ML and AI Tools and Frameworks for Developers
Artificial intelligence is a complex area including specialized mathematical algorithms, computing machines, software programs and many more. Artificial intelligence solutions are implemented in almost all areas of social activities in business research and other fields. The importance of an artificial intelligence developer has therefore become so crucial in today’s modern era. If you want to know about Artificial intelligence in web development make sure to check our article “AI & Web development?”.
The number of tools and frameworks available to data scientists and developers has increased as machine learning becomes more prominent. Microsoft, IBM, Google, and AWS have machine learning APIs that they respective cloud platforms. It makes easier for developers to build services by summarizing some of their machine learning algorithms’ complexities. Let’s discuss how artificial intelligence software can be properly developed and how best to find and hire AI engineers.
As follows, we introduce top machine learning tools.
1.Google ML Kit
ML Kit is a mobile SDK that offers a powerful package to Google’s machine learning expertise. Even developers new to machine learning are easy to use for Android and iOS applications. With a few lines of code, you can implement the functionality you need.
To get started with the ML Kit, comprehensive knowledge of neural networks or models is not necessary. It provides APIs. ML Kit can help you to your custom Tensor-flow Lite models in mobile applications.
You can use machine learning techniques in your apps with the aim of the ML Kit. The Google Cloud Vision API, TensorFlow Lite, and the Android Neural Networks API is Google ML technologies that can be combined in one SDK. The functionality is available on the device or in the cloud are as follows.
OpenNN is Abbreviation of Open Neural Networks Library. It is a software library which is written by C + + programming language. It implements a major area of research into deep learning (as a subfield of machine learning)which is known as neural networks.
OpenNN implements data mining methods and provides a set of functions. These can be integrated into other software tools. There is no graphical user interface, some functions can be integrated with Specific visualization tools.
OpenNN’s main advantage is high performance. To higher processing speed and better memory management, it is developed in C + + and implements parallel CPU with CUDA via OpenMP and GPU to accelerate processing.
A wide range of materials including tutorials on the site. A Neural Designer tool is available for advanced analytics. This tool helps you simplify data entries by creating visual content like graphs and tables.
Here you can download OpenNN .
3. Apache Mahout
As the definition of Apache Mahout(TM) says, “It is a distributed linear algebra framework and mathematically expressive Scala DSL designed to let mathematicians, statisticians, and data scientists quickly implement their own algorithms”. 
- Mathematically Expressive Scala DSL
- Support for Multiple Distributed Backends (including Apache Spark)
- Modular Native Solvers for CPU/GPU/CUDA Acceleration
This was the exclusive field of academics and companies with large budgets for research. In today’s, the need for intelligent application is growing. Intelligent applications can learn from data.
Apache Mahout is used to creating applications using machine learning techniques including collaborative,
categorization and clustering. It is filtering to find commons in large groups of data or to tag large web content volumes.
- Scalable for large sets of data – The core of machine learning algorithms is Implementation of large distributed and scalable systems.
- Scaled to support all of the business cases – it is distributed within the commercially friendly license of Apache Software.
- Scalable community – A wide community with dynamic, diverse and responsive features which facilitates discussions on the project and its possible use.
Many of the implementations rely on the Apache Hadoop platform, so how it works should be well understood.
4. HPE Haven On-demand
HPE  is a platform for cloud services that simplify how data can be interacted and turn it into an asset at any time. They offers a large collection of applications programming interfaces (APIs) for machine learning to interact in a different way with structured and unstructured data. Some of these APIs can be employed in cognitive computing and data science. Depending on the definitions and objectives of the problem, there are a number of ways to address problems in these realms.
The APIs offered at the launch should be familiar to those who worked on Bluemix or the Microsoft Azure Marketplace with IBM Watson. Most of them provide common business functions, like enterprise search or converting file formats, and even needle – moving apps for machine learning are relatively familiar.
This Framework is a .NET framework that implements machine learning .NET written in C#. image and audio processing libraries are completely combined with frameworks. This framework is a complete framework to build a degree of production in computer audition, statistics applications, computer vision and signal processing for all-purpose even commercial purpose. The project’s source code is available in the Gnu Lesser Public License (version 2.1).
