Top 20 Machine Learning Frameworks for Rapid Prototyping

Machine learning frameworks can get confusing because they come in many shapes and sizes. Choosing the proper machine learning framework for your project can be daunting, especially considering each option’s speed, scalability, and performance. Selecting the appropriate framework is essential to achieving your goals for your AI project. In this blog, we'll explore machine learning frameworks and help you discover the best options to help you achieve faster, more innovative, and more efficient AI development that drives innovation and real-world impact.

If you want to get up and running quickly, Inference's AI inference APIs can help you easily reach your goals. You can leverage our pre-built APIs to minimize the time spent developing your AI application so you can focus on what matters: your project.

What is a Machine Learning Framework?

A machine learning framework is an interface with pre-built functions and structures that simplify many complexities of the machine learning lifecycle, including data preprocessing, model building, and optimization. With machine learning frameworks, developers can build models faster and more efficiently without understanding the underlying mathematical and statistical principles powering ML algorithms. Machine learning frameworks differ from machine learning libraries. An ML framework is a broader set of tools for controlling the overall ML application development process flow. At the same time, a library is a collection of prewritten code used to perform specific tasks.

Why Use Machine Learning Frameworks?

Machine learning frameworks offer businesses a cost-effective way to develop custom software applications. These frameworks eliminate manual coding and allow developers to use pre-built components, significantly reducing development costs.

Using frameworks will also enable businesses to build sophisticated applications tailored to their specific needs quickly. By leveraging AI and ML power, companies can develop more efficient software solutions than traditional software development methods.

How Machine Learning Frameworks Streamline App Development

When selecting machine learning frameworks, consider their ability to streamline development. These frameworks have pre-implemented algorithms, data handling utilities, and optimization techniques. As a result, they enable developers to focus on solving the specific problem at hand rather than getting caught up in the technicalities of AI implementation.

Machine Learning Frameworks Save Time

Machine learning frameworks are tremendous time-savers for modern software development. They simplify creating, testing, and deploying applications. They provide a complete development environment:

• With debugging tools
• Testing harnesses
• Data visualization capabilities

This speeds up the development process as developers can act quickly without manually compiling and debugging each code section. Machine learning frameworks also offer a wide variety of pre-built models.

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Top 20 Machine Learning Frameworks for Rapid Prototyping

1. Inference: Fast and Efficient Serverless Inference for LLMs

Inference delivers OpenAI-compatible serverless inference APIs for top open-source LLM models, offering developers the highest performance at the lowest cost in the market.

Beyond standard inference, Inference provides specialized batch processing for large-scale async AI workloads and document extraction capabilities designed explicitly for RAG applications.

Start building with $10 in free API credits and experience state-of-the-art language models that balance cost-efficiency with high performance.

2. Apache Singa: A Distributed Deep Learning Framework

Apache Singa is a general distributed deep learning platform for training big deep learning models over large datasets. It is designed with an intuitive programming model based on the layer abstraction.

A variety of popular deep learning models are supported, namely feed-forward models including:

• Convolutional Neural Networks (CNN)
• Energy models like Restricted Boltzmann Machine (RBM)
• Recurrent neural networks (RNN)

3. Amazon Machine Learning: ML Built for Everyone

Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. It provides visualization tools and wizards that guide you through creating machine learning (ML) models without learning complex ML algorithms and technology.

It connects to data stored in Amazon S3, Redshift, or RDS and can create a model by running binary classification, multiclass categorization, or regression.

4. Azure ML Studio: Build and Deploy Models to the Cloud

Azure ML Studio allows Microsoft Azure users to create and train models, then turn them into APIs that other services can consume. Users get up to 10GB of storage per account for model data, although you can also connect your Azure storage to the service for larger models.

Many algorithms are available, courtesy of both Microsoft and third parties. You don’t need an account to try out the service; you can log in anonymously and use Azure ML Studio for up to eight hours.

5. Caffe: A Fast and Modular Deep Learning Framework

Caffe is a deep learning framework for expression, speed, and modularity. It was developed by the Berkeley Vision and Learning Center (BVLC) and community contributors. Yangqing Jia created the project during his PhD at UC Berkeley.

