From Pytorch to Rapidminer: The Top ML & AI Tools/Frameworks
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Machine learning (ML) and artificial intelligence (AI) have facilitated the processing of vast amounts of data. Data scientists and developers have access to numerous tools and databases constantly growing.
Each of these tools has different functions that have broken down barriers holding programmers back. Some tools are more mathematically oriented while others revolve around data analysis and data mining.
AI is a complex area specializing in mathematical algorithms, computing machines, software programs, and much more. With the help of tools and frameworks, data scientists can take ML, AI, cloud computing, digital technology to a whole new level.
Working with Artificial Intelligence (AI) technology, ML allows software applications to learn from the data and become more accurate in predicting outcomes without human intervention and without being explicitly programmed. To make this happen, programmers use tools and libraries to handle ML tasks.
Here is an overview of some of the most common tools used to make this technology work:
Scikit Learn
Scikit Learn is a free software ML library for the Python programming language. It is a simple and efficient tool for data mining and data analysis. It is built on Numpy, Scipy, and Matplotlib platforms and provides a range of supervised and unsupervised learning algorithms in Python such as classification, regression, clustering, and more. Scikit Learn is the basic building block for any Machine Learning algorithm.
KNIME
KNIME is a free and open-source data analytics reporting and integration platform that is built for powerful analytics on a GUI based workflow. This platform is used for gathering and wrangling data, data modeling and visualization, and data management, deployment, and optimization. If someone wants to work on data analytics but doesn’t know how to code they can easily use this tool to derive insights.
Tensorflow
Tensorflow is one of the popular libraries for machine learning and deep learning tasks. It is an open-source library built by the Google Brain team and it is used for numerical computation and large-scale machine learning. It is especially useful for graphics applications that deal with images and videos. Tensorflow provides accessible and readable syntax which provides essential and easy-to-use programming resources. This platform uses Keras and other high-level APIs to run smoothly. It can run both on CPUs and GPUs also it relies on user-defined static graph concepts for computational models. According to the developer’s economics community, 86% of Machine Learning developers use TensorFlow to run their applications.
WEKA
WEKA is an open-source Java software. It utilizes a collection of Machine Learning algorithms for data mining and data exploration tasks and is one of the most powerful machines learning tools for understanding and visualizing machine learning algorithms on local machines. WEKA uses both a graphic and command-line interface and is very good visualization software. It provides predictive modeling and visualization and is an environment for comparing learning algorithms and graphic representation data. One problem users have encountered, however, is there is very little documentation and online support available for this platform.
Pytorch
Pytorch is a Python-based library built to provide flexibility as a deep learning deployment platform. It is actively used by Facebook for all their Machine Learning and deep learning applications. It comes with a dynamic computational graph which makes it easier to execute relatively small parts of code. Users don’t have to wait for a long compilation process to build and run their models, which means computations can be run almost instantaneously. The platform is effective in providing rapid prototyping and analyzing research ideas with quick feedback. Pytorch has proven to be an effective tool that allows sharing across teams using a standardized framework.
Keras
Keras is an open-source neural network Python library. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, or Theano. It is designed to enable fast experimentation with deep neural networks and is modular and extendable. The platform has a high-level of API that helps to run on TensorFlow CNTK, Theano, or MxNet. It is also popular because of its ease-of-use and syntactic simplicity facilitating fast development. Keras is slower than TensorFlow and PyTorch but it has simple architecture and is more readable and concise. It is preferred when implementing rapid prototyping, such as quickly building and testing neural networks with minimal lines of code. There is a single line of code used for implementing Keras which makes it a preferable framework for programmers. It is more suitable for small size datasets and it is recommended for beginners due to its simple and easy-to-understand design.
Rapidminer
Rapidminer is a data science software platform for teams that unites data preparation, machine learning, deep learning, text mining, and predictive model deployment. It has a powerful and robust graphic user interface that enables users to create, deliver, and maintain predictive analytics. Uncluttered, disorganized, and seemingly useless data becomes very valuable data with Rapidminer. It simplifies data access and structures in a way that is easy for individuals or teams to comprehend. Through GUI it helps in designing, implementing workflows, and visualization of data.
Google Cloud AutoML
Google Cloud AutoML is a suite of Machine Learning products that enable developers with less Machine Learning expertise to train high-quality models according to their business requirements. It provides a simple graphic user interface to train, test, predict, evaluate, and deploy models on data provided by the user. Currently, the suite of tools includes AutoML Vision, AutoML Natural Language, and AutoML Translation.
Azure ML Studio
Azure ML Studio is a collaborative and drag-and-drop cloud-based tool. It can be used to create, test, and deploy predictive analytic solutions on data. It is very useful for applications because it can publish Machine Learning models as web services that can be consumed within BI tools such as Power BI, Excel, or any other custom application. It also provides a visual workspace where developers create, runs experiments, test, and evaluate results of Machine Learning models. It can drag-and-drop datasets and connect data and algorithms through a workflow to machine learning experiments.
These ML applications are continually being improved to make it possible to make sense of large amounts of data to produce useful information. Companies can see trends that help them better understand and target their customers, increasing sales. ML can also be used to detect data from internal and external threats by detecting anomalies in your computer systems. The possibilities for using this technology are unlimited.
At accentedge we work with our clients to provide customized ML solutions. Our team, with 30+ years of technology experience, can help your business adapt to the digitally revolutionized world. We bring experience with these different tools & frameworks and are here to help with customized approaches to meet your data needs.
