Pecan.ai, a startup that wants to help business analysts build machine learning models in an automated fashion, emerged from stealth today and announced an $11 million Series A. The round was led by Dell Technologies Capital and S Capital. Along with a previously unannounced $4 million seed round, the company has raised a total of $15 million. CEO Zohar Bronfman says he and co-founder Noam Brezis, whom he has known for more than a decade, started the company with the goal of building an automated machine learning platform. They observed that much of the work involved in building machine learning models is about getting data in a form that the algorithm can consume, something they've automated in Pecan. "The innovative thing about Pecan is that we do all of the data preparation and data, engineering and data processing, and [complete the] various technical steps [for you]," Bronfman explained.

In this post, we will explore how to use automated machine learning (AutoML) to create new machine learning models over your data in SQL Server 2019 big data clusters. SQL Server 2019 big data clusters make it possible to use the software of your choice to fit machine learning models on big data and use those models to perform scoring. In fact, Apache SparkTM, the popular open source big data framework, is now built in! Apache SparkTM includes the MLlib Machine Learning Library, and the open source community has developed a wealth of additional packages that integrate with and extend Apache SparkTM and MLlib. Manually selecting and tuning machine learning models requires familiarity with a variety of model types and can be laborious and time-consuming.

Paolo Tamagnini, Simon Schmid, and Christian Dietz are data scientists and software engineers at KNIME. Is it possible to fully automate the data science lifecycle? Is it possible to automatically build a machine learning model from a set of data? Indeed, in recent months, many tools have appeared that claim to automate all or parts of the data science process. Could you build one yourself?

Paolo Tamagnini, Simon Schmid, and Christian Dietz are data scientists and software engineers at Knime. Is it possible to fully automate the data science lifecycle? Is it possible to automatically build a machine learning model from a set of data? Indeed, in recent months, many tools have appeared that claim to automate all or parts of the data science process. Could you build one yourself?

Auto ML is a powerful concept for the next generation of AI tools. It's part of a general movement to extend AI-based automation to data science. Just within the past several years, the possibilities created by machine learning and deep learning have exploded across many industries. Unfortunately, machine learning is difficult and tedious, and there aren't enough qualified practitioners. Although many companies are envisioning a future of ubiquitous AI, a lack of data scientists experienced with machine learning will prevent them from making that vision a reality.

The power of machine learning comes at a price. Once you have the skills, the toolkit, the hardware, and the data, there is still the complexity involved in creating and fine-tuning a machine learning model. But if the whole point of machine learning is to automate tasks that previously required a human being at the helm, wouldn't it be possible to use machine learning to take some of the drudgework out of machine learning itself? Short answer: a qualified yes. A collection of techniques, under the general banner of "automated machine learning," or AML, can reduce the work needed to prepare a model and refine it incrementally to improve its accuracy.

H2O.ai, creator of applications for making machine learning accessible to business users, has introduced a product intended to allow business users familiar with products like Tableau to extract insights from data without needing expertise in deploying or tuning machine learning models. Driverless AI, currently in beta, is billed by H2O.ai as an "expert system for AI" -- a way to automate the kinds of expertise that data scientists bring to developing machine learning models. The target audience is non-expert users, who can take datasets and run GPU-accelerated ML algorithms against them to extract useful results, without understanding the ins and outs of data science. In addition to business users eager to leverage ML in their organizations but lack expertise, H2O is also pitching Driverless AI to data scientists. H2O considers Driverless AI to be a way for expert users to automate some of the more tedious processes of analyzing a dataset, such as selecting which of various automatically trained models is the best fit for a given dataset.