


From Pytorch to Rapidminer: The Top ML & AI Tools/Frameworks
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.
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 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 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.
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 machine 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.
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.
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.
These ML application 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.


