Introduction
Automated machine learning (AutoML) is the future of machine learning. Building a machine learning model is much easier, by running the organized processes to collect raw data and sift through them to get the most relevant information; it incorporates data science and makes it readily available throughout the departments.
Importance of AutoML
Automated machine learning saves organizations time and money to create the capabilities themselves; this is because of its in baked knowledge of data experts. This improves ROI in data science and minimizes the time taken to capture value.
Recently, automated machine learning was at the disposal of organizations with vast resources. Automated machine learning is readily available; it enables organizations to roll out machine learning solutions without a challenge, consequently allowing businesses to focus on more demanding problems in data science.
Open-Source Automated Machine Learning Platforms
Here are six important open source tools for AutoML:
| Platform | Description |
| Auto Weka 2.0 | It is designed commonly for its tabular data use. |
| Auto-Sklearn | It mainly leverages previous advantages of meta-learning, Bayesian optimization and ensemble construction. |
| Auto-Keras | It is an advantageous tool for those with little or no knowledge in data science or machine learning. It provides a platform for deep learning tools and is fully automated. |
| TPOT | It follows scilkit learn API closely and is built on the skilt learning library. Its main use is classification and regression tasks. |
| TransmogrifAI | It is built on sparkML and Scala. Its main function is to automate machine learning models creation. |
| H2O AutoML | It formats the workflow of machine learning. It includes tuning and training of many models. |
Open-Source Machine Learning Platform
Although they are not so different from AutoML, they use similar tools.
Here are a few open-source tools for the machine learning platform. These include libraries for: python, java, JavaScript and Go among other programs.
| Platform | Description |
| Apache Mahout | Originally designed to work with Hadoop for running distributed applications. It makes scalability and efficient and fast. |
| Compose | Allows users to write a set of labelled data functions using python. |
| Core ML Tools | It integrates with the likes of Python machine learning libraries and tools. It can convert models from TensorFlow, Keras, PyTorch, ONNX, Caffe, LibSVM, Scikit-learn and XGBoost. |
| Cortex | It provides a conducive means to serve predictions from machine learning models using, PyTorch, Scikit-learn, Python and TensorFlow. |
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