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Guide About Artificial Intelligence: AI Development Tools And Frameworks That Will Be Trending In 2020

02 May

Global investments and considerations for Artificial intelligence solutions tend to increase in the long run because of its incredible usability.

The use cases of AI can materialize the improvements across various sectors with the help of its building blocks as the availability of data, algorithms, and computing efficiency. Artificial intelligence has lots of tangible applications out of which fancy solutions are presented by machine intelligence and algorithms. The professionals in the same industry believe that AI solutions will become the game-changer in 2020. 

Let us throw light on AI

Artificial intelligence demonstrates the behavior of computer systems associated with the interpretation of human learning including reasoning, problem-solving, manipulation, and knowledge representation approaches. The Artificial intelligence development company in the same segment is working to make it socially intelligent and highly creative. In simple terms, it can be understood as- expecting a machine or robot to understand the emotions and respond in a logical manner towards it.

Artificial intelligence is essentially the most incredible technology used in specific devices. It has become the most dynamic part of various business software and SaaS product development requirements. AI solutions are deploying innovative solutions for the upcoming project automation across various industries. There are certain speculations regarding the scope of AI in coming years which include-

  • In the next five years, AI global market is expected to cross $ 60 billion which was only $1.4 billion in 2016
  • In coming years it will enhance business productivity by up to 40%
  • AI startups have also grown around 15 times over the past few years
  • The investment in this industry has also grown around 6 times
  • It is speculated that which the significant efforts of Artificial intelligence development company the robots are most likely to mimic human behavior

Why should you know about artificial intelligence tools and frameworks?

Artificial intelligence has eventually been making its path into the software development process and it will continue for foreseeable future requirements. AI solutions have incorporated deep machine learning algorithms into regular functioning to automate the tasks for the user. Automation of these processes will save lots of time and energy to make the job easy and simple for the employees as well as developers and they can work actively towards it. It is certain that artificial intelligence has a direct impact on enhancing business productivity as well.

With all the progression it is becoming a reliable staple in business software. The efficiencies of AI and ML tools and frameworks are embedded in all the solutions that offer functions like predictive capabilities and automation. It makes the business professionals working in the same segment get away with most of the complex processes easily. It also helps the developers to build intelligent solutions binding machine learning or artificial intelligence solutions to it and create a unique application from scratch. 

Most of the tools, frameworks, or libraries help the users to create in-depth functions of the software. The tools and frameworks of artificial intelligence have become necessary in order to facilitate enterprises with absolute excellence.

List of artificial intelligence tools and frameworks

Various IT evolutions have been supported by the Artificial intelligence development company from the past few years. This includes processing the voluminous data all across various industries and using them productively. There are a number of tools and frameworks available for developers or data scientists that can extract most out of artificial intelligence and machine learning. Let us explore most of AI tools and frameworks and learn about each one of them-

  1. SCIKIT LEARN

It is one of the most popular Ml libraries that can efficiently underpin lots of administered/ unsupervised calculations. It also manages, directs, or calculated relapses, bunching, choice trees, k-implies, etc. Basically, Scikit learn expands on the libraries of python, SciPy, or NumPy.

  • It controls multiple calculations for ai software development, data mining assignments, including the changing information, ensembling techniques, feature determination and executing them for a couple of lines
  • It is more than enough instrument for fully-fledged ML (this is functional until you actually conduct progressive complex calculations)

FEATURES

  • CROSS VALIDATION- Scikit learn permit developers to estimate the overall performance of the supervised models for unseen data
  • CLUSTERING- this feature is mostly for unsupervised classification
  • FEATURE EXTRACTION- it is mostly required in IoT app development where it gets easy to identify the text or image data attributes
  • DATASETS- generation and testing of data sets having a specific property
  1. AMAZON MACHINE LEARNING

Amazon machine learning is a cloud-based software tool that can be used by the web as well as mobile app developers. This managed SaaS application development service can be used for developing machine learning models as well as generating the predictions out of it. AML can integrate data from various sources including Amazon S3, RDS, or Redshift. It is a dedicated tool that provides lots of visualization and wizard tools to the developer.

FEATURES

  • Support Model types like multiclass classification, regression and binary classification
  • Allow the user to create data source object from my SQL database
  • Permit users to create data source object from data in Amazon redshift
  • It has fundamental evaluation and real-time predictions

  1. ACCORD.NET

Accord .net tool is a .net based framework that has lots of ready to use libraries, especially for audio and image processing. This framework is preferred for most commercial applications because of its efficiency to develop production grade vision along with deploying statistics applications indulged with signal processing. 

