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Top 5 AI Development Tools in 2019

11.05.19
Helen Kovalenko

Artificial intelligence has ceased to be a fantastic story. In the current reality, we use its capabilities every day, sometimes without even realizing it. The best technological minds are working to make this innovation even more advanced and fully understand its mission – to make people’s lives more convenient and safe.

Ordinary users get this opportunity in the form of a ready-made application on their smartphone or another device. However, we know that tremendous work has been done for this. To facilitate the task of AI development, below, we have collected five of the most convenient and effective tools that AI developers should use in 2019.

Google Cloud ML Engine

Google Cloud ML Engine

Credit: Google Cloud

This is an almost universal platform, the task of which is to teach machine learning models to perform various tasks. Its functionality is suitable for deep learning, as well as training in predictive skills. Both of these tasks can be implemented both separately or simultaneously.

A distinctive feature of this software is that it makes it possible to configure a hyperparameter that determines the accuracy of predictions. Without this function, developers would have to test several values and determine the accuracy of the results themselves.

As for the machine learning models themselves, a set of Python-based tools is used to create them. But the main value of this software is ready-made predictive models. They allow you to get the results of predictions in two ways. This is online and batch forecasting. In the first case, the model analyzes a single set of data, and in the second case, the task is complicated by the analysis of all available information and the formation of results based on them.

Take a look at this video to find out more about how AI algorithms are becoming capable of making accurate predictions.

Amazon Machine Learning (AML)

This is powerful, but at the same time, easy-to-learn technology that is ideal for both professional developers and novice programmers. Like the previous tool, AML allows you to create machine learning models and generate predictions.

This technology provides opportunities for visualization, allows you to create three classification models (multi-class classification, binary classification, and regression). The instrument will enable you to quickly deploy neural networks, as well as train them online. The specificity of the technology is that AML is directly integrated with the Amazon Web Service, so it can be difficult and unusual to use this tool for those who have not encountered Amazon solutions before.

Google ML Kit for Mobile

This technology is ideal for creating Android applications based on artificial intelligence. The main features that you can create and build into the application with its help are face and voice recognition, barcode scanning, and recognition of other signs and symbols.

The creators of the technology claim that building applications that use AI have never been as intuitive and straightforward as it can be using the Google ML Kit for Mobile. It’s realistic to use ready-made solutions out of the box, plus add a couple of lines of your code to make the application’s functionality work as it was intended.

Apple’s Core ML

Apple’s Core ML

Credit: Apple Developer

This is a framework that allows you to integrate machine learning models into your applications created for iOS. In other words, it is a machine learning environment that will enable you to perform fast real-time forecasting.

It is straightforward to work with this technology – it uses a machine learning model that is pre-trained in the cloud, then converted to Core ML format and added directly to your project.

Also, this framework supports the use of other technologies that will be extremely useful when creating applications for Apple. This is the Vision framework that allows you to recognize faces and images, Natural Language Processing for the most accurate speech recognition and name recognition, as well as GameplayKit, which will enable you to embed random number generation algorithms, and in-game search for possible solutions.

Also, Apple’s Core ML works excellent with other Apple machine learning systems.

Azure Machine Learning Studio

Azure Machine Learning

Credit: MSDN Blogs – Microsoft

This is an intuitive and interactive platform, with the help of which all the possible tasks of creating and training artificial intelligence systems met in one place. Developers get a huge library of algorithms that they can modify, customize, and adapt to their needs.

Models created using this platform can be released on the Internet to receive new data, and the process of creating the algorithm also occurs online without the need to download or install the software. Using Azure Machine Learning Studio, developers also gain access to APIs that they can implement in their model for improved cognitive algorithms and transformative artificial intelligence. The platform provides the following features.

  • Drag-and-Drop Function
  • Datasets
  • Modules
  • Trained Models
  • Experiments
  • Experiment Conversion
  • Web Service Publishing
  • APIs

So, we examined the five most universal programs for creating artificial intelligence systems and machine learning algorithms. Of course, the final choice depends on the specifics of the business for which it is created and the application itself, which should be created as a result.

However, it can be safely stated that the above technologies will be sufficient to realize the most fundamental tasks of artificial development since the main value of an application based on artificial intelligence comes from the implementation of a small amount of input data. However, this data should be of high quality and suitable for subsequent analysis.

Main Photo Credit: DevTeam.Space

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