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…
The end-to-end process for launching a data science project is daunting - and many enterprise projects never make it to production. The process is similar in most organizations and consists of: Data collection, last mile ETL, feature engineering, and machine learning. However, while the process is understood by most teams, the actual execution is very complex and involves a high-level of operational risk.We recently published a complete guide to operationalizing data science. In this guide, we identified five complex issues to be addressed, for a business to derive value from operationalizing data science. Highlights from the paper: Issue 1: Quality There are two groups in the data science process who are not aligned operationally:1) Data engineers build data pipelines with SQL or GUI-based tools, 2) Data scientists build machine-learning scoring pipelines using Python or R. Software engineers must often reimplement much of the work from these two groups before production…
