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A Guide to the Best Machine Learning Frameworks of 2022

In the last few years, several machine learning frameworks emerged to streamline MLOps and the deployment of AI applications. These frameworks help ML teams develop models faster and easier without reinventing the wheel when starting new projects.

With the abundance of machine learning frameworks available today, which one should you use? Each framework has its strengths and weaknesses with varied learning curves, and the right one depends on the project.

What are Machine Learning Frameworks?

Machine learning frameworks are tools, libraries, or interfaces that help ML practitioners develop models faster. Machine learning relies on algorithms, which can be very difficult and time-consuming to understand. ML frameworks simplify and speed up the ML model development without going through all the underlying algorithms. They reduce the complexity of building models by using a set of pre-built and optimized components.

Top Seven Machine Learning Frameworks

Machine learning used to be a daunting endeavor, but training models has become faster and easier thanks to machine learning frameworks. Let’s dive into the seven most popular ml frameworks used today to help you select the best one for your project:

1. Hugging Face

Hugging Face initially started as a chat platform in 2017. They had a conversational AI-run chatbot application to entertain users and developed a natural language processing (NLP) model, Hierarchical Multi-Task Learning (HMTL). Today, Hugging Face has shifted its focus to advancing and democratizing NLP for everyone. They have a large open-source community and an NLP library that provides various resources like transformers, datasets, tokenizers, and more.

Transformers is Hugging Face’s NLP library and is its most popular one. It provides thousands of pre-trained models for translation, text classification, text generation, question and answer, information retrieval, and summarization.

2. TensorFlow

Tensorflow almost always emerges as the best machine learning framework on every ML resource page. It’s one of the most popular frameworks today because of its versatility, scalability, speed, and flexibility.

TensorFlow is an open-source library for numerical computation using data flow graphs. Dataflow graphs are structures that show how data flows across processing nodes inside a graph. Each node in the graph represents a mathematical operation, and each edge between nodes is a multidimensional data array or tensor.

You can leverage TensorFlow’s extensive library of pre-trained models for your applications and use the library to build regression models, classification models, neural networks, and more. TensorFlow offers an easy front-end API for developing apps using Python or JavaScript and then executes such applications in high-performance C++. However, Tensorflow can be very complex to use and challenging for beginners.

Developed at Google Brain Team in 2015, it soon gained popularity among researchers worldwide and became an open-source project in November 2017. However, it is still in its early stages compared to other frameworks like scikit-learn, which have been around for several years.

3. Scikit-learn

Most ML practitioners think of scikit-learn when using Python for AI and machine learning. David Cournapeu developed it in 2007, and it is one of the oldest machine learning frameworks. If you’re a beginner, scikit-learn is an excellent framework for Python developers looking to master the basics of machine learning. It makes it easy to execute popular algorithms like decision trees, random forests, support vector machines, logistic regression, K closest neighbors, and linear regression.

Scikit-learn is built on top of several popular Python packages: NumPy, SciPy, and matplotlib. This ML framework allows you to build machine learning models and provides other functions like data pre-processing cross-validation and hyperparameter tuning.

ML practitioners often begin an ML project in Scikit-learn and switch to another framework afterward. You can use Scikit-learn for pre-processing data and then move to another framework for the next steps. The same is true with other ML frameworks—you can use one for early-stage development and then switch to another framework later.

4. PyTorch

PyTorch, a relatively new deep learning framework, is swiftly gaining popularity among academics because of its ease of use when dealing with complex tasks. It supports dynamic computation graphs, making it attractive for ML teams working with natural language processing (NLP) data and time series. PyTorch is also easier to learn compared to other deep learning frameworks.

Like TensorFlow, PyTorch does regression, classification, neural networks, etc. It also runs on both CPUs and GPUs. The Facebook AI Research (FAIR) team created PyTorch to address the adoption challenges of its predecessor library, Torch. Lua, Torch’s programming language, is powerful but is not popular among machine learning practitioners.

5. Keras

Keras is a high-level framework built on top of TensorFlow. It is written in Python and can efficiently run on GPUs and CPUs. Keras is known for its modularity, usability, and extensibility. It doesn’t handle low-level computations but hands them off to a back-end engine. Keras allows you to switch between different back-ends, such as Tensorflow, PlaidML, CNTK, Theano, and MXNet.

Because Keras provides a python front-end and offers multiple choices of back-ends for computation purposes, Keras is comparatively simple to learn and use. The only takeaway is that this feature makes Keras slower than other frameworks but well-suited for beginners.

When working with small datasets, rapid prototyping, and multiple back-end support, Keras is the preferred framework of choice.

6. Caffe

CAFFE (stylized as Caffe) stands for Convolutional Architecture for Fast Feature Embedding and is another framework written in C++. The Berkeley AI Research group and the Berkeley Vision and Learning Center developed Caffe, popularly known for visual recognition. Their website states, “Caffe was made with expression, speed, and modularity in mind.”

Regarding speed, Caffe can work through over 60M images per day with a single NVIDIA K40 GPU, which translates to one millisecond per image per inference and four milliseconds for learning. With more recent libraries, it is still getting faster than ever. Speed has always been Caffe’s most significant selling point.

Caffe has extensive community support making it easy for machine learning practitioners to find answers to questions on user forums. Over 1000 developers have forked Caffe in its first year, and many have contributed significant changes to the project.

7. XGBoost

Extreme Gradient Boosting, better known as XGBoost, was first developed by Tianqi Chen as a research project. It gained widespread recognition in ML competitions after using it as a winning solution in the Higgs Machine Learning Challenge.

Its documentation states, “XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible, and portable.” It uses the Gradient Boosting framework to implement machine learning algorithms. You can download and install XGBoost on your machine and then access it from various interfaces, including C++, CLI, Julia, Python, Scala, and R. Although it offers a slew of advanced features, this library is laser-focused on computational speed and model performance.

Choosing the Right ML Framework

There’s no shortage of machine learning frameworks today, so choosing the best one can be challenging. Each framework has a unique set of functionalities for every ML practitioner. Since many machine learning libraries are available, select one that fits your project’s requirements and goals. There’s no single best framework, only a framework that suits your needs best.

Comet’s machine learning platform integrates with many of the popular deep learning and ML frameworks that data science teams use today, including this list. Try Comet for free and get started using your favorite machine learning framework.

Team Comet

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