DotData, a San Mateo, California-based provider of automation tools that operationalize data science processes, today announced it has raised $23 million in a series A funding round led by JAFCO, with participation from Goldman Sachs. The fresh capital brings its total raised to $43 million, following a $20 million seed round in April 2018. CEO Ryohei Fujimaki said the funds will bolster the company's ongoing sales, marketing, and product development efforts. "We are pleased with the confidence our investors show in our vision, team, product, and ability to execute and expand market share," said Fujimaki, who founded DotData in 2018 with Kusumura Yukitaka, Masato Asahara, and Yusuke Muraoka. "Our company's rapid growth over the past 18 months signals a significant market demand for our unique data science automation platform. These funds will enable us to accelerate product development and innovation to continue bringing transformational value to our customers."
As data and the business problems that can be addressed by it proliferate, our ability to analyze them is falling behind. We don't have enough data scientists, we can't create enough good models, and we can't get them into production. Enter automated machine learning (AutoML), which offers substantial potential for solving the problem. This powerful set of tools can help with a wide variety of ML activities, including preparing data for analysis, performing feature engineering, automatically generating well-fitting models using the best algorithm, and generating code or APIs to help deploy the model into production. AutoML has been around in some form since the mid-1990s, but it didn't really take off until the past few years.
Machine learning may seem like a mysterious creation to the average consumer, but the truth is we're surrounded by it every day. ML algorithms power search results, monitor medical data, and impact our admission to schools, jobs, and even jail. Despite our proximity to machine learning algorithms, explaining how they work can be a difficult task, even for the experts who designed them. In the early days of machine learning, algorithms were relatively straightforward, if not always as accurate as we'd like them to be. As research into machine learning progressed over the decades, the accuracy increased, and so did the complexity.
For impactful machine learning, data scientists first need clean, structured data. That's where feature engineering comes in -- to refine data structures that improve the efficiency and accuracy of machine learning models. Ryohei Fujimaki, Ph.D., CEO and founder of dotData, a data science platform, said, "Features are, without question, even more critical than the machine learning algorithm itself." Poor quality features will result in a failure of the machine learning algorithm, he said. On the other hand, high-quality features will allow even simple machine learning algorithms like linear regression to perform well.
The popularity of AI and ML have wide-reaching effects on your enterprise. Here are three important trends driven by AI to look out for next year. As the need for additional AI applications grows, businesses will need to invest in technologies that help them accelerate the data science process. However, implementing and optimizing machine learning models is only part of the data science challenge. In fact, the vast majority of the work that data scientists must perform is often associated with the tasks that preceded the selection and optimization of ML models such as feature engineering -- the heart of data science.