Quick AI implementation: popular tools for developers Quick AI implementation: popular tools for developers

As investigated by Forrester, 58% of IT companies are now contemplating the possibilities of artificial intelligence. Nonetheless, only 12% of them are capable of AI inclusion due to the lack of relevant knowledge. The process may be less difficult if special tools are applied making artificial intelligence available to any business.

It is not currently necessary to hire a large team of talents developing innovative models of machine learning from scratch. Cloud platforms and frameworks can be applied for the development of own unique solutions. Almost all of them use an open source processing the most difficult machine learning stages, which is a weight off developers’ shoulders.

Both large and small businesses can introduce AI and ML solutions to manufacturing processes using special tools. Those who want to apply artificial intelligence in practice can take a look at various platforms enabling to create a new ML model. Particularly, tools for chatbots, Big Data analysis, and computer vision.

Chatbot-designing platforms

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A chatbot is an application that conducts a dialog with a user. Chatbots are integrated into messengers (Facebook, Telegram, Viber) and can text you, assist clients in product search, take orders or even carry out money transactions. They are pretty user-friendly programs which functionality may be broadened by artificial intelligence. More complicated chatbots offering paid services and free versions also exist.

Microsoft Bot Framework. A free-of-charge framework that creates chatbots for Skype, Slack, Messenger, and SMS. One doesn’t need to have profound knowledge in programming to use the tool. Microsoft Bot Framework offers special functions (QnA Maker) allowing to launch a chatbot.

Chatfuel. The most popular free framework for Facebook and Telegram bots integration. Such well-known brands and companies as Adidas apply the app. One does not need to write a code, only set rules of communication for a chatbot. Chatfuel has already helped businesses create 360 000 chatbots with 17 m users.

Botkit. This free platform requires programming skills. It also provides both a basic toolset (Botkit Studio) and plug-ins enabling extra functions. Botkit has an open source publicly available on GitHub.

ML frameworks

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Such platforms are used for large tasks like language and image recognition and Big Data processing.

Apache Singa. It is a user-friendly programming model that works across a cluster of machines. The framework supports several machine learning models, particularly, recurrent neural networks and restricted Boltzmann machines. The models can be trained in a consecutive and synchronous way. Apache Singa is run on CPU and GPU clusters.

Microsoft Azure ML Studio. This service leverages ML cloud storage. Microsoft has both paid and free versions, a possibility to use ready-made algorithms (in-house and created by foreign companies). The platform can be tested from an anonymous account. Besides, one may convert the models into APIs and provide other services. The free version offers 10 Gb of storage. One example of the apps developed via this framework is How-Old (recognizes sex and age by an image).

Amazon Machine Learning. One of the company frameworks that connects to cloud data stored in Amazon S3, Redshift, and RDS. The platform runs models of binary and multi-class categorization as well as regression. The service is tooled for Amazon meaning that models are neither exported nor imported.

МL tools with computer vision

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Cloud AutoML Vision. It is a Google service with a visual interface allowing simple creation of image-recognizing models. It doesn’t require specific knowledge of ML algorithms. You can train and manage models directly on Google Cloud. The program leverages transfer learning and search technologies with neural architecture, which makes it high-accuracy. As reported by the developers, one day would be enough to build an effective model.

Caffe2. The second version of a Facebook platform created especially for tasks entailing computer vision. It currently provides other services such as speech recognition and media data processing. The framework was written on C++. Its main advantage is high-speed performance.

Platforms for scientific calculations

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Scikit-learn. It is suitable for math and scientific calculations. The framework has all the tools needed to conduct clustering, classification, and regression. Written on Python, the platform is supported by a large community of developers and experts on machine learning who contribute new methods.


An open-source framework used for scientific calculations. It works with data applied for image recognition, audio processing, and statistical apps creation. The platform is based on C#. Accord.NET is a tested operational code. The framework is often upgraded and uses a number of datasets.

Each of the modern frameworks provides a unique set of functions for working with artificial intelligence. Such tools allow us to get acquainted with AI-based technologies in order to test them and immerse in the topic for the serious work. Everyone can use frameworks: both beginner and experienced programmer.

The main advantages of such platforms are time economy, elimination or minimizing of the most difficult development stages. What should be mentioned, many frameworks are free and use an open source.

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