The 23 Most Effective Tools for Machine Learning Teams

With the tremendous increase in the adoption of artificial intelligence (AI) and machine learning, the number of software tools available to developers for use in this area is growing. Familiarity with various AI frameworks and APIs will allow machine learning teams faster and more qualitative create their projects and apps. Therefore, this material provides the most effective tools for machine learning Teams and everything related to them.

  • Microsoft’s machine learning tools

In 2017, at the Microsoft Ignite conference in Orlando, Florida, the corporation launched three new machine learning tools that are popular today:

  • Azure Machine Learning Experimentation service

Microsoft Azure Machine Learning is a cloud computing infrastructure. In the context of machine learning, the tool is a platform that allows regardless of the quality of data to build solutions directly “in the cloud” and easily create BI applications based on them.

Tools above were created for developers of their own artificial intelligence and users of existing models. To provide universal access to the new technology, Microsoft offers:

  • Custom Speech Service

So far, Microsoft’s portfolio of innovative products includes about 25 tools that can be used to develop emotion, image, and speech recognition and language understanding functions. For non-developers, the corporation also has a tool to use the functionality of artificial intelligence within Excel spreadsheets.

  • Ai-one

Ai-one gives developers the ability to create intelligent assistants within most applications. The impetus for Ai-one development was “biological intelligence”. Ai-one is the best smart assistant for artificial intelligence developers.

The Analyst Toolbox contains a document library, a toolkit for constructing intelligent agents, and an API. Ai-one can transform data into a generalized set of rules favorable to most frameworks of artificial intelligence and deep machine learning.

  • Protege

Protege is used for developing business applications. At the same time, Protege has an open-source toolkit that is ideal for developing “knowledge-based applications with ontology elements”.

The target audience of Protege is artificial intelligence experts. It allows to create, upload, modify and share applications. There is an active community within Protege that ensures an optimal level of collaboration and facilitates problem-solving. Protege is the best tool for knowledge-based programs.

  • IBM Watson

IBM is one of the leading players in artificial intelligence. IBM’s Watson platform contains a large number of tools for developers and business users.

Watson is presented as a set of open-source APIs. Watson users have access to sample codes, a starter set of tools, and can create cognitive search engines and virtual agents.

Watson has a platform for creating chatbots that can be used by machine learning experts. For faster bot training, Watson offers pre-training content for chatbots. IBM platform has many effective tools for machine learning teams.

  • DiffBlue

DiffBlue was created by Daniel Kroening of Oxford University. It is a simple and extremely useful tool for implementing coding automation. DiffBlue automates processes such as bug localization, code refactoring, and test writing as well as identifying and fixing weaknesses in the code. DiffBlue is an indispensable coding automation tool for artificial intelligence developers.

  • Google’s TensorFlow

TensorFlow is an open-source software platform designed specifically for machine learning projects.

TensorFlow relies on a library of numerical calculations that use data flow graphs. It allows developers to deploy deep learning technology over multiple central processing units (CPUs) on mobile and desktop devices.

TensorFlow contains a wealth of documentation, training materials, and online resources. It can provide essential support for machine learning teams. TensorFlow is one of the best tools for machine learning projects.

  • Nervana Neon

Nervana Neon is a joint product of Nervana and Intel. It is an open-source machine learning library based on the Python language. Formed in 2004, Neon provides developers with the ability to build, train and deploy deep learning technologies in the cloud. Nervana Neon is the next generation of intelligent agents and applications.

  • OpenNN

OpenNN is a C++ programming library that was used for the development of a famous VPN for Netflix. This programming library is designed for experienced developers who create neural networks. The target audience of OpenNN is professional developers with experience in artificial intelligence and machine learning. The library has a large number of documents and training materials for understanding the world of neural networks.

OpenNN has also created a tool for advanced analytics, Neural Designer, which allows you to simplify and interpret data input by creating visual content in the form of charts and tables.

  • Apache Spark MLlib

Apache Spark MLlib is an in-memory data processing framework that has a large database of algorithms with an emphasis on classification, clustering, and collaborative filtering.

Inside the Apache incubator is an open-source framework called Singa, which is a software tool for deep learning networks. Apache Spark MLlib offers a large base of algorithms.

  • Caffe

Caffe offers an image-based automatic inspector tool that is created by Berkeley Vision and Learning Center (BVLC) in conjunction with the developer community. Caffe is a C++ library for deep learning algorithms, which was developed (and still is developed by different contributors) with a focus on such important features as data purity, readability, and processing speed.

Open-source code, Python support, and integration with MATLAB, as well as high speed, allowed Caffe to find a wide application, including a commercial environment. Of course, not every implementation is both fast and fairly stable, but if you solve the dilemma of “speed vs reliability” in favor of speed, the library is unrivaled.

The framework is already used as part of scientific research, in prototype startups, and even in industrial applications for computer vision and speech recognition. Notable users of Caffe are Facebook and Pinterest.

  • Veles

Veles is part of Samsung’s distributed deep learning platform. It is written in C++ and uses the Python language to coordinate nodes. The proposed API is ready-to-use, contains trained models, and can be used for data analysis. Veles is one of the best out-of-the-box APIs.