FEATURES

  • CLASSIFICATION- this framework support vector machines, decision trees, and logistic regression to help the developers resolve issues
  • CLUSTERING- it applies the clustering algorithms in arbitrary data including audios, videos and data tables 
  • HYPOTHESIS TEST- accord .net has more than 35 statistical hypothesis tests including the non-parametric tests
  • TRANSFORMATION OF AUDIO SIGNALS- this framework can parse, filter and save the audio signals easily 
  1. TENSORFLOW

The fascinating aspect of Tensorflow is that you can easily arrange and run the program composed in python on either CPU or GPU. Through these, you do not have to compose the whole program in C++ and CUDA to keep running the same on GPU. It is also capable of arranging multilayer hubs through which you can easily set, train, and transfer the systems having huge data sets. This function enables Google to easily understand the questions and comprehend them verbally in the voice acknowledgment application.

FEATURES

  • ROBUST PRODUCTION- this tool serves a direct way of production, in spite of the language or platform selection it can be deployed easily
  • MODEL BUILDING- with the help of multiple abstractions developer can easily choose one of the requirements
  • DISTRIBUTED TRAINING- it supports exploring true distributed methods which involve reducing the training time and searching for reliable hyperparameters
  • ACCESSIBLE SYNTAX- Tensorflow offers syntax to outshine Artificial intelligence solution and enhance readability 
  1. SONNET

It is a high-level AI framework that is designed to deal with complex network structures, especially in Tensorflow. it aims to create the primary python instances corresponding to any specific part of the complex network. Later the instances are directly connected to the computational graph and link them by simplifying the high-level architectures. Sonnet is a powerful programming framework that is built around a single concept of ‘snt module’. 

FEATURES

  • It allows developers to build their own objects or models
  • It is explicitly created to work parallel with TensorFlow hence developers can access its underlying information and scopes
  • It can also be integrated with bare TF code and can be written with high-level libraries as well

Read the blog- How Will Artificial Intelligence Impact The IT Industry

  1. THEANO 

This framework is exceptionally folded over the keras, an anomalous state of system library that runs parallel with Theano Library. It is a moderate python library that can keep running over the framework and was created to implement the profound learning models quickly for AI innovations. It can run on python 2.7 or 3.5 and continuously execute on CPU or GPU. It also enables the users to make information accelerated up to multiple times faster while kept running on the CPU. The speed of the stool is especially profitable that conducts complex undertakings easily.

FEATURES

  • GPU USAGE- Theano can easily perform the computation which is data-intensive with faster speed as compared to CPU
  • GENERATION OF C CODE- it can evaluate the expressions faster
  • NUMPY INTEGRATION- Theano tool has the integration with NumPy 
  • SYMBOLIC DIFFERENTIATION- it can compute the derivatives for single or multiple inputs for all functions
  1. MXNET 

This tool enables developers to trade computation time across the memory that can turn out to be extremely useful for recurrent nets on large sequences. It is developed with scalability in preferences which makes it easy to use and implement across multi-machine instances. MxNet has a list of features like writing custom layers for high-level languages and so on. It is not directly governed by any major corporation which makes it a reliable open-source community-based tool. 

FEATURES

  • BINDING MULTIPLE LANGUAGES- MxNet supports Perl, Scala, Julia, Clojure, C++, R, Java, along with in-depth integration into python
  • HYBRID FRONT END- it offers speed and flexibility to the developers
  • SCALABILITY- this tool is developed to provide scalability and support multi-machine and multi GPU system
  • TVM SUPPORT- it has TV and support which enables developers to run the program as a whole on devices
  1. CNTK 


 

This tool enables users to easily recognize and combine model types. It also implements stochastic gradient descent along with automatic differentiation or parallelization across various servers or GPUs. This tool is available for most of the developers under the open source license.

FEATURES 

  • PERFORMANCE- it can achieve the best performance on all the systems and benchmark functions
  • EFFICIENCY- it is capable to train the production systems faster
  • FLEXIBILITY- it supports multiple tasks like image, text, and speech

Read the blog- How Many Types of Artificial Intelligence are there?

  1. OPENNN

OpenNN is the latest tool that has an array of arsenal advanced analytics and features. It also provides incredible graphs and tables in order to facilitate the interpretation of data entries to a greater extent.

FEATURES

  • It has the highest processing speed for the objects
  • It is developed in C++ and conducts reliable design and implementation
  • OpenNN AI tool also assist in proof of concept and technology evaluation
  1. H20-

    H20 is an open-source ML platform which is also an AI tool. It is entirely business-oriented and assists to analyze the data and draw insights out of it. It is equally significant in IoT app development because of its two source versions. This tool can also be used for risk and return analysis, predictive modeling, insurance analytics, health care and customer intelligence, advertising technology, etc.

