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AI use cases can materialize improvements across various sectors with the help of its building blocks, such as the availability of data, algorithms, and computing efficiency. Artificial intelligence has many 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 a game-changer in 2020.
Let us throw light on AI
Artificial intelligence demonstrates the behavior of computer systems associated with interpreting 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 emotions and respond logically towards them.
Artificial intelligence is the most incredible technology used in specific devices. It has become the most active part of various business software and SaaS product development requirements. AI solutions are deploying innovative solutions for the upcoming project automation across multiple industries. There are certain speculations regarding the scope of AI in coming years, which include the-.
- 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.
- Investment in this industry has also grown around six times.
- With the significant efforts of Artificial intelligence development companies, 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. It will continue for the foreseeable future requirements. AI solutions have incorporated deep machine learning algorithms into regular functioning to automate tasks for the user. Automating these processes will save lots of time and energy to make the job easy and simple for the employees and developers, and they can work actively towards it. Artificial intelligence certainly has a direct impact on enhancing business productivity as well.
It is becoming a reliable staple in business software with all the progression. The efficiencies of AI and ML tools and frameworks are embedded in all solutions that offer predictive capabilities and automation functions. It quickly makes business professionals in the same segment get away with most of the complex processes. It also helps the developers to build intelligent software solutions, binding machine learning or artificial intelligence solutions to it and creating a unique application from scratch.
Most tools, frameworks, or libraries help users create in-depth software functions. Artificial intelligence tools and frameworks have become necessary 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 over the past few years. This includes processing the voluminous data across multiple industries and using them productively. There are several tools and frameworks available for developers or data scientists that can extract the most out of artificial intelligence and machine learning. Let us explore most AI tools and frameworks and learn about each one of them-
Read the blog- How Will Artificial Intelligence Impact The IT Industry
SCIKIT LEARN
It is one of the most popular Ml libraries that can efficiently underpin many administered/ unsupervised calculations. It also manages, directs, or calculates relapses, bunching, choice trees, k-implies, etc. Scikit learn expands on the libraries of Python, SciPy, and NumPy.
- It controls multiple calculations for ai software development, and data mining assignments, including 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 conduct progressive complex calculations)
FEATURES
- Cross-validation: Scikit teach 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
AMAZON MACHINE LEARNING
Amazon Machine Learning is a cloud-based software tool that can be used by web and mobile app developers. This managed SaaS application development service can be used for developing machine learning models as well as generating predictions out of them. AML can integrate data from various sources, including Amazon S3, RDS, or Redshift. It is a dedicated tool that provides the developer with lots of visualization and wizard tools.
FEATURES
- Support Model types like multiclass classification, regression and binary classification
- Allow the user to create data source objects from my SQL database.
- Permit users to create data source objects from data in Amazon Redshift.
- It has fundamental evaluation and real-time predictions.
ACCORD.NET
Accord .net tool is a .net-based framework with many ready-to-use libraries, especially for audio and image processing. This framework is preferred for most commercial applications because of its efficiency in developing production-grade vision and 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 audio, 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 audio signals easily
TENSORFLOW
The fascinating aspect of Tensorflow is that you can easily arrange and run the program composed in Python on either the 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 can also arrange multilayer hubs through which you can easily set up, 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 acknowledgement application.
FEATURES
- ROBUST PRODUCTION- this tool serves a direct way of production; despite 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
SONNET
It is a high-level AI framework that deals 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 built around a single concept of 'sent modules."
FEATURES
- It allows developers to build their 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.
THEANO
This framework is exceptionally folded over the keras, an anomalous state of the system library that runs parallel with Theano Library. It is a moderate Python library that can keep running over the framework. It was created to implement profound learning models for AI innovations quickly. It can run on Python 2.7 or 3.5 and continuously execute on the CPU or GPU. It also enables the users to accelerate information up to multiple times faster while running on the CPU. The speed of the stool is especially profitable as that conducts complex undertakings easily.
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FEATURES
- GPU usage: Theano can easily perform the computation, which is data-intensive with faster speed as compared to the 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
MXNET
This tool enables developers to trade computation time across the memory, which can be extremely useful for recurrent nets on large sequences. It is developed with scalability in preferences, making it easy to use and implement across multi-machine instances. MxNet has a list of features, like writing custom layers for high-level languages. 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, and 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
CNTK
This tool enables users to recognize and combine model types easily. It also implements stochastic gradient descent and automatic differentiation or parallelization across various servers or GPUs. This tool is available for most 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 of training the production systems faster
- FLEXIBILITY- it supports multiple tasks like image, text, and speech
OPENNN
OpenNN is the latest tool with an array of advanced analytics and features. It also provides incredible graphs and tables 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 assists in proof of concept and technology evaluation
H20-
- H20 is an open-source ML platform which is also an AI tool. It is entirely business-oriented and assists in analyzing the data and drawing insights from 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 to achieve absolute transparency and understanding of the algorithms
- H2O has in-process deployment editors accessible for Python, R, etc
- It is efficiently distributed and performs in-memory processing easily
Read the blog- How Will Artificial Intelligence Impact The IT Industry
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 barcode scanning, text recognition, image labeling, etc.
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 applications and innovation with the help of its architecture
KERAS
This tool is for developers who are in favor of python or SaaS application development to a greater extent. It is a high-level library with 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 the community requires it.
FEATURES
- Extensibility: its models are reliable, have complete expressiveness, come 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
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 need more experience.
FEATURES
- QUICK AND EASY DEPLOYMENT- AutoML has an easy GUI that helps developers to improve and evaluate the deployment models based on 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
TORCH
It can serve many 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?
Artificial intelligence and machine learning have become the most demanding technology with a strong community backed by them. There has been an increase in the number of businesses adapting to it for the future. All the glitters and certain twists make it essential to know which software or AI tools and frameworks can be deployed as a fully functional algorithm that can easily meet your business objectives. It is important to choose a solution that can help you stand different and conduct an in-depth learning process of SaaS application development and similar instances. All the tools and frameworks mentioned above are branding solutions you must try in 2020.
These tools will not only enhance the developer's skills but will also equip the enterprise with future-ready solutions. However, the decision to choose an artificial intelligence framework entirely depends on your project requirements and the development team's technical skills. You also have to include features that can leverage your application development process and make them more accessible for the users and 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
AI use cases are reshaping the technology's development instances and availing capital dependency on them. These improvements will make the systems more intelligent and enhance their regular functions, like auto-driving cars, automatic electrical consumption, robotic diagnosis, etc. In simple terms, we can have AI to automate functional technology for better convenience. In the long run, AI will unlock the significant value of several technologies and development fields, especially SaaS product development, etc.
- CONVERGENCE OF IoT- IoT is another trending technology that will likely merge with machine learning and artificial intelligence. Altogether, they can be used across various platforms for 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 productivity and customer experience.
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Final words
With every passing day, Artificial intelligence and machine learning are evolving to their core. It also facilitates 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 create effective solutions per business requirements.
