Latest Ranking: Best Deep Learning Frameworks for 2025
In the rapidly evolving world of Artificial Intelligence (AI), a plethora of frameworks are simplifying the development and deployment of AI systems. As we approach 2025, these tools have become essential for building AI systems with affordable budgets and the flexibility to scale production.
One such framework, Hugging Face, has revolutionised the way developers use AI models. By providing pre-trained models for natural language processing, computer vision, and audio data, Hugging Face has expedited the development process for chatbots, translation systems, and image classifiers.
TensorFlow, developed by Google, remains a trusted choice for training at scale, particularly in data-sensitive industries such as healthcare and finance. The TensorFlow ecosystem offers Keras for application building, TensorFlow Lite for mobile app running, and TensorFlow Extended for large-scale production.
PyTorch, preferred by many researchers for its simplicity, is another strong contender. Its continuous improvement and feature updates have made it a popular choice for both research and real-world applications. JAX, known for its speed and efficiency, is often used in scientific projects where performance is crucial.
MindSpore, developed by Huawei, is a versatile framework that works across different platforms, including cloud servers, edge devices, and smaller gadgets. Its ability to switch between dynamic and static modes provides more flexibility to users, while its automatic parallelism simplifies the training of massive models. MindSpore has found wide adoption in sectors such as telecommunications, healthcare, and manufacturing, especially in China, for developing AI applications and edge computing solutions.
On the other hand, OneFlow, primarily used by tech companies and research institutions focusing on high-performance computing and AI development, simplifies large-scale training by allowing developers to focus on building models instead of dealing with setup complexities.
ONNX, DL4J, and MXNet ensure compatibility and enterprise adoption across AI systems. ONNX focuses on compatibility, allowing users to run models built using one to work in another. Deeplearning4j (DL4J) is popular in companies that use Java or Scala, while MXNet, which supports multiple programming languages, continues to be relevant in 2025.
For beginners in the AI domain, Keras often serves as the starting point due to its user-friendly nature. MindSpore, OneFlow, and Hugging Face, with their simplified use of advanced models, have further streamlined the development process, making AI more accessible to a wider audience.
In summary, the AI landscape in 2025 is characterised by a variety of frameworks that cater to different needs and sectors. As these tools continue to evolve, they will undoubtedly play a crucial role in shaping the future of AI development.
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