Caffe is released under the BSD 2-Clause license. Models and optimization are defined by configuration without hard-coding. Its speed makes it perfect for research experiments and industry deployment. Caffe can process over 60M daily images with a single NVIDIA K40 GPU.

6. H2O: An Open Source Machine Learning Platform for Everyone

H2O allows anyone to easily apply math and predictive analytics to solve today’s most challenging business problems. It intelligently combines unique features not currently found in other machine learning platforms, including:

• Best of Breed Open Source Technology
• Easy-to-use WebUI and Familiar Interfaces
• Data Agnostic Support for all Common Database and File Types

With H2O, you can work with your existing languages and tools. Further, you can extend the platform seamlessly into your Hadoop environments.

7. Massive Online Analysis: The Go-To Framework for Data Stream Mining

Massive Online Analysis (MOA) is the most popular open source framework for data stream mining, with a very active and growing community. It includes a collection of machine learning algorithms:

• Classification
• Regression
• Clustering
• Outlier detection
• Concept drift detection
• Recommender systems
• Evaluation tools

Related to the WEKA project, MOA is also written in Java and scales to more demanding problems.

8. Pattern: A Flexible Web Mining Module for Python

Pattern is a web mining module for the Python programming language. It has tools for:

• Data mining (Google, Twitter, and Wikipedia API, a web crawler, an HTML DOM parser),
• Natural language processing (part-of-speech taggers, n-gram search, sentiment analysis, WordNet)
• Machine learning (vector space model, clustering, SVM)
• Network analysis and <canvas> visualization

9. Scikit-Learn: The Foundation for Machine Learning in Python

Scikit-Learn leverages Python’s breadth by building on top of several existing Python packages, NumPy, SciPy, and matplotlib, for math and science work. The resulting libraries can be used for interactive “workbench” applications or reused in other software. The kit is available under a BSD license and is fully open and reusable.

Scikit-learn includes tools for many standard machine-learning tasks (clustering, classification, regression, etc.). And since scikit-learn is developed by a large community of developers and machine-learning experts, promising new techniques are included in reasonably short order.

10. Torch: A Scientific Computing Framework for Machine Learning

Torch is a scientific computing framework with broad support for machine learning algorithms that puts GPUs first. Thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation, it is easy to use and efficient.

The goal of Torch is to give you maximum flexibility and speed in building your scientific algorithms while making the process extremely simple. Torch has a large ecosystem of community-driven packages in machine learning, computer vision, signal processing, parallel processing, image, video, audio, and networking. It builds on top of the Lua community.

11. Veles: A Distributed Deep Learning Framework

Veles is a distributed platform for deep-learning applications, and it’s written in C++, although it uses Python to perform automation and coordination between nodes. Datasets can be analyzed and automatically normalized before being fed to the cluster, and a REST API allows the trained model to be used in production immediately.

It focuses on performance and flexibility. It has few hard-coded entities and enables training of all the widely recognized topologies, such as:

• Fully connected nets
• Convolutional nets
• Recurrent nets, etc.

12. MXNet: An Efficient and Scalable Deep Learning Framework

MXNet is an open-source deep learning framework known for its efficiency and scalability. MXNet is efficient for both research and production. It has a growing community and industry support, but its community is smaller compared to TensorFlow and PyTorch.

13. Keras: The Go-To High-Level Neural Network API

Keras is an open-source framework developed on top of TensorFlow. It is written in Python and can efficiently run on GPUs and CPUs. After going through a long research and adaptation phase, Keras became the choice of high-level neural networks.

François Chollet is a Google engineer who designed it to be fast, easy to implement, and modular by nature. ML developers can apply it in different domains like:

• Healthcare
• Corporate insights
• Sales predictions
• Customer support
• Virtual assistants, etc.

14. Theano: A Pioneer in Deep Learning Frameworks

Theano was built on top of NumPy and is one of the fastest ML libraries. It was released under the BSD license and written in Python and CUDA. Developers use it to work with multi-dimensional arrays and allow users to optimize mathematical representations in ML projects.

