Top 10 Popular Artificial Intelligence and machine Learning Tools & Frameworks for Developers

Artificial intelligence is going to be the next big in the tech world. In fact, we’ve already started to experience it, but most of us are unaware of it. Where and how? If you’re using Google Allo app, you have already come across it. Today all sorts of tech companies, including mobile app development companies are adding AI service to their service portfolio because of its emergence and demand it created.

The best tools and software in the filed make the world well experience the technology. The best AI frameworks and APIs will enable developers to learn new skills in the AI field. So we have crafted a list of 10 best AI and machine learning tools in the market, including of Google and IBM.

10 Top AI and Machine Learning Tools for Developers

• AI-one
• Protégé
• IBM Watson
• DiffBlue
• TensorFlow
• Nervana Neon
• Google Cloud ML Engine
• Microsoft Azure
• Rainbird
• Dialogflow

1. AI-one

Captioned with biologically inspired intelligence, ai-one helps developers build intelligent assistance within most software applications. “Analyst Toolbox’ in the app provides developers building agents, a document library and APIs. ai-one is capable to turn data into generalized rule sets. It do the same by enabling lots of in-depth AI and machine learning structures.

2. Protégé

Protégé allows developers to develop knowledge-based applications with ontologies. This enterprise focused tool has a suite of open-source tools for developers. Developers can easily create, upload, modify and share applications. It is focused on both experts and beginners. The active community it has makes the troubleshooting easier. Because of its simplicity, Protégé is preferred by mobile app development companies and other technology companies to develop advanced solutions based on AI and machine learning.

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3. IBM Watson

IBM Watson is a big player and well-known name in AI and machine learning fields. Its Watson platform offers a wide range tools for both developers and business users. It is available for developers as a set of open APIs. It provides access to different sample code, starter kits, and developers can use it to build cognitive search engines and virtual agents.

Watosn’s chatbot building platform is focused on beginners. With little machine learning skills, anyone can build chatbots using this platform. It also provides pre-trained content for chatbots, which quickens training the bot.

4. DiffBlue

DiffBlue is a dedicated code automation platform. Created by Daniel Kroening at the University of Oxford, DiffBlue is simple and easy to use. The tool is focused on locating bugs, performing test writing, refactoring code, finding and fixing weak code. All these are done through automation.

5. TensorFlow

Developed by Google, TensorFlow is an open-source software platform which is dedicated for machine learning projects. It provides a library comprising of numerical computation using data flow graphs. With the tool, developers can deploy deep learning frameworks over multiple CPUs, on desktop, mobile and tablets. It provides developers with a bunch of documentation, online resources and tutorials. Developers who are not familiar with Python can also utilize the tool well.

Features:

• Deep Flexibility
• True Portability
• Connect Research and Production
• Maximize Performance
• Helps in building and training your machine learning models.
• You can also run your existing AI models using TensorFlow.js which is a model converter.
• It helps in neural network.
• A full cycle deep learning system.
• Train as well as build ML models effortlessly using high-level APIs
• TensorFlow is an open source software and highly flexible.
• TensorFlow can also perform numerical computations using data flow graphs.
• Runs on GPUs and CPUs, and also on various mobile computing platforms.
• Efficiently deploy and train the model in the cloud.

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6. Nervana Neon

Neon is an open-source Python-based machine learning library. Neon allows developers to build and train deep learning technologies and they can deploy the same in the cloud. Developers can understand the tool with video tutorials and a ‘model zoo’ which contains pre-trained algorithms and example scripts.

7. Google Cloud ML Engine

If you’re training your classifier on a plenty of data, your PC or PC may function admirably. In any case, if you have billions or millions of training data? Or then again, the algorithm is very complex and take quite a while in proper execution? You should utilize Google Cloud ML Engine for your rescue. Google Cloud ML Engine is a platform where artificial intelligence app developers or machine learning app developers and data scientists create and run optimum quality machine learning models or algorithms.

Here are top features of Google Cloud ML Engine:

• It provides machine learning model training, building, deep learning and predictive modelling.
• The two services viz. prediction and training can be used independently or jointly.
• This software is widely used by enterprises, i.e., detecting clouds in a satellite image, responding faster to emails of customers.
• It can be widely used to train a complex model.

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8. Microsoft Azure 

Microsoft Azure Machine Learning offers cloud based progressed analytics intended to improve on AI for business. Business users can show their way, with top tier algorithms from Xbox, Bing, R or Python packages, or by dropping in custom R or Python code. The completed model would then be able to be deployed in minutes as a web service, which can connect to any business data, anywhere.

It can also be published out to the community into the Machine Learning Marketplace. In Machine Learning Marketplace, the application programming interfaces (APIs) and completed services are available.

Features:

• Digital Marketing
• Mobile
• E-Commerce
• LOB Applications
• SharePoint on Azure
• Dynamics on Azure
• SAP on Azure
• Red Hat on Azure
• DevOps
• Development and Test
• Monitoring
• Business Intelligence
• Big Data and Analytics
• Data Warehouse
• Business SaaS Apps
• Cloud Migration
• Hybrid Integration
• Backup and Archive
• Disaster Recovery
• Internet of Things
• Digital Media
• High Performance Computing
• Blockchain
• Microservice Applications
• Gaming
• Mainframe Migration

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9. Rainbird

Rainbird is an award winning Artificial Intelligence platform that makes the business tasks more smarter. It empowers enterprises to build frameworks with human-like decision making abilities, resulting in greater efficiency and increased quality. Rainbird empowers users to take existing, human, business information and consolidate it with the organization’s information to automate knowledge work and deliver consultative systems that can transform the way the company’s staff and customers interact with each other. Rainbird is at the forefront of knowledge work automation technology. It is an amazing environment for re-designing information work empowering organizations to automate and augment the work their staff’s performance.

Features:

• Visual User Interface
• Controlled Learning Algorithms
• Smart Data Import
• Natural Language Processing (NLP)
• Analytics and insights

10. Dialogflow

API.AI allows clients to build brand-unique, natural language connections for bots, applications, services, and gadgets / devices. It includes a Natural Language Understanding Tools to design unique conversation scenarios, design comparing activities and break down interactions with users. Leverage predefined knowledge bundles gathered from several years, including encyclopaedic data, climate, news, booking, flight timetables, and much more. While the platform learns from examples provided by Dialogflow developers and conversations, it has with end users to continuously improve user experience of all the models and algorithms. API.AI offers Conversation Support wherein users can switch seamlessly between conversation topics, while remembering where they left off for each one.

Features

• Machine Learning
• Integration
• Conversation Support
• Cross-platform Support
• Multilingual Support
• High Performance

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Conclusion:

There are some other tools that help you develop AI and machine learning based apps – tools offered by Amazon Web Services, tools offered by Microsoft, and the tools like OpenNN and Apache Spark MLlib. Choosing between the tools depends on your specific requirement.

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