The one ‘advantage’ of the COVID pandemic is the upsurge in digitization. Companies and businesses turn to the latest technology to enable smooth remote operations.
Following the rise in demand for innovation is an explosion in developer platforms and tools, particularly for machine learning and artificial intelligence.
Machine learning frameworks are for developers to create AI-powered software without the need to code from scratch. They help speed up development without limiting the accuracy and efficiency of the software.
Here we’ll look at some of the best machine learning frameworks for developers to use in 2021 for mobile apps or other software.
In 2018, the Facebook AI Research team combined Caffe2 and Torch framework to create PyTorch. It offers a range of optimizing algorithms, stability, and flexibility for modular research and production deployment.
The framework is open-source and compatible with cloud-based software development. It comes with various built-in features, including libraries and tools, and supports Lua programming language for the user interface.
PyTorch is best for its hybrid front-end development and designing computational graphs. It also offers a relatively shorter learning curve for Python developers, making it ideal for beginners and those new to the world of machine learning and AI.
Launched in 2007, TensorFlow has grown to be one of the more popular ML frameworks among developers and researchers. It is an open-source platform with extensive features and APIs to build and train ML models.
It is a versatile tool with a massive library for programming, regression models, classifications, and neural networks. The framework is also compatible with multiple devices and platforms.
TensorFlow can run on a standard CPU and accelerators like GPU and TPU and is available for platforms, including Windows, Linux, Mac OS, Android, and iOS. Plus, ML models trained using TensorFlow are compatible with browsers, desktops, microcontrollers, and edge computing devices. Its diverse support range makes the framework unique and production-ready.
Another ML framework in the list that supports Python programming language is SciKit Learn. It is among the best machine learning tools, particularly for data mining and analysis. Developers can perform an array of ML tasks, including:
- Decision Tree regressions
- Linear regression
- Random Forest regressions
- Stochastic Gradient Descent Models
- K-Nearest neighbor
- And more.
The framework also supports designing models and algorithms for classifications, dimensional reduction and model selection, pre-processing, and clustering.
Plus, as an open-source platform, it comes with detailed documentation that makes it beginner-friendly, allowing even amateur developers to create machine learning software.
Unlike other machine learning frameworks, Keras is specific to experimenting with neural networks. It is an open-source neural network library written in Python and compatible with TensorFlow, Theano, Microsoft Cognitive Toolkit (CNTK), R, and PaidML.
It makes ML modeling straightforward by simplifying part of the coding. It is user-friendly, simple, flexible, and modular, which makes it ideal for amateur developers.
However, since Keras relies on other deep learning frameworks, it should be considered a complementary option rather than an alternative.
The framework is a suite of comprehensive and flexible APIs for professionals with different levels of experience and requirements. It can run on both CPUs and GPUs for training and interference.
Machine learning frameworks open the gate to automated systems, which puts everyday, mundane tasks into the ‘hands’ of AI and frees up employees for priority work. With the diverse array of opportunities available, many businesses are eager to invest in the development of ML and AI and creating custom software for their company.
Your choice of ML framework depends on the purpose of the software, algorithm requirements, and developer expertise. So, consult with companies like Logiciel Services before you dive into working with machine learning and AI.
I hope the blog helped in understanding some of the best machine learning frameworks available.