Machine learning engineers are part of the team that develops products and builds algorithms, and ensures that they work reliably, quickly, and at scale. They work closely with data scientists to understand theoretical knowledge and industry applications. The key differences between data specialists and machine learning engineers are:

· Machine learning engineers build, develop and maintain products for machine learning systems.

· Data experts conduct research to form ideas about machine learning projects and then analyze them to understand the measurement impact of machine learning systems.

Here is a framework for machine learning:

1.Apache Singa is a generic distributed deep learning platform for training depth learning on large data sets. It is designed based on a simple development model of layered abstraction. It also supports a variety of current popular deep learning models, including feedforward models (convolutional neural network, CNN), energy models (constrained Boltzmann machine, RBM and recurrent neural network, RNN), and provides many embedded layers for users.

2.Amazon Machine Learning (AML) is a service that is easily accessible to developers at all levels of Machine Learning technology. It provides visual tools and wizards to guide you through building Machine Learning without having to learn complex Machine Learning algorithms and techniques.

3.Azure ML Studio allows Microsoft Azure users to create and train models, and then turn those models into apis that can be used by other services. Although you can link your own Azure storage to the services of the larger model, the storage capacity of each account model data cannot exceed 10GB. There are plenty of algorithms available in Azure, thanks to Microsoft and a few third parties. You don’t even need to register an account to log in anonymously and use Azure ML Studio for up to eight hours.

Caffe is a deep learning framework based on the BSD-2-protocol developed by the Berkeley Center for Visual Learning (BLVC) and community contributors.Caffe adheres to the development philosophy of “presentation, Efficiency, and modularity.” Model and combinatorial optimization is implemented through configuration rather than hard coding, and users can switch between CPU and GPU processing as needed. Caffe’s high efficiency makes it perfect for experimental research and industrial deployment. Over 60 million images per day can be processed using a single NVIDIA K40 GPU processor.

5.H2O makes it easy to apply mathematics and predictive analytics to solve today’s challenging business problems. It skillfully combines unique features not currently used in other machine learning platforms: best open source technology, easy-to-use WebUI and familiar interfaces, support for common databases and different file types. With H2O, you can use existing languages and tools. It also scales seamlessly into the Hadoop environment.

6.Massive Online Analysis (MOA) is currently the most popular open source framework for data stream mining with a very active community. It contains a series of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommendation systems) and evaluation tools. Like the WEKA project, MOA is written in Java, but more extensible.

MLlib (Spark) is the machine learning library of Apache Spark. It aims to make machine learning scalable and easy to operate. It consists of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as low-level optimization native language and high-level pipeline API.

8.Mlpack is a C++ based basic learning library that was first released in 2011 and was designed with “scalability, efficiency and ease of use” in mind, according to the library’s developers. There are two ways to execute Mlpack: by quickly handling the cache of simple “black box” operations executed on the command line, or by using C++ apis to handle more complex work. Mlpack provides simple command-line programs and C++ classes that can be integrated into large machine learning solutions.

9.Pattern is a Web mining component of Python programming language, including data mining tools (Google, Twitter, Wikipedia API, web crawler, HTML DOM parser), natural language processing (part-of-speech tagging, N-gram search, Sentiment analysis, WordNet interface), machine learning (vector space modeling, clustering, support vector machines), Network analysis and < Canvas > visualization.

Scikit-learn expands the use of Python based on several existing Python packages (Numpy, SciPy, and Matplotlib) for mathematical and scientific work. The resulting library can be used for interactive workbench applications or embedded into other software for reuse. The toolkit is based on the BSD protocol and is completely free, open source and reusable. Scikit-learn includes a variety of tools for machine learning tasks such as clustering, classification, and regression. Scikit-learn is developed by a large community of developers and machine learning experts, so the cutting-edge technologies in SciKit-Learn tend to be developed in a very short period of time.

11.Shogu is one of the earliest machine learning libraries. It was created in 1999 and developed in C++, but not limited to C++ environment. With the SWIG library, Shogun works in a variety of languages such as Java, Python, c#, Ruby, R, Lua, Octave and Mablab. Shogun is designed for unified large-scale learning, such as classification, regression, or exploratory data analysis, for a wide range of specific types and learning configurations.

12.TensorFlow is an open source software library that uses data flow diagrams for numerical operations. It implements data flow diagrams, in which tensors (” Sors “) can be processed by a series of graphically described algorithms. Data streams can be encoded in C++ or Python to run on a CPU or GPU device.

13.Theano is a definable, optimizable, and numerically computable Phython library released based on the BSD protocol. Theano is also an algorithm that supports efficient machine learning and can be achieved at speeds comparable to big data processing achieved with C.

14.Torch is a scientific computing framework that broadly supports GPU-first machine learning algorithms. The framework is easy to use and efficient due to the simple and fast scripting language LuaJIT and the underlying C/CUDA implementation. Torch aims to let you build your own scientific algorithms with minimal process, maximum flexibility and speed. Torch was developed based on Lua and has a large ecosystem of community-driven library packages designed for machine learning, computer vision, signal processing, parallel processing, graphics, video, audio and networking.

15.Veles is a distributed platform for deep learning applications developed in C++, but it leverages Python to automate and collaborate tasks between nodes. Data can be analyzed and automatically standardized before relevant data is centralized into the cluster, and the REST API allows trained models to be immediately added to the production environment, with a focus on performance and flexibility. Veles has almost no hard coding and can train all widely recognized network topologies, such as full convolutional neural networks, convolutional neural networks, recurrent neural networks, etc.


Tell us more about your favorite machine learning frameworks in the comments.


DevendraDesale (@devendradesale) is a data science graduate student currently working on text mining and big data technologies. He is also interested in enterprise architecture and data driving. In his spare time, he enjoys parties and adventures in the unknown.


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This article is recommended by Beijing Post @ Love coco – Love life teacher, translated by Ali Yunqi Community organization.

Abstract: This article was originally titled “Top 15 Frameworks for Machine Learning Experts”.

The article is a brief translation. For more details, please refer to the original text