What are AI frameworks?

Early AI frameworks started within academic or research communities as a way to support the development of neural networks. As deep learning evolved, the frameworks that followed catered to large-scale AI models and more specific use cases like natural language processing (NLP). AI frameworks also became widely available to novice data scientists, beginner AI developers and enterprises without expertise in machine learning, making AI more accessible.

AI frameworks have built-in functionalities to help accelerate and simplify machine learning pipelines. While each framework varies, typical elements include:

• Packages and libraries for preparing, processing and loading datasets

• Implementations of common machine learning algorithms

• Preconfigured and pretrained models ready to be fine-tuned or deployed

• Methods for model training and model evaluation

• Application programming interfaces (APIs) to facilitate AI development in different programming languages

• Extensions for optimizing hyperparameters and model performance

Some frameworks offer these advanced features:

• Distributed training APIs and libraries for training models across multiple machines or GPUs through parallel computing

• MLOps tools for model deployment and monitoring

• Visualization tools to better understand models through histograms of weights and biases, plots of learning curves and model predictions, and graphs of metrics like accuracy and loss

Designing the underlying architecture of AI systems can be a complicated endeavor, but building it from the ground up adds to that difficulty. AI frameworks encapsulate machine learning complexities into modules, reducing the time and effort spent on understanding the underpinning math and statistics and transforming that into software code.

Here are some of the main advantages of AI frameworks:

• Reduced development cost

• Standardized workflows

• Swifter implementation

The predefined building blocks that come with AI frameworks allow organizations to save on initial development costs. Rather than spending on coding AI apps from scratch, enterprises can invest in crafting solutions targeted to business and customer needs.

Frameworks provide foundational methodologies and AI tools for the development process. As such, they aid in establishing standard machine learning workflows that are scalable for diverse AI projects—from automation, computer vision and generative AI (genAI) to natural language understanding and robotics.

The comprehensive software development environment of AI frameworks helps speed up and streamline the process. It also opens up avenues for rapid prototyping, which can then lead to faster innovation and quicker time to market for AI-powered applications.

Enterprises have various options to pick from, so they must think about their requirements and use cases. A fitting framework meets a company’s short-term needs, addresses their long-term goals and suits their technical capabilities.

Here are a few factors to consider when selecting an AI framework:

• Ease of use

• Integration

• Interpretability and explainability

• Open source versus proprietary

• Performance and scalability

Despite its prebuilt components, an AI framework still has a learning curve. Businesses might want to try out different frameworks to assess how user-friendly they are and check for clear and detailed documentation, tutorials and other resources to smoothen the learning curve.

An AI framework’s compatibility with an organization’s tech stack is essential. IT teams must evaluate how well a framework integrates with data sources, infrastructure and other platforms.

The ability to interpret the results and outputs of machine learning algorithms and understand the decision-making processes behind AI models are often crucial for sectors like healthcare and finance. Some AI frameworks incorporate techniques for AI interpretability and AI explainability.

Open-source AI frameworks are usually free to use for any purpose, while proprietary frameworks are often licensed for commercial use. Open-source frameworks are cost-effective and offer greater control and transparency. Meanwhile, proprietary frameworks come with dedicated and timely support and more advanced features.

High performance and scalability are vital, especially for AI systems handling huge data volumes or requiring real-time responses. These systems can benefit from frameworks with optimization features and distributed training functionalities.

Frameworks continue to improve alongside advancements in AI. And while it’s impossible to mention every framework out there, here’s a list of some of the most current and popular AI frameworks:

• Hugging Face

• IBM Watson Studio

• Keras

• LangChain

• PyTorch

• Scikit-learn

• TensorFlow

Hugging Face is a company known for its library of NLP-focused transformer models and its vibrant open-source community. Its Model Hub acts as a platform for sharing and accessing a myriad of pretrained AI models. Hugging Face also contains libraries for processing datasets and evaluating models and tools for model serving.

IBM Watson® Studio helps data scientists, developers and analysts build, run and manage AI models. It includes these features:

• A library of NLP models

• Visualization tools

• Optimization models that can be used for decision-making

• AutoAI to automate data preparation, model development, feature engineering and hyperparameter optimization

• MLOps tools for deploying and monitoring machine learning models

• AI governance tools

Keras bills itself as “a deep learning API designed for human beings, not machines.” It aims to make developing models as simple as possible for novices and experts alike.

Its high-level API is written in Python and can run on top of other frameworks like TensorFlow and PyTorch. This core API has built-in methods for creating, training and evaluating models. Keras also supports transfer learning, fine-tuning and distributed training.

Other members of the Keras ecosystem include the KerasHub library of pretrained models, the KerasTuner hyperparameter optimization framework and the Keras Recommenders library for building recommendation engines.

LangChain is an open-source framework for crafting applications powered by large language models (LLMs), including chatbots and AI agents. It employs a modular architecture, with each module representing abstractions that encapsulate the complex concepts and steps necessary to work with LLMs. These modular components can then be chained together to create AI applications. Its LangSmith platform allows for debugging, testing and performance monitoring.

PyTorch is a pioneer in the AI frameworks field. This open-source deep learning framework blends the machine learning library of Torch with a Python-based high-level API. It demonstrates versatility through a broad range of neural network architectures, from simple linear regression algorithms to complex convolutional neural networks.

The framework is known for representing deep learning models as dynamic computation graphs, giving AI developers the flexibility to modify a model’s code without having to reset the entire model during development, debugging and training. PyTorch also has a rich ecosystem of libraries and tools to supplement its core features.

Scikit-learn is one of the earliest open-source toolkits for data science and machine learning. It’s built on top of the Python libraries NumPy for mathematical and numerical computing, SciPy for scientific computing and Matplotlib for visualization. Scikit-learn has a wide variety of algorithms for both supervised and unsupervised learning, functions for model selection and evaluation, dataset transformation modules, dataset loading utilities and tools for predictive data analysis.

TensorFlow is another open-source AI framework pioneer. It treats data as multidimensional arrays called tensors, then builds a computational graph mapping the flow of data between operations in a neural network. It also supports eager execution, wherein operations are run immediately without creating a computational graph.

A brainchild of Google, TensorFlow offers a robust ecosystem of datasets, models, tools, libraries and extensions for building and deploying machine learning models, and even a dedicated full-stack package for creating recommendation engines. It also has active community support and a wealth of educational resources on machine learning.

Additionally, the open-source framework shares a suite of resources for incorporating responsible AI practices into each phase of the machine learning pipeline. These include tools to examine datasets for potential biases, privacy-preserving techniques like federated learning and libraries to evaluate metrics such as fairness.