The framework includes the source code of libraries and NuGet packages and executable installers. The covered areas are image and signal processing, numerical linear algebra, statistics, numerical optimization, machine learning and supporting libraries (like as visualization and graph plotting).
This project was created for the extension of AForge.NET Framework capabilities. AForge.NET has been incorporated into itself. The latest version of it joined the two frameworks which are called the Accord.NET.
A set of instance applications allows you to quickly get up and running and a documentation and wiki help to fill in the details.
6.Amazon Machine Learning
Amazon Machine Learning offers wizards and visualization tools. This guides you through the development of machine learning models (ML) without complex machine learning technology and machine learning algorithms. Your models are ready; it is easy for Amazon Machine Learning to obtain your application predictions using simple API. No infrastructure needs to be implemented or managed with a customized prediction code.
It allows you developing robust scalable intelligent applications which are employed without an extensive background in algorithms and techniques for machine learning. The service including three operations for the construction process of machine learning models. This is the analysis of data, training model and assessment of models. Its features include batch APIs and real-time forecasts that enable users to build intelligent applications easily.
AWS now offers Amazon SageMakerto facilitate machine learning for data scientists and developers. It is a completely managed service with complex training, development and hosting features. These features allow developers to focus on machine learning models data science without worrying about infrastructure or system management.
7. Azure Machine Learning Workbench
Azure Machine Learning service capabilities of facilitates and increase the speed up of building, training, and deployment of models for machine learning. Automated machine learning allows data scientists at all levels of expertise to identify appropriate machine learning algorithms and hyperparameters more quickly.
To support common open-source frameworks, like TensorFlow, PyTorch and scientific knowledge allows data scientists to use their choice of tools. DevOps machine learning improves productivity by allowing cloud and edge models to be experimentally tracked and managed. All these capabilities, including the workstations of data scientists, any python environment can be accessed.
Microsoft has developed Azure Machine Learning service in close cooperation with its customers, who use it every day to improve customer service, build better products and optimize their operations.
Azure Machine Learning was built on the following design principles, which are detailed in here, to simplify and accelerate machine learning.
- Enable data scientists to use rich and familiar tools for data science
- Facilitate the use of popular learning frameworks for machines and deep learning
- Accelerate valuation time by providing lifecycle learning capabilities from end to end
8. IBM Watson Analytics
IBM Watson Analytics is an intelligent application for self – service visualization and data analysis to discover patterns and data insights. It guides you through the discovery process and automates the subsequent predictive analysis and cognitive processes.
IBM Watson Analytics has the ability to process natural languages. You can interact with your data as if you were talking to it such as, structured and unstructured information can be easily extracted from answers.
IBM Watson Analytics allows you to immediately find new and emerging data trends. The service even visually presents it through your dashboards. the patterns can be detected faster. With the aims of IBM Watson Analytics, you can encounter new and emerging trends in your data. The service even visually presents it with your dashboards. the patterns can be detected faster.
IBM Watson Analytics employs natural languages processing (NLP) to let you to conversations with your data. You can use your own words for seamlessly understandable insights with querying the application.
The purpose of this article is to give an overview of the trending machine learning and frameworks tools. A treasure trove of resources for developers who just started their careers and experts will be found through the list above. Although some depend on a particular programming language, others can be used in a variety of instances, including in the cloud. Both software and cloud-based offerings enable developers to benefit from each other’s benefits.
Before building a machine learning application, it can be a difficult task to select one technology from the many options. Therefore, before making a final decision, it is important to evaluate several options. To help you select the machine learning framework that best suits your workflow consults an artificial intelligence engineer.
We are witnessing some common themes surfacing as these tools now develop. Flexibility in these software functions often costs performance or scalability or both. If a toolset is tightly linked to a language or deployment format, it is typically more difficult to reshape it bigger, wider, faster or fatter. Over time, platforms or wider community – driven migration to the most efficient, powerful, open, smartest and most “trainable” tools are likely to be consolidated.