Machine Learning has been serving several industries for the past many years. It has enabled businesses to work easily with data. Moreover, the acceleration in the adoption of ML tools has evolved with time making it even easier to use today. Using AutoML tools, the act of gathering data and turning it into actionable insights has become much convenient. People with even less knowledge of data science and machine learning can work with these automated tools.
In 2013, DataRobot invented automated machine learning — and an entirely new category of software as a result. Unlike other tools that provide limited automation for the complex journey from raw data to return on investment, the company’s Automated Machine Learning product supports all of the steps needed to prepare, build, deploy, monitor, and maintain powerful AI applications at enterprise scale.
DataRobot’s AutoML product accelerates the productivity of your data science team while increasing your capacity for AI by empowering existing analysts to become citizen data scientists. This enables your organization to open the floodgates to innovation and start your intelligence revolution today.
Google Cloud AutoML
Cloud AutoML is a suite of machine learning products that enables developers with limited machine learning expertise to train high-quality models specific to their business needs. It relies on Google’s state-of-the-art transfer learning and neural architecture search technology.
Cloud AutoML leverages more than 10 years of proprietary Google Research technology to help your machine learning models achieve faster performance and more accurate predictions.
dotData was born out of the radical idea, unique among machine learning companies, that the data science process could be made simple enough for just about anyone to benefit from it. Led by Dr. Ryohei Fujimaki, a world-renowned data scientist, and the youngest research fellow ever appointed in the 119-year history of NEC, dotData was created to accomplish this mission. The company values its clients and works hard to provide the highest value possible in Automated Machine Learning (AutoML).
dotData was first among machine learning companies to deliver full-cycle data science automation for the enterprise. Its data science automation platform speeds time to value by accelerating, democratizing, and operationalizing the entire data science process through automation.
Splunk’s original version started off as a tool for searching through the voluminous log files created by modern web applications. Since then it has grown to analyze all forms of data, especially time-series and others produced in sequence. The latest newest versions of Splunk includes apps that integrate the data sources with machine learning tools like TensorFlow and some of the best Python open-source tools. Such modern tools offer quick solutions for detecting outliers, flagging anomalies, and generating predictions for future values.
H2O has made it easy for non-experts to experiment with machine learning. In order for machine learning software to truly be accessible to non-experts, the company has designed an easy-to-use interface that automates the process of training a large selection of candidate models. H2O’s AutoML can also be a helpful tool for the advanced user, by providing a simple wrapper function that performs a large number of modeling-related tasks that would typically require many lines of code, and by freeing up their time to focus on other aspects of the data science pipeline tasks such as data-pre-processing, feature engineering and model deployment. It can be employed for automating the machine learning workflow, which includes automatic training and tuning of many models within a user-specified time-limit.