Ask data engineers about the most frustrating part of their job and the answer will most likely include “data preparation.” Talk to a data scientist about the AI/ML workflow and what bogs them down, the answer invariably will be feature engineering. Analytics and data science leaders are well aware of the limitations of current AI/ML development platforms. They often lament about their team's ability to only manage a few projects per year. BI leaders, on the other hand, have been trying to embed predictive analytics in their dashboards but face the daunting task of learning how to build AI/ML models. Automated machine learning (AutoML) was built specifically to address some of the challenges of data science - the underlying practice at the heart of both problems. Like every new technology, there is a lot of confusion surrounding AutoML. Here are the top 5 misconceptions about AutoML: 1. AutoML means selecting the algorithms…
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.…
