it excellent flexibility. In turn, this allows developers to use it across various types of applications.

As the Keras website notes, Keras is "designed for human beings, not machines" [1]. 

Types of Keras models 

You have three ways to create deep learning models when working in Keras. These data structures contain layers forming the units on which you can build deep learning models. Types of Keras model APIs include the following:

• Sequential: You use a single input and output to build the layers of Keras models in a linear stack for simple model development.

• Functional: While you would use sequential models for straightforward tasks, functional 

Keras models allow you to build more complex models. It supports more than one input and output at a time and offers more flexibility, making it the standard for many users. 

• Subclassing: In some instances, you may have use cases that don't meet sequential or functional Keras modeling standards. In this instance, you can use model training APIs to customize implementation. 

Keras alternatives

Keras is a user-friendly framework with several alternatives available. Three popular options include TensorFlow 2.0, PyTorch, and MXNet. Let's examine each in more detail before reviewing additional details about Keras. 

TensorFlow

Google released this open-source end-to-end framework in 2019, with features like visualization tools, feature columns for simpler data handling, and parallel training, accelerating the time it takes to train models. It's free and offers straightforward debugging with the TensorBoard feature. TensorFlow 2.0 also offers excellent scalability and compatibility with Keras.

The benefits of using TensorFlow 2.0 include:

• Use with Python or JavaScript

• Runs on various platforms, including local or cloud-based.

• Allows for high-level work using Keras' library.

• Offers abstraction, allowing developers to focus on the logic of the application.

• Eager execution mode for evaluating each graph operation individually

What is Keras vs. TensorFlow?

While Keras and TensorFlow do have some differences, you can use them together to take advantage of the Keras API as a TensorFlow user. By integrating the Keras API with TensorFlow, you utilize the Keras interface, data processing, and hyperparameter tuning while maintaining the scalability of TensorFlow. 

PyTorch

Meta developed PyTorch and released the open-source framework in 2016. Users often praise this deep learning framework for its simplicity and support for developers creating complex applications, including those for natural language processing. It features automatic differentiation, integration with Python for an easy-to-use interface, added community support, TorchScript for running models in various environments, and Tensor computation for added speed.

The benefits of using PyTorch include:

• Python-based structure for easier coding

• Straightforward debugging

• Supported on various platforms

• PyTorch library sought after for deep learning research

• Dynamic computational graphs

• Enables developers to research and build prototypes quickly

Read more: What Is PyTorch?

MXNet

Apache released MXNet in 2015. This open-source framework supports creating and training models in various languages like Python, Perl, Java, and Julia. Users appreciate its reproducibility, resource utilization, and ability to blend imperative and symbolic programming for fast training and effective creation of neural networks, with uses such as natural language processing and image classification.

The benefits of MXNet include:

• Excellent scalability across multiple BPUs and hosts

• Robust ecosystem with support

• Supports various languages, including C++, R, Scala, and more

• Hybrid programming for easier training and deployment

Who uses Keras?

Developers, researchers, and professionals across various industries, including information technology, education, financial services, and health care, use Keras. Some of the world’s leading institutions use Keras, including global organizations and major companies like:

• CERN, the European Council for Nuclear Research

• NASA

• National Institutes of Health

• Waymo

• Google

• YouTube

• Amazon

• Spotify

• Uber

• Netflix

If you’re considering working within the field of machine learning, deep learning, or artificial intelligence (AI), you may need to develop your skills and proficiency in working with Keras. A few examples of jobs in which you may use Keras include the following:

All salary information represents the median total pay from Glassdoor as of December 2025. These figures include base salary and additional pay, which may represent profit-sharing, commissions, bonuses, or other compensation.

Machine learning research scientist

Median total pay: $224,000 [2]

Job outlook (projected growth from 2024 to 2034): 20 percent [3]

In this job, you’ll primarily focus on developing AI algorithms and machine learning models. You'll work with data and collaborate with other specialists, such as data engineers and scientists. To work in this field, you typically need a bachelor's degree or higher in data or computer science or another related topic. You may work as a machine learning research scientist in many industries, including the federal government, computer systems design, scientific services, hospitals, educational institutions, or software companies, to name a few.

AI developer

Median total pay: $157,000 [4]

Job outlook (projected growth from 2024 to 2034): 15 percent [5]

As an AI developer, you'll create programs with AI functionality, incorporating algorithms into various projects and using deep learning or machine learning as needed. Common tasks include designing and developing AI systems, explaining systems to company leaders, building data architecture, and using deep learning platforms to help answer companies' challenges. Employers typically look for AI developers with a bachelor's or master's degree.

Deep learning engineer

Median total pay: $150,000 [6]

Job outlook (projected growth from 2024 to 2034): 20 percent [3]

Although this position is somewhat similar to that of a machine learning engineer, as a deep learning engineer, you'll primarily focus on the beginning stages of data engineering projects. You'll work within the modeling phase to define data requirements, train deep learning models, and deploy code. Bachelor’s degrees are common requirements for entering the field. 

What are the pros and cons of using Keras?

Like any other deep learning framework, Keras has its own unique set of benefits and drawbacks. We examine them briefly below.

Advantages of using Keras

• User-friendly: With its simple API and pre-trained models, Keras is simple to learn and use.

• Flexible: Deployable across various platforms and devices

• Speed: Faster, more intuitive, streamlined in research, prototyping, and deployment; Keras also offers fast debugging.

• Customization: Simple to customize and share models and their components.

• Excellent library: The Keras library features natural, frictionless abstractions.

• Ample community support: This open-source framework boasts a sizable community and excellent support.

Disadvantages of using Keras

• Limited features: Lacks many available online projects as alternatives, like TensorFlow, and it has yet to provide support for creating dynamic charts.

• Tricky debugging: Although Keras has integrated debugging, it can also pose challenges with tricky errors.

• Ineffective library errors: Users often report inefficient library error messages.

Explore helpful career resources 

Subscribe to our weekly LinkedIn newsletter, Career Chat, for career tips and AI and machine learning trends. Then, check out these free resources to broaden your knowledge:

• Learn from an expert: Reimagining Work and Learning with AI: Expert Insights from Dr. Jules White

• Watch on YouTube: Career Spotlight: Machine Learning Engineer 

• Build your knowledge: Artificial Intelligence Glossary: Learn AI Vocabulary

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