Data Science has impacted a lot of businesses from different industries. While data science has managed to come so far, becoming “the sexiest job of the 21st century”, there is one more technology that is gaining prominence.
Today, automation is not only limited to sectors like robotics, but it also collaborating with other domains to make things easy for techies and one such domain is data science. There are a large number of companies coming up with tools and products for data science domain. In this article, we shall look at some of these automation tools that one data science professional can use.
There are several machine learning algorithms that can be used right off the shelf, and many of these methods are implemented in the Weka package. However, each of these ML algorithms has its own hyperparameters that can drastically change their performance, and there are a staggeringly large number of possible alternatives overall. This is where Auto-Weka comes into the scenario.
Initially released in 2013, Auto-WEKA considers solving the problem by simultaneously selecting a learning algorithm and setting its hyperparameters. It also solves the problem using Bayesian optimisation. Auto-Weka is also focused on helping non-expert users to more effectively identify ML algorithms and hyperparameter settings appropriate to their applications.
Developed by Sparkcognition , a company that builds AI systems to advance the most important interests, Darwin is another next go-to tool for solving data science problems at scale. It is an automated model building tool that allows its users to go from data to the model in significantly less time than traditional methods. Also, it enables rapid prototyping of scenarios and productive extraction of insights.
Talking about how this tool works, the tool uses a patented approach based on neuroevolution that custom builds model architectures to ensure the best fit for the problem at hand.
DataRobot is an advanced Enterprise AI platform. The platform incorporates knowledge, experience and best practices of some of the world’s leading data scientists. Talking about automation, DataRobot’s Automated Machine Learning platform help ML developers automate the creation of machine learning models with unprecedented transparency in order to help understand and trust the predictions they make. The platform is equipped with different types of regression techniques, ranging from the simplest to complicated statistical classic regression models. Furthermore, one of the best things about this platform is the fact that it can also solve simple problems with up to 100 different categories.
DataRobot has been a sought after platform for data science professionals since the get-go. To know more about this platform, you can check out their official product site .
When it comes to machine learning automation, H2O has emerged as a leader. It is an open-source, distributed in-memory machine learning platform with linear scalability. The platform is created in such a way that it supports most of the widely used statistical & machine learning algorithms.
One of the best things about this platform is that it has an industry-leading AutoML functionality that automatically runs through all the algorithms and their hyperparameters to produce a leaderboard of the best models.
Feature engineering is considered to be one of the most important, most time-consuming and challenging for data science professionals. dotData that packs the best-in-class AI capabilities works towards automating it. Simply put, the company is solely focused on democratising and automating the entire data science workflow.
Compared to the traditional process, where it can take months between identifying a use case to getting pipelines into production, this AI/ML platform helps in executing complex data science projects with speed, and at scale.