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Why AutoML Isn’t Enough to Democratize Data Science 

In this contributed article, Noam Brezis, co-founder and CTO of Pecan AI, explores that because AutoML was born out of academia, in its current incarnation it is only built to simplify the model building process. This is likely the reason why existing AutoML solutions are finding challenges with scaling. Plus, these types of solutions are not incorporating the aspects of data prep and feature engineering, nor the model training, deployment and monitoring, which as a result slowing down adoption of AI within the enterprise and curtailing the impact it can deliver.

AutoML- The Future of Machine Learning

In this contributed article, Ankush Gupta and Kavya Shree of FischerJordan, explore the scope, use cases and challenges of AutoML and how data scientists and AutoML can have a future together. The authors discuss the causes driving the use of AutoML, the benefits and challenges associated, and major providers in the space. They conclude by analyzing the parts of the data science and ML process that can/cannot be automated and if AutoML will replace data scientists / both will go hand-in-hand.

Research Highlights: AutoDC: Automated Data-centric Processing

Most AutoML solutions are developed with a model-centric approach, however, according to a research paper, “AutoDC Automated Data-centric Processing,” that was accepted into last year’s highly selective NeurIPS conference on the development of an automated data-centric tool (AutoDC), it was found to save an estimated 80% of the manual time needed for data set improvement – typically a bespoke and costly process.

Is Data a Differentiator for Your Business? If So, Traditional OCR Cannot Be An Answer

In this contributed article, Ankur Goyal, CEO and co-founder of Impira, discusses how to make the most of your OCR investment with AutoML. Automated Machine Learning is a nascent AI technology that exposes the power of machine learning (ML) to a much broader audience than data scientists and technologists.

JADBio Provides AutoML for BioMed Data

JADBio is an AI startup company working with BioMed data. This remarkable team, headed by Prof. Ioannis Tsamardinos, has created an automated machine learning (AutoML) platform designed for life scientists. No Coding. No Statistics. No Math. No Problem … just add data.

How AutoML is Accelerating Time to Value on Data Science Use Cases

In this contributed article, Chetan Alsisaria, CEO & Co-Founder of Polestar Solutions & Services Pvt Ltd., takes a closer look at a popular ML use case and how decision making can be improved by applying ML and deriving value from it. Investing in data science processes can add value in several ways – influencing critical decisions in recruitment, marketing, sales, supply chain, operations and many more.

How Companies Can Gain Value From Small Data

In this contributed article, Shanif Dhanani, CEO of Apteo, highlights four ways to circumvent the need for big data. While big data can fuel astonishing results, organizations can gain value from “small data” as well.

Tecton.ai Launches with New Data Platform to Make Machine Learning Accessible to Every Company

Tecton.ai emerged from stealth and formally launched with its data platform for machine learning. Tecton enables data scientists to turn raw data into production-ready features, the predictive signals that feed machine learning models. Tecton is in private beta with paying customers, including a Fortune 50 company.

DataRobot Opens Up its Platform for COVID-19 Response Efforts

DataRobot, a leader in enterprise AI, announced it will provide the DataRobot enterprise AI platform free of charge to those interested in using it to help with the COVID-19 virus response effort. In collaboration with Amazon Web Services (AWS), DataRobot’s COVID-19 response program provides free access to DataRobot’s automated machine learning.

Machine Learning for All: the Democratizing of a Technology

Our friends over at H2O.ai have produced a short new eBook “Machine learning for all: the democratizing of a technology” which covers machine learning features and automatic AI solutions, and how organizations can benefit from using them.