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Machine Learning Engineer Tools

The world is abuzz with the news of machine learning advancement. Everyone wants to know, learn, and use machine learning to reap this emerging technology’s benefits. Machine learning engineer is one of the most desirable qualifications in the job market.

In recent years, machine learning engineer's job has become a bit easier thanks to the development of open-source and proprietary software, tools, libraries, and packages. As businesses embrace AI and machine learning, the number of software tools for engineers has grown too. Some notable tools are listed below.

Azure Machine Learning

Microsoft Azure Machine Learning Studio offers the most comprehensive capabilities. Ideal for beginners, the drag and drop tool is interactive and collaborative and allows building, testing, and deploying predictive models. Data processing is visual, so complex programming is not essential to use Azure.


Collaboratory (CoLab) is an open-source cloud-based Jupyter Notebook environment needing no special setup. Google developed this research project to familiarize the masses with machine learning research and applications. Beginners can easily start experimenting because COLAB does not involve the installation of prerequired packages and libraries.


RapidMiner combines data processing, machine learning, and predictive modeling to build machine learning applications as a data science tool. Its graphical interface enables users to develop, deploy, and analyze predictive analytics. RapidMiner helps simplify data accessibility and understanding by cleaning cluttered, disorganized, and noisy data.


Python-based Pytorch is a flexible deep learning tool. It develops on top of NumPy, a computing library of Python. Pytorch supports dynamic computation graphs, which supports writing code in your preferred language, and CUDA implements the algorithm using GPU and reduces the computational time. Facebook uses Pytorch for its machine learning operations.


TensorFlow is a specialized machine learning and deep learning neural network library. It enables simple visualization of complex multilayer networks with high versatility.  TensorFlow has tools for research, design, development, deployment, and production of machine learning models.

The TensorFlow environment is highly accessible with easy syntax. The applications of TensorFlow include voice and image recognition, natural language processing, and generative adverbial networks.


As an open-source java-based machine learning tool, Waikato Environment for Knowledge Analysis (WEKA) offers data mining and data exploration algorithms. An engineer can access this highly powerful tool via a user interface or command line. Even beginners can understand and visualize the inner workings of the algorithms using WEKA.


Konstanz Information Miner (KNIME) is an open-source data analysis tool to collect and integrate data and extract insights. The tool has a graphical interface, so coding is not essential to get the most out of this tool. The tool helps collect data, generate and deploy models, and start production.


It is a free Python machine learning library. The library expands on NumPy, SciPy, and Matplotlib. It is an efficient tool for data mining and data analysis.  Scikit-Learn provides an array of supervised and Unsupervised ML algorithms such as classification, regression, time-series, clustering, support vector machine, dimensional reduction, random forests, and k-neighbors.

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