Developers should be aware of famous Artificial Intelligence and Machine Learning Tools

By ridhigrg |Email | May 30, 2019 | 2778 Views

As adoption of AI and machine learning increases amongst businesses, the number of software tools for developers has also grown.
If nothing else, getting to know different AI frameworks and APIs will enable developers to learn new skills as the demand for knowledge in AI and machine learning.

Techworld explores the top tools on the market.
1. Google ML Kit
Google ML Kit, Google's machine learning beta SDK for mobile developers, is designed to enable developers to build personalized features on Android and IOS phones.

The kit allows developers to embed machine learning technologies with app-based APIs running on the device or in the cloud. These include features such as face and text recognition, barcode scanning, image labeling and more.

Developers are also able to build their own TensorFlow Lite models in cases where the built-in APIs may not suit the use case.

2. Infosys Nia
Infosys Nia is a knowledge-based AI platform, built by Infosys in 2017 to collect and aggregate organizational data from people, processes and legacy systems into a self-learning knowledge base.

It is designed to tackle difficult business tasks such as forecasting revenues and what products need to be built, understanding customer behavior and more.

Infosys Nia enables businesses to manage customer inquiries easily, with a secure order-to-cash process with risk awareness delivered in real-time.

3. 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 designed for developers to build applications such as pattern recognition, computer vision, computer audition (or machine listening) and signal processing for commercial use.

The Accord.NET Framework is divided into multiple libraries for users to choose from. These include scientific computing, signal and image processing, and support libraries, with features like natural learning algorithms, real-time face detection and more.

4. H2O 
H2O is an open source software tool, embedded with a machine learning platform for businesses and developers.
It was designed by H2O.ai and is written in the Java, Python and R programming languages. The platform is built with the languages developers are familiar with in order to make it easy for them to apply machine learning and predictive analytics.

H2O can also be used to analyze datasets in the cloud and Apache Hadoop file systems. It is available on Linux, MacOS and Microsoft Windows operating systems.

5. PredictionIO
Apache PredictionIO is an open source machine learning server built on top of an open source stack for developers and data scientists to create predictive engines for any machine learning task.

It consists of three core components:
PredictionIO platform - its open source machine learning stack for building, evaluating and deploying engines with machine learning algorithms
Event Server - an open source machine learning analytics layer for unifying events from multiple platforms
Template Gallery - a place for you to download engine templates for a different type of machine learning applications

6. Torch
The torch is a scientific computing framework, an open source machine learning library and a scripting language based on the Lua programming language. It provides an array of algorithms for deep machine learning. The torch is used by the Facebook AI Research Group and was previously used by DeepMind before it was acquired by Google and moved to TensorFlow. 

7. Protege
Although enterprise-focused, Protege has a suite of open source tools ideal for developers to create 'knowledge-based applications with ontologies'.

Aimed at both experts and (somewhat) beginners, Protege lets developers create, upload, modify and share applications. Protege also houses an active community, making troubleshooting simple and collaboration optimized.

8.Google's TensorFlow
TensorFlow is an open source software platform specifically designed for machine learning projects.

TensorFlow works by providing a library comprising of numerical computation using data flow graphs. This lets developers deploy deep learning frameworks over multiple central processing units (CPUs), on mobile, desktop and tablet devices.

TensorFlow includes lots of documentation, tutorials, and online resources, so for those not familiar with the platform, or Python, TensorFlow provides lots of support for developers.

9. Amazon Web Services
Amazon Web Services (AWS) provides a number of artificial intelligence toolkits for developers, including Amazon Recognition Image, Amazon Lex and Amazon Polly. 

Recognition uses AI to add image interpretation and facial recognition to apps, which is often used for biometric security features.

Polly uses AI to automate voice to written text across 47 voices in 24 languages. Lex is the open source engine behind Amazon's personal assistant Alexa, allowing developers to integrate chatbots into web and mobile applications.

10.Amazon Web Services 
Amazon Web Services (AWS) provides a number of artificial intelligence toolkits for developers, including Amazon Recognition Image, Amazon Lex and Amazon Polly. 

Recognition uses AI to add image interpretation and facial recognition to apps, which is often used for biometric security features.

Polly uses AI to automate voice to written text across 47 voices in 24 languages. Lex is the open source engine behind Amazon's personal assistant Alexa, allowing developers to integrate chatbots into web and mobile applications.

Source: HOB