Blog

Cynthia Leung - August 02, 2022

Data science notebooks are powerful, flexible tools that data scientists use every day. But they are code-heavy linear workflows which do not properly address data scientists' need for multi-stakeholder collaboration, reproducibility, fast iterative discovery, and operational work to deploy. We explore a few ways notebooks fail data scientists here.

Paul Yang - June 01, 2022

Historically, Machine Learning algorithms were a bit painful to use, and required tedious human intervention in order to tune hyperparameters. Recent innovations in AutoML means that data scientists can now get better models in less time, by using new tools that support automatic exploration of how to assemble the best ML pipeline.

Tim Kraksa - March 27, 2022

Low-code tools are revolutionizing businesses, enabling citizen developers to create new business applications that drive innovation. Now, the same thing is starting to happen for citizen data scientists.

Benedetto Buratti - February 01, 2022

As organizations made data analytics a strategic priority, demand for data analysis outputs exceeded supply of trained data scientists. To bridge the gap, no code workflow platforms (KNIME, Alteryx…) were developed to make advanced data science easier, and give access to wide audiences.

Paul Yang - November 01, 2021

Move fast and break things — but still be data informed. Startups must tailor their data analytics practices to focus on on delivering strategic insights quickly. These are a few observations we’ve observed in our partnerships with startups, as Einblick helps lean organizations produce better analytics.

Paul Yang - October 06, 2021

While code can accomplish everything, there is a set of repetitive operations where visual-based no code operators will help every data scientist. In that way, no code operators are just the next logical extension of importing libraries.

Paul Yang - August 27, 2021

Why have advancements in Machine Learning (ML) imperfectly translated to better data driven decision making? How can business line stakeholders and data scientists bridge the gap between quality analysis and executed changes?

Benedetto Buratti & Paul Yang - March 01, 2021

In data science, there are many different versions of correctness. Accuracy itself can be highly misleading: We don't want accurate nuclear launch detection and we don't want accurate self driving cars.

Paul Yang - January 26, 2021

But it’s 2022 and it’s time to say goodbye to spreadsheets as the primary tool for data analysis. You should be able to work in a fast, collaborative space for business analysis, and harness innovations in AI/ML to quickly identify key drivers and even access predictive modeling.