  • Apache Mahout

Mahout is designed for professionals who would like to develop scalable machine learning applications. Mahout gives developers the ability to use pre-formed algorithms for Apache Spark, H20, and Apache Flink.

This open-source tool is mainly focused on collaborative filtering, clustering, and classification algorithms — and while Hadoop implements them using map/reduce technology, no one restricts third-party developers: on the contrary, the foundation actively encourages you to create algorithms without being tied to Hadoop. Apache Mahout is a very necessary tool for application scaling.

  • Microsoft Azure

The biggest bonus of the Azure machine learning tool is the “Azure Machine Learning Studio” platform that allows developers without deep machine learning experience to drag-and-drop datasets and offload predictive analytics.

Microsoft also offers Cortana Intelligence, a tool to fully manage big data and analytics and transform data into meaningful information.

  • Jupyter Notebook

It is an easy-to-use, free interactive web environment. Besides Python, Jupyter Notebook supports more than 40 programming languages. It’s great for experimenting with new ideas on the fly, writing documentation, and creating analytical reports. It reminds an IDE but falls short of it in terms of functionality, even if it’s quite broad.

Among machine learning tools, Jupyter is good because of the fast analysis, modeling, and visualization of data. Results can be exported to a variety of formats, including the commonly used PDF and HTML. Jupyter Notebook is great for interactive modeling.

  • Kaggle: The Data Science Community

Kaggle also provides an interactive development environment. The difference between Kaggle and Jupyter is that just one click separates machine learning specialists from the great Data Science and machine learning community. Here you can find ready-made datasets, models, and even program code to solve different problems.

  • Infosys Nia

Infosys Nia is an intelligent AI platform developed by Infosys in 2017 to collect and summarize organizational data from people, processes, and legacy systems into a self-learning knowledge base. The tool is designed to solve complex problems such as forecasting revenue and what products to build. Infosys Nia greatly eases machine learning teams’ work.

  • Accord.NET Framework

Accord.NET Framework is a machine learning framework that is combined with audio and image processing libraries written in C#. The framework is needed for pattern recognition, computer vision, computer audio analysis, and signal processing. The Accord.NET Framework is divided into several libraries. These include scientific computing, signal and image processing, and other libraries such as natural learning algorithms, real-time face recognition, etc.

Accord.NET Framework is constantly updated and maintained by an active development team. It is extremely easy to work with algorithms and signal processing in Accord.NET Framework. Moreover, it easily handles numerical optimization and artificial neural networks.

  • H2O

H2O is an open-source software tool embedded in a computer platform for enterprises and developers. It was developed by H2O.ai and is written in Java, Python and R. The platform is built using languages that developers are familiar with to facilitate their use of machine learning and intelligent analytics. H2O can be used to analyze data sets on Apache Hadoop cloud and file systems. It is available for Linux, MacOS, and Microsoft Windows.

  • PredictionIO

Apache PredictionIO is a machine learning server built on top of an open-source stack for developers and data analysts to create intelligent engines for any machine learning task. It consists of three main components. First is the PredictionIO platform. It is an open-source stack for building, evaluating, and deploying engines with machine learning algorithms. The second is Event Server. It is a learning analytics layer for combining events from multiple platforms. The third is Template Gallery. It is a place for downloading engine templates for different types of machine learning applications.

  • Eclipse Deeplearning4j

Eclipse Deeplearning4j is an open-source deep-learning library for the Java virtual machine. It can serve as a DIY tool for Java, Scala, and Clojure programmers working with Hadoop and other file systems. It allows developers to configure deep neural networks and is designed for use in business environments on distributed GPUs. The project was created by a San Francisco-based company called Skymind.

  • RapidMiner

RapidMiner is almost the most famous data analysis tool which used to be called YALE (Yet Another Learning Environment). The software was developed just for machine learning, so today many conservative users are well aware of its benefits. Among them is a convenient graphical interface with a drag-and-drop function of data flows.

The RapidMiner Extensions platform also allows individual developers to contribute to the development of the environment by uploading and exposing new algorithms to the public.

  • OpenCV

OpenCV (Open Source Computer Vision) is another C/C++ open-source library. OpenCV offers a wide range of tools for working with visualization including for machine learning purposes. It can be used freely for academic and commercial purposes because it is distributed under the BSD license. Today it works with most operating systems including mobile iOS and Android.

  • Theano

Theano is a very effective tool for machine learning teams. By using graphics processing units (GPUs) instead of central processing units (CPUs), Theano-based artificial intelligence models achieve high accuracy in computational operations that require a lot of computational power. Theano can provide high performance for multidimensional data sets computation.

Theano is based on the Python programming language, which has long proven itself in tasks that require fast processing and response. With Theano library, the expression evaluation process is faster, due to dynamic code generation which significantly increases effectiveness. Moreover, the framework also provides excellent accuracy, even with minimal values.

Conclusion

The demand for new competencies in artificial intelligence and machine learning technologies is growing. Gaining new knowledge about different artificial intelligence platforms and APIs for developers will expand and deepen their technical skills as well as help to work more effectively.

Above I described the 23 most effective tools for machine learning teams. All of them have great functionality and noticeably speed up work on ML and AI projects. I recommend trying all the above tools, as their number is constantly increasing. The field of machine learning is actively developing, which generates more and more tools for machine learning teams.

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