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
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…
Data Science: Complex and Time-Consuming Data science is at the heart of what many are calling the fourth industrial revolution. Businesses leverage Artificial Intelligence (AI) and Machine Learning (ML) across multiple industries and multiple use-cases to make more intelligent decisions and to accelerate decision-making processes. Data scientists play central roles in this revolution. However, according to a 2018 study published by LinkedIn, there is a national shortage of over 150,000 data science-related jobs. This severe shortage means that the race to improve the productivity of data scientists is leading to some exciting new technologies. One of the primary challenges is the sheer complexity, iterative, and highly manual nature of the data science process. Data scientists must sift through scores of raw data, typically found in highly complex systems with hundreds of tables. Integrating and transforming those tables to create "feature tables" is at the heart of the entire process.…
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.…
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…
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