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AutoML and Beyond – Part 1

By dotData

With AutoML trending in data science, our CEO spoke at #Ai4Finance on data preparation, aggregating tables, feature engineering, the #AutoML process, and AutoML’s missing gaps.  We’ll post the Conclusion / Part 2 next Thursday.  Video: Part 1 – AutoML and Beyond. Share On [social_warfare ] Related Articles

Ai4 Finance: dotData CEO Promotes Automated Feature Engineering

By dotData

We are proud to share a few photos from the #Ai4Finance2019 New York Conference.  With such a great turnout, our founder and CEO Ryohei Fujimaki displayed many of the new #datascience #innovations where dotData is paving the way for #AutomatedFeatureEngineering. Related Articles

Automated Machine Learning vs. Data Science Automation [Infographic]

By dotData

A lot has been written over the past few years about AutoML. Automated Machine Learning is a rapidly growing category of software platforms in the field of data science. Looking at the world of data science strictly from the perspective of automating the machine learning component leaves a lot to be desired. 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. The automation of feature engineering is at the heart of data science. The infographic below shows a side-by-side comparison of how typical “AutoML” platforms can help the data scientist vs. data science automation: Made with Visme Infographic Maker OR Linked at:Infographic: data science automation vs automl

How Will Automation Change Enterprise Data Science? – Part 2

By dotData

continued from last week's post... dotData, Data Science Without The Headaches dotData is a brand new breed of AutoML product that provides what we call Full Cycle Data Science Automation. At the heart of our vision is the idea that the data science process should be fast, easy to perform, and easy to analyze and deploy, from raw business data to the business values. Our vision has led us to develop dotData Enterprise and dotData Py, two related platforms that leverage the same automation engine in uniquely different ways. dotData Enterprise is ideal for the citizen data scientist: fully automated, point-and-click driven, and ready to automate 100% of the data science process without requiring in-depth knowledge of how data science works. dotData Py, on the other hand, is ideal for data scientists. dotData Py provides a python library for Jupyter notebooks, one of the most popular data science platforms available.…

How Will Automation Change Enterprise Data Science? – Part 1

By dotData

According to a recent study by Dimensional Research, Over 96% of enterprise companies struggle with AI and Machine Learning (ML) projects. The reasons behind the incredibly high failure rates are numerous, but many are associated with shortages of staff, data that requires too much pre-processing to use appropriately, and a lack of understanding of the ML models on the part of business users. Organizations struggle with AI and machine learning, in large part, because projects take too long to complete, lab-generated results are often difficult to recreate in production environments, and the value derived by AI and ML projects is not clear enough.  AI & Machine Learning Automation - The Solution To All Our Problems? Recently, several startups have come to market with innovative platforms designed to "automate" the process of generating machine learning models. The promise of "AutoML" as it has become known, is that by accelerating the machine-learning…

2019: The Year of AI and Machine Learning

By dotData

2018 will be remembered, in many ways, as the year that disruptive emerging technologies began to reshape business models and change the economics of organizations. According to Gartner, digital business reached a tipping point last year, with forty-nine percent of CIOs reporting that their enterprises have already changed their business models, or are in the process of doing so. When CIOs and IT leaders were asked which technologies they expect to be most disruptive, artificial intelligence (AI) was the top-mentioned technology, with data and analytics taking second place. Based on my work leading more than 100 data analysis projects across a variety of industries, the combination of these technologies is hardly surprising; it’s an accurate reflection of what my colleagues and I see in data science on a daily basis. Data science is now a major area of technology investment for organizations, driven by the impact it can have on…

How Will Automation Tools Change Data Science?

By dotData

Data science is now a major area of technology investment, given its impact on: customer experience, revenue, operations, supply chain, risk management, and multiple other business functions. Data science enables a data-centric decision-making process for organizations.  It is accelerating digital transformation and AI initiatives.  According to Gartner, Inc. only 4 percent of CIOs have implemented AI, and only 46 percent have plans to do so.While investments continue to grow, many enterprises find it increasingly challenging to implement and accelerate data science practices.  This article provides an overview of recent trends in machine learning and data science automation tools.  It also  addresses how those tools will change data science. Read the full article "How Will Automation Tools Change Data Science" on KDnuggets, featuring Dr. Ryohei Fujimaki, CEO and founder of dotData.

2019 Predictions for Machine Learning

By dotData

Manufacturers are adept in gathering data from their operations, but don’t always know how to make the best use of the data collected.  Data science automation can provide the needed guidance. Dr. Ryohei Fujimaki, CEO and founder, of dotData, joins us to predict how machine learning will change data science over the next year.  Read the full article "2019 Predictions for Machine Learning" on Manufacturing.net for Ryohei's five (5) predictions for machine learning.

A Vision of Rapid, High-Quality Data Analysis for All Businesses

By dotData

Leveraging Data to be Competitive It is becoming increasingly important for enterprises to leverage data to be competitive. Yet, there are three challenges related to embracing data utilization that all businesses share: it takes time, advanced skills, and expertise. Together, these challenges make it difficult for enterprises to fully leverage their data for business growth.  Data analytics is not simply prediction by machine learning. Rather, it is a process involving many different steps, including: data preparation, feature engineering, machine learning, visualization, and model operationalization. Until now, completing this process for just a single project would have taken months. Moreover, a wide variety of highly-skilled personnel are needed for each step – such as domain experts, data scientists, data engineers, and BI engineers.  Additionally, processes and outcomes have tended to be highly dependent on the experience and intuition of each individual. Feature Engineering Made Easy For feature engineering in particular, it…