As originally seen on Forbes Cognitive World, our CEO - Ryohei Fujimaki PhD was a primary contributor to this article. In case you've missed it, we've reposted the Original Article below. It's an AutoML WorldThe world of AutoML has been proliferating over the past few years - and with a recession looming, the notion of automating the development of AI and Machine Learning is bound to become even more appealing. New platforms are available with increased capabilities and more automation. The advent of AI-powered Feature Engineering - which allows users to discover and create features for data science processing automatically - is enabling a whole new approach to data science that, seemingly, threatens the role of the data scientist. Should data scientists be concerned about these developments? What is the role of the data scientist in an automated process? How do organizations evolve because of this newfound automation?AutoML 2.0, More Automation for…
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 Related Articles
conclusion from last week...Part 2 Beyond AutoML : Data Science Automation While the rise of AutoML platforms has provided for faster execution of "test and learn" ML development, it has also brought about additional challenges. In most ML and data science projects, ML development is only one part of the process. The earlier stages of the process that require handling multiple raw tables and manipulating them based on in-depth domain knowledge to create flat, aggregated feature tables is a far more complicated and time-consuming challenge. The data and feature engineering process in enterprise data science has to deal with such different data as relational, transactional, temporal, geo-locational, and text data, which never starts from a single, flat, aggregated and cleansed table. Data science automation provides for a full-cycle automation process that includes data and feature engineering, in addition to standard AutoML. The ability to automatically generate features from massive and…
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…
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…
Data science is now a major area of technology investment, given its impact on: customer experience,revenue,operations,supply chain,risk management, andmultiple 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. Related Articles
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, andexpertise. 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, andmodel 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 has long been thought that this…