Even though Theano is comfortable with GPU and CPU systems, it can yield faster results when developers make it work with the former. This machine learning tool can render its tasks 140 times faster when used in GPU architectures. Theano finds extensive applications in finance and logistics projects as a popular machine learning tool.

15. Spark MLLib: Apache Spark's Scalable Machine Learning Library

MLLib is Apache Spark's ML library. This open-source framework offers a programming interface and is familiar to all Python-based ML developers. It focuses on working with Resilient Distributed Dataset (RDD) data structure rather than NumPy arrays.

It is Spark's fundamental data structure. It comprises different learning algorithms, including:

• Regression
• Classification
• Collaborative filtering
• Clustering
• Low-level optimization, etc.

16. LangChain: A Framework for LLM Applications

LangChain has recently gained popularity as a large language model (LLM) application framework. It allows developers to build applications using LLMs with features like:

• Model I/O
• Data connections
• Chains
• Memory
• Agents
• Callbacks

LangChain integrates with various tools, including OpenAI and Hugging Face Transformers, and is used for diverse applications, such as:

• Chatbots
• Document summarization
• API interaction

17. Fast.ai: A Deep Learning Library for Rapid Prototyping

Developing an ML model prototype is time-consuming. As the name suggests, fast.ai makes it faster. The framework's high-level API offers preconfigured algorithms and a well-thought-out structure, thus ensuring faster development of functioning deep learning model prototypes. But that doesn't make the framework any less enjoyable for experts: the low-level API can create sophisticated and finely tuned ML models and optimize them to the smallest detail.

The goal of the framework and the non-profit organization behind it is to “make neural networks uncool again.” This is not to belittle neural networks’ popularity but to broaden the technology’s accessibility beyond the academic elite and experts.

18. XGBoost: The Best Framework for Gradient Boosted Trees

If you work with structured or tabular data, an algorithm based on decision trees should be on your shortlist. XGBoost offers the perfect software and hardware optimization combination to accelerate the Gradient Boosted Trees algorithm. With APIs in Python, Java, C++, Scala, and Julia, the framework supports multiple implementation options for Gradient Boosted Trees and runs on:

• CPUs
• GPUs
• Distributed computing resources

The framework has already impressed in many Kaggle competitions and, thanks to its speed, offers shorter calculation times than normal gradient boosting. The combination of hardware optimization, parallelization, and cloud integration makes the framework ideal for accelerating calculations based on decision trees.

19. Metaflow: A Framework for Managing ML Projects

Developed by Netflix and available as an open-source project since 2019, this framework simplifies various challenges related to scaling and versioning ML projects. Metaflow stores code, data, and dependencies in a content-addressed memory, making it possible to:

• Develop workflows further
• Reproduce old ones
• Edit new ones

By offloading individual steps to separate nodes on AWS, Metaflow makes it easier to scale the ML workflow without worrying about communication between nodes. The framework is beneficial if you plan to manage and execute your ML workflow in a production environment on AWS.

20. Microsoft NLTK: A Comprehensive Natural Language Toolkit

Microsoft Natural Language Toolkit (NLTK) is a comprehensive NLP toolkit developed by Steven Bird and Edward Loper. The library was created to advance research in machine learning, NLP, and related fields, such as computational linguistics and information retrieval.

The framework provides numerous algorithms for natural language text processing tasks like:

• Text classification
• Tagging
• Stemming
• Lemmatization

As a Python-based library, NLTK is open-source and provides extensive documentation that guides users through various machine learning and data science tasks. The framework is available on Windows, Linux, and macOS operating systems. ML developers use the framework to build chat boxes and identify sentiments in reviews and natural language.

Related Reading

• Best AI APIs
• Best Multimodal Models
• RAG Use Cases

Start Building with $10 in Free API Credits Today!

Inference delivers OpenAI-compatible serverless AI inference APIs for top open-source LLM models, offering developers the highest performance at the lowest cost in the market. Beyond standard inference, Inference provides specialized batch processing for large-scale async AI workloads and document extraction capabilities designed explicitly for RAG applications.

Start building with $10 in free API credits and experience state-of-the-art language models that balance cost-efficiency with high performance.

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