Machine Learning Frameworks (ML) for Experts in 2023

A library, interface, or other tool that makes it easier and faster for developers to create ML models is called a machine learning framework. The developers don’t have to learn the ins and outs of the intricate algorithms that work behind the scenes to do this. It uses a library of pre-built and optimized components to provide a simple and direct approach to creating machine learning models. It increases the efficiency of the development process by saving programmers from having to start from scratch every time they create a new ML application. Many similar libraries that are already in use inside machine-learning frameworks facilitate the construction of machine-learning models

The activities involved in the machine learning life cycle include;

• Data Engineering
• Model development
• Hyperparameter tuning
• Testing
• Logging
• Monitoring 
• Deployment.

Here are some of the most popular and widely used machine learning frameworks, each with its own strengths:

TensorFlow:

Developed by Google’s Brain Team, TensorFlow is one of the most popular and widely adopted deep learning frameworks. It offers a wide range of tools for developing machine learning and deep learning models, including the high-level Keras API.

Key features:

• Based on JavaScript
• Open source and has extensive APIs
• Can be used via script tags or via installation through npm
• Runs on CPUs and GPUs
• Extremely popular and has lots of community support

PyTorch:

Developed by Facebook’s AI Research lab, PyTorch is known for its dynamic computation graph, which is particularly helpful for research and development. It is highly favored by the research community and has gained popularity in recent years.

Key Features:

• Supports cloud-based software development
• Suitable for designing neural networks and Natural Language Processing
• Used by Meta and IBM
• Good for designing computational graphs
• Compatible with Numba and Cython

H2O:

An open-source machine learning framework called H2O was created to assist businesses with their decision support system procedures. H2O is extensively used in customer intelligence, insurance customer analytics, risk and fraud propensity analytics, patient analytics in healthcare, and advertising spending and ROI.

There have been three iterations of H2O; the most recent is H2O-3. This is an in-memory platform intended for scalable, distributed machine learning.

Key Features:

• Developed decision support system processes
• Prevalent in the risk and fraud propensity analytics and patient analytics in healthcare
• H2O-3 is compatible with Java, JSON, R, Python, Scala, and the Flow notebook
• Integrates with Hadoop and Spark

Scikit-Learn:

Scikit-Learn is an excellent choice for traditional machine-learning tasks. It provides a simple and consistent interface for a wide range of machine-learning algorithms, including classification, regression, clustering, and more.

With a rich library dedicated to the Python programming language, Scikit-learn specifically facilitates Python development work. It is regarded by many users as one of the top ML frameworks for data mining and analysis. Model selection, pre-processing, regression, clustering, dimensional reduction, classification method, and model creation are all supported by Scikit-learn.

Key Features:

• Works well with Python
• The top framework for data mining and data analysis
• Open-source and free

CNTK:

Neural networks are represented as a series of directed graphs-based computational development steps by Microsoft-owned CNTK. It is an open-source machine learning framework made with production readers and C++ programming language algorithms. Large-scale, multi-dimensional, or sparse data sets from C++, Python, and BrainScript are a perfect fit for CNTK. It enables developers to combine and visualize many types of machine learning models, such as convolutional neural networks, feed-forward deep neural networks, and recurrent networks.

Key Features:

• Written in C++
• Suitable for large-scale, multi-dimensional, or sparse data sets
• Supports Python and BrainScript

Apache MXnet:

Amazon chose to use Apache MXNet as the foundational machine learning tool for AWS. It is scalable across multiple GPUs and servers and is distributed over a cloud infrastructure through the use of a parameter server. 

Multiple language APIs are supported by MXNet, including those for Python, JavaScript, Julia, C++, Scala, and Perl. Many IT businesses, including Microsoft, Intel, and Baidu, embrace Apache MXNet. Prominent research and academic establishments such as MIT and the University of Washington also heavily utilize Apache MXNet.

Light GBN:

For experts dealing with gradient boosting, LightGBM is a high-performance framework known for its speed and efficiency. It excels in tabular data competitions and structured data problems. Its ability to handle large datasets and its scalability makes it a popular choice among experts looking for a competitive edge in machine learning competitions.

Key Features

Adopted by Amazon for AWS

Supports multiple languages, including Python, JavaScript, Julia, C++, Scala, and Perl

Microsoft, Intel, and Baidu also support Apache MXNet

Also used by the University of Washington and MIT

Apple Core ML

Graduates and self-taught developers will find Core ML to be very beginning-friendly, as it was primarily created for macOS, iOS, and TVOS applications.

With its extensive feature set, Core ML offers many services like object tracking, natural language processing, image and phrase categorization, barcode identification, and a gameplay kit. It can deliver exceptional performance by utilizing both CPUs and GPUs because of its distinctive low-level tech stack. In terms of usability and security, Core ML guarantees user privacy and the app’s operation even while it is offline.

Key Features

• Developed for macOS, iOS, and TVOS applications
• Beginner friendly
• Supports both CPUs and GPUs

Dask-ML 

As experts tackle machine learning tasks that involve big data, Dask-ML comes to the rescue. Dask-ML provides parallel and distributed computing capabilities, allowing experts to scale their machine-learning workflows seamlessly. It’s particularly well-suited for handling large datasets and training models on distributed clusters.

Final Words

In 2023, machine learning experts have a plethora of frameworks and tools at their disposal. The choice of the best framework depends on the specific requirements of the project, the level of expertise, and the need for performance and scalability. Many companies are now offering AI development services to help businesses integrate artificial intelligence into their processes and products.

Whether you’re delving into deep learning, working on traditional machine learning problems, or dealing with large-scale data, these top machine learning frameworks offer the power and flexibility needed to tackle the challenges of a data-driven world. The key to success for experts lies in selecting the right tool for the job and staying up-to-date with the ever-evolving landscape of machine learning frameworks.