Automated machine learning represents a fundamental shift in the way organizations of all sizes approach machine learning and data science. Applying traditional machine learning methods to real-world business problems is time-consuming, resource-intensive, and challenging. It requires experts in the several disciplines, including data scientists – some of the most sought-after professionals in the job market right now.
Automated machine learning changes that, making it easier to build and use machine learning models in the real world by running systematic processes on raw data and selecting models that pull the most relevant information from the data – what is often referred to as “the signal in the noise.” Automated machine learning incorporates machine learning best practices from top-ranked data scientists to make data science more accessible across the organization.
DataRobot automates the entire modeling lifecycle, enabling users to quickly and easily build highly accurate predictive models. DataRobot enables users to build and deploy highly accurate machine learning models in a fraction of the time it takes using traditional data science methods.
Data Science Machine is an end-to-end software system that is able to automatically develop predictive models from relational data. The Machine was created at the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. The system automates two of the most human-intensive components of a data science endeavour: feature engineering, and selection and tuning of the machine learning methods that build predictive models from those features.
AutoML is a tool from Google that automates the process of developing machine learning algorithms for various tasks. It’s user-friendly, fairly simple to use, and completely open-source. Best of all, Google‘s always updating it.
AutoML-Zero – designed to create a population of 100 “candidate algorithms” by combining basic random math, then testing the results on simple tasks such as image differentiation. The best performing algorithms then “evolve” by randomly changing their code.
Auto-sklearn frees a machine learning user from algorithm selection and hyperparameter tuning. It leverages recent advantages in Bayesian optimization, meta-learning and ensemble construction.
Azure Automated Machine Learning – cloud-based environment for training, deploying, automating, managing and tracking ML models. Automated machine learning picks an algorithm and hyperparameters for you and generates a model ready for deployment.
AutoKeras – automatic machine learning system based on Keras. It is developed by DATA Lab at Texas A&M University. The goal of AutoKeras is to make machine learning accessible for everyone.