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Artificial intelligence (AI) and machine learning (ML) are transforming software development. Over 70% of developers are already using or planning to use AI in their projects.
With capabilities like ML, computer vision, and natural language processing, AI has the potential to automate tedious tasks, create more intuitive user experiences, and gain insights from data right from the start — during the discovery phase of app development. However, successfully implementing AI requires careful evaluation of the available frameworks and libraries.
In this guide, you’ll explore the top AI tools for web developers. Whether you’re looking to add NLP, computer vision, or general ML capabilities to your app, these frameworks make it easy to integrate AI.
What Are AI Frameworks Used For?
AI frameworks abstract the complex math and algorithms required for AI. It allows developers to quickly add powerful AI capabilities without needing a deep background in data science. Here are some of the most common uses:
- Text analysis using natural language. Translate between languages, analyze text, identify sentiment, and recognize speech.
- General ML. Create and refine ML models to accurately predict outcomes, classify data, and power recommendation engines.
- Computer Vision. Analyze, categorize, and process images and videos.
- AI Application Development. Google Cloud AI, Amazon SageMaker, and Microsoft Azure ML simplify AI app development and deployment.
The right framework depends on your specific needs. But most handle the heavy lifting of building and training ML models, allowing you to focus on integration and application development.
How to Choose an AI Framework
With new frameworks constantly emerging, it’s getting harder to select the right one. Here are key factors to consider:
- Your tech stack. Look for a framework that integrates nicely with your existing web stack.
- Ease of use. Prioritize frameworks with good documentation and abstraction to ramp up quickly.
- Active community. Pick a popular framework with an engaged developer community for ample learning resources and support.
- Scalability. If building large-scale production apps, ensure your framework can handle growth and deployment.
By considering your current and future needs, you can zero in on a robust AI framework to supercharge your web apps.
5 Must-Haves in 2024
In 2024, there are a few key frameworks and libraries that might interest you:
1. Keras: The Developer-Friendly API
If you’re looking for an API that puts the user experience at the forefront, Keras is your go-to choice. It offers a smooth experience with consistent and simple APIs that alleviate the cognitive load. Say goodbye to complex setups and hello to clear, actionable error messages that make troubleshooting a breeze.
One of the standout features of Keras is its versatility. Working seamlessly with JAX, TensorFlow, and PyTorch, Keras empowers you to create models that transcend framework boundaries. This interoperability opens up a world of possibilities, allowing you to leverage the strengths of multiple frameworks within a unified environment.
2. Theano: Powering Mathematical Expressions
Theano is a Python library for evaluating mathematical expressions, especially matrix-based ones. It uses NumPy-like syntax and compiles computations to run efficiently on CPUs or GPUs.
Theano is a good option if you need flexibility and speed for building deep learning models from scratch. While Keras is higher-level and easier to use, Theano gives you more control and optimization opportunities.
3. fast.ai
With Fastai, users may easily achieve state-of-the-art outcomes in conventional deep learning tasks with high-level components that are rapid and simple to use. Researchers can combine the low-level components it offers to create new strategies.
Fastai has been utilized for tasks like:
- Image classification. Identifying objects, scenes, animals, etc. in images.
- Object detection. Finding instances of objects in images and locating their positions.
- Segmentation. Dividing images into regions that represent distinct objects or parts of objects.
- Tabular data modeling. Predicting numerical targets from columns of mixed data types.
- Time series forecasting. Predicting future values from sequences of past data.
Whether you want to create a computer vision model or build a natural language processing system, fastai makes implementing cutting-edge AI remarkably simple.
4. H2O.ai
H2O is an end-to-end open-source platform for data science and ML. Engineering, model training, deployment, and data preparation features are among its capabilities. A key highlight is its automatic machine learning (AutoML) functionality, which automates the model training process. H2O also provides interpretable models like its driverless AI tool that generates explanatory reports for models.
H2O is designed to be fast and scalable using an in-memory distributed architecture. This allows it to handle both big data and small data equally well. It can be used for various use cases like predictive maintenance, fraud detection, and customer churn. H2O has clients across various industries including financial services, healthcare, and technology. It’s used by over 18,000 organizations globally.
Overall, H2O offers many key capabilities needed to operationalize AI under one roof. Its visual interface and automation features make it well-suited for non-experts.
5. Hugging Face
Hugging Face is best known for developing its Transformer models like BERT that have advanced the state-of-the-art in NLP. Beyond models, it provides a platform for building and deploying NLP applications. Key features include tokenizers, pipelines, spaces (datasets), and inference APIs.
With Hugging Face, you can leverage pre-trained models to build text classification, question answering, text generation, and translation apps. The unified API makes it easy to use PyTorch, TensorFlow, or other frameworks interchangeably. Hugging Face Spaces allows you to share datasets, models, and apps publicly to foster collaboration. There are also inference APIs via Hugging Face Inference to host your NLP models.
For web developers, Hugging Face lowers the barrier to applying modern NLP. You don’t have to be an expert in deep learning or Transformers to build useful applications. The active community also means continued evolution of what’s possible with NLP.
Conclusion
With the variety of options, there is an AI framework for every web developer. Using the frameworks above, it is possible to quickly integrate AI capabilities without advanced data science skills. Give one a try in your next project.