FEATURES

  • it is imperative in order to achieve absolute transparency and understanding the algorithms
  • H2O has in-process deployment editors accessible for Python, R, etc 
  • It is efficiently distributed and performs in-memory processing easily
  1. GOOGLE ML KIT

It is Google's machine learning beta SDK specially designed for mobile developers to enable them to create personalized features on multiple platforms. It also allows the developer community to embed AI and ML technologies with the application based API running on their cloud or device. This includes features like barcode scanning, text recognition, image labeling, and so on.

FEATURES

  • ACTIVE DEVELOPMENT- it has an extensible code that assists in the active development process
  • SPEED- it can process around 60M images every day which means it has the fastest speed in ai software development as well
  • COMMUNITY- it has a vast community of developers and data resources
  • EXPRESSIVE ARCHITECTURE- it supports unique application and innovation with the help of its architecture
  1. KERAS

This tool is for the developers who are in favor of python or SaaS application development to a greater extent. It is a high-level library having ultimate networks and it is backed by Tensorflow. This tool provides an abstract structure to the developers that can easily be converted into multiple frameworks if required by the community.

FEATURES

  • EXTENSIBILITY- it's models are reliable having complete expressiveness that comes with an array of amenities
  • USER-FRIENDLINESS- it is specially designed for developers and it is user friendly
  • LIMITED COGNITIVE LOAD- it required limited cognitive load with consistent APIs
  • PYTHON- models in his tool are written in python code which makes it easy to debug
  1. AUTO ML

Out of all the tools and frameworks, this is one of the most reliable and strongest editions having an arsenal of amenities available at the developer's disposal. It consists of machine learning instances to optimize the AI models. Auto ML tool is best for newbies who lack previous experience.

FEATURES

  • QUICK AND EASY DEPLOYMENT- AutoML has easy GUI that helps developers to improve and evaluate the deployment models based upon their data 
  • PERFORMANCE- this tool has incredible performance as it can leverage Google to search efficiencies to make it faster. It is highly evident in SaaS product development
  • SMOOTH TRAINING ML MODELS- it helps the developers to train their models as per the requirement
  1. TORCH

It can serve a vast range of AI algorithms for conducting deep machine learning instances. The torch tool can also leverage the process of scripting languages and underlying C implementation outshines the AI models. 

FEATURES

  • It has a powerful N-dimensional array
  • It has fast and efficient GPU support
  • Multiple routes for slicing, indexing and transposing
  • Linear algebra routine

Which is the best tool for developers?

At present Artificial intelligence and machine learning have become the most and demanding technology having a strong community backed by it. There has been an increase in the number of businesses adapting to it for the future. All the glitters there are certain twists which make it essential to know which software or AI tools and frameworks can be deployed as a fully functional algorithm that can meet your business objectives easily. It is important to choose a solution that can not only help you stand different but also conduct an in-depth learning process of SaaS application development and similar instances. All the tools and frameworks mentioned above are the branding solutions that you cannot avoid in 2020. 

These tools will not only enhance the developer skills but will also make the enterprise equipped with future-ready solutions. However, the decision to choose an artificial intelligence framework entirely depends upon your project requirements and technical skills of the development team. You also have to include the features that can leverage your application development process and make them more accessible for the users as well as developers. You can explore each of the tools mentioned above and learn which one suits your requirements and your business prerequisites best.

The future technologies supported by AI software development?

The use cases of AI are reshaping the development instances of the technology along with availing capital dependency on them. These improvements will make the systems more intelligent and enhance regular functioning like auto-driving cars, automatic electrical consumption, robotic diagnosis, etc. In simple terms we can AI is all set to automate functional technology for better convenience. In the long run, we will get to see AI unlocking the significant value of a number of technologies and development fields, especially SaaS product development, etc.

  • CONVERGENCE OF IoT- IoT is another trending technology that is likely to merge with machine learning and artificial intelligence in the future. All together they can be used across various platforms for conducting efficient root cause analysis, machinery detection, predictive maintenance, etc. the role of artificial intelligence is dynamic in the internet of things solutions.

  • EXTENDED REALITY- startups are more inclined towards extended reality (XR) rather than virtual or augmented reality. It helped them enhance the immersive experiences supporting artificial intelligence to the core of business solutions. The extended reality also helps them address various security concerns to improvise the productivity and customer experience.

Final words

Every passing day Artificial intelligence and machine learning is evolving to its core. It is also facilitating the IT sector and other businesses with innovative solutions, IoT app development services, and other assistance. The artificial intelligence tools mentioned in this article can ease your software development process. With these tools you can also create effective solutions as per business requirements.

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