Top AIML Tools and Softwares

With disruptive technologies shaping the global economy, upskilling has become the new norm for professionals seeking the best opportunities in the job market. Digital revolution 2.0 is happening slowly, but surely. With that in mind, we bring you the top AIML softwares, tools and programming languages of 2019.

Top AIML Softwares, Tools and programming Languages of 2019

  1. Salesforce Einstein:  The Salesforce Einstein is an AI platform that creates recommendations and predictions based on business process inputs and customer data. This platform is used to automate customer-facing responses to optimise productivity and customer relationship.
  2. IBM Watson: IBM Watson is a highly efficient AI software that is capable of learning more with very few data points. Companies even leverage IBM’s industry expertise to optimise their own business processes. This software allows businesses to build models from scratch or use an existing pre-trained business solution.
  3. Deep Vision: This facial recognition model monitors demographic variation for any geographic location to understand behaviour. Companies use this model to decode customer patterns and design their marketing campaigns accordingly. This model can also be used for predictive analysis and target potential leads. 
  4. Cloud Machine Learning Engine: A cloud-based predictive analytics modelling platform, Cloud Machine Learning Engine works on both large and small size data to provide insights on different business processes. Companies have built secure infrastructures, APIs, data analytics, developer and machine learning tools using this model. 
  5. Azure Machine Learning Studio: This provides an interactive platform for predictive analysis. The drag-and-drop feature provides an easy to use interface to create innovative cloud-based apps and edge computing. Azure Machine Learning Studio also offers infrastructure, AI services and tools for developing intelligent solutions.
  6. Nvidia Deep Learning AI: A suite of products dedicated to deep learning and machine intelligence, this software helps organisations with smart and predictive analysis. Used by government and businesses alike, the Nvidia deep learning AI provides an adaptive platform for all kinds of analytical insights.
  7. Playment: Playment is a data labelling platform that prepares training data for computer vision models at scale. It specialises in data relating to autonomous vehicle, drones, mapping, and similar spaces which require high precision annotation.  

Top Machine Learning Tools of 2019

  1. TensorFlow: TensorFlow has topped the charts for machine learning tools in 2019. It offers a JS library that helps to develop and train machine learning algorithms. This open-source machine learning library was created by Google. It is flexible, easily adaptable and has an exhaustive list of tools for machine learning training models.
  2. Amazon Machine Learning: This is a cloud-based machine learning software which can be used at scale by web or mobile app developers. Amazon machine learning is widely used for creating machine learning models and predictive analysis. It provides wizards and visualization tools and supports three types of models (multi-class classification, binary classification and regression).
  3. Accord.NET:  This .Net ML framework has image and audio processing libraries in C#, making it a favourite among developers. It includes multiple libraries for a number of applications like linear algebra, statistical data processing, pattern recognition and more. 
  4. Apache Mahout: This is a framework for Scala DSL and distributed linear algebra. An open-source free project of the Apache software foundation, this framework can implement algorithms for mathematicians, statisticians and data scientists. 
  5. Shogun: This machine learning library has C++ programming languages which provides data structures for ML problems. Additionally, it supports other languages like Ruby, Lua, Java, Octave, R, Python and more so that it is widely adaptable.

Top Programming languages of 2019

  • Python

Python is one of the top programming languages due to its simplicity. The syntax and data structure of Python is easy to learn. Object-oriented programming, functional programming as well as procedure-oriented style of programming by Python. A number of libraries exist within this programming language, making our tasks simpler. Being one of them. Helps us settle numerous logical calculations. Python offers rapid prototyping compared to other programming languages such as Java or C++. It can be used in a numerous AIML and requires a smaller number of code lines compared to other programming languages.

  • R

R is a full-fledged software that is used for statistical computing and visualization, it is a free and open source programming language, allowing it to be easily accessed. Programmers can learn from the source code and modify it as required. R has been in use for a long time now and has been used in areas such as and, It is one of the best programming languages when it comes to statistical purposes as  is useful for controlling and analyzing data. It has various packages like RODBC, and these are used in the field of AI and ML. These packages allow the language to implement ML Algorithms easily. R also has a wide utilization is the banking sector.

  • Java

The existence of Java dates back way before Python, and it works well for Machine Learning development. It provides many benefits such as, ease of use, package services, simplified work with large-scale projects, graphical representation of data and better user interaction. Game development and mobile application development can also be done using Java. One of the most popular toolkits for Java is Weka, this contains tools for data pre-processing, classification, regression and visualization. Another library called Deeplearning4j which is specially designed for Java, provides support for deep learning algorithms. 

  • Julia

Compared to the other programming languages, Julia is a newer, high-level, free programming language. It is rapidly being adopted by the tech as well as finance community due to its ability to execute ML Algorithms at a fast speed. Investment banks are using Julia for time series analytics and insurance companies are using it for risk calculation. One of the major advantages is that Julia is equipped with Flux. Flux provides a flexible interface which can be modified to create advanced models. Some of the tools available for Julia are a linter, profiler, debugger package and many more. Julia is adaptable to existing ML frameworks such as TensorFlow and MXNet.

  • C/C++

C++ is a general-purpose language developed as an extension for the popular C Programming Language. It was planned with an inclination towards resource-constrained software and large systems, effectiveness and adaptability of utilization as its main features. Most people start with C/C++ as their first programming language. C/C++ is used in the area of AI in Gaming. It has sophisticated libraries such as Mlpack, which is a fast and flexible ML Library. The main aim is to implement ML Algorithms at a faster speed.

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