by Satavisa Pati
April 30, 2022
These open-source AI technologies are playing major roles in ML projects in 2022Artificial intelligence (AI) technologies are quickly transforming almost every sphere of our lives. From how we communicate to the means we use for transportation; we seem to be getting increasingly addicted to them. Because of these rapid advancements, massive amounts of talent and resources are dedicated to accelerating the growth of the technologies. Here are the top 10 open-source AI technologies powering ML projects in 2022.TensorFlowTensorFlow is an open-source machine learning framework that is easy to use and deploy across a variety of platforms. It is one of the most well-maintained and extensively used frameworks for machine learning. Created by Google for supporting its research and production objectives, TensorFlow is now widely used by several companies, including Dropbox, eBay, Intel, Twitter, and Uber. TensorFlow is available in Python, C++, Haskell, Java, Go, Rust, and most recently, JavaScript. You can also find third-party packages for other programming languages.KerasKeras is an open-source software library designed to simplify the creation of deep learning models. It is written in Python and can be deployed on top of other AI technologies such as TensorFlow, Microsoft Cognitive Toolkit (CNTK), and Theano. Keras is known for its user-friendliness, modularity, and ease of extensibility. It is suitable if you need a machine learning library that allows for easy and fast prototyping, supports both convolutional and recurrent networks, and runs optimally on both CPUs (central processing units) and GPUs (graphics processing units).Scikit-learnscikit-learn is an open-source library developed for machine learning. This traditional framework is written in Python and features several machine learning models including classification, regression, clustering, and dimensionality reduction. Scikit-learn is designed on three other open-source projects—Matplotlib, NumPy, and SciPy—and it focuses on data mining and data analysis.Microsoft Cognitive ToolkitMicrosoft Cognitive Toolkit (previously referred to as CNTK), is an AI solution that can empower you to take your ML projects to the next level. Microsoft says that the open-source framework is capable of “training deep learning algorithms to function like the human brain.” Some of the vital features of the Microsoft Cognitive Toolkit include highly optimized components capable of handling data from Python, C++, or BrainScript, the ability to provide efficient resource usage, ease of integration with Microsoft Azure, and interoperation with NumPy.TheanoTheano is an open-source Python library that allows you to easily fashion various machine learning models. Since it’s one of the oldest libraries, it is regarded as an industry standard that has inspired developments in deep learning. At its core, it enables you to simplify the process of defining, optimizing, and assessing mathematical expressions. Theano is capable of taking your structures and transforming them into very efficient code that integrates with NumPy, efficient native libraries such as BLAS, and native code (C++).CaffeCaffe (Convolutional Architecture for Fast Feature Embedding) is a machine learning framework that focuses on expressiveness, speed, and modularity. The open-source framework is written in C++ and comes with a Python interface. Caffe’s main features include an expressive architecture that inspires innovation, extensive code that facilitates active development, a fast performance that accelerates industry deployment, and a vibrant community that stimulates growth.TorchTorch is a machine learning library that offers a wide array of algorithms for deep learning. The open-source framework provides you with optimized flexibility and speed when handling machine learning projects—without causing unnecessary complexities in the process. It is written using the scripting language Lua and comes with an underlying C implementation. Some of Torch’s key features include N-dimensional arrays, linear algebra routines, numeric optimization routines, efficient GPU support, and support for iOS and Android platforms.Accord.NETInitially released in 2010, Accord.NET is a machine learning framework entirely written in C#. The open-source framework is suitable for production-grade scientific computing. With its extensive range of libraries, you can build various applications in artificial neural networks, statistical data processing, image processing, and many others.Acumos AIAcumos AI is a relatively new entrant in the open-source AI software segment – but it is backed by industry leaders AT&T and TechMahindra. The two companies wanted to buck the trend of tech giants like Microsoft, Google, and Apple leading open source innovations and make AI available for commercial deployments. That’s how they introduced Acumos AI, a design studio based on Linux, to help integrate other frameworks and develop cloud-based AI apps.ClearMLOverview: ClearML is the result of the recent rebranding of Allegro AI, a provider of open-source tools for data scientists and machine learning labs. Along with the rebranding, ClearML announced a free hosted plan to give data scientists the freedom to manage AI/ML experiments and orchestrate workloads without investing in additional resources. ClearML can be leveraged as an MLOps solution, ready for implementation via just two lines of code.Share This Article
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Source: https://www.analyticsinsight.net/top-10-open-source-ai-technologies-powering-ml-projects-in-2022/
