“Data driven” decisions aren’t innovative decisions
If you want to innovate new solutions, you can't rely on data about existing solutions.
ML and AI consulting-as-a-service (Ep. 542)
The home team talks with Jaclyn Rice Nelson, cofounder and CEO of Tribe AI, about the explosion of hype surrounding generative AI, what it’s like to work at a startup after working at Google, and how Tribe is leveraging the power of a specialist network.
Eira May
Senior Content Marketer
Stop aggregating away the signal in your data
By aggregating our data in an effort to simplify it, we lose the signal and the context we need to make sense of what we’re seeing.
Podcast 406: Making Agile work for data science
Data scientists and engineers don’t always play well together. We discuss an approach to your tech stack that can bring them together.
How often do people actually copy and paste from Stack Overflow? Now we know.
April Fool's may be over, but once we set up a system to react every time someone typed Command+C, we realized there was also an opportunity to learn about how people use our site. Here’s what we found.
Building a QA process for your deep learning pipeline in practice
Deep learning models still need testing, but many of the common testing approaches don't apply. But with the right methods, you can still make sure your pipeline produces good results.
Why your data needs a QA process
At this point, most software engineers see the value of testing their software regularly. But are you testing your data engineering as well?
Level Up: Mastering statistics with Python – part 5
Rather than dig into complex math or over-simplify by using a pre-written function, we'll write our own binomial test function, primarily using base Python. In the process, we'll learn more about how hypothesis testing works and build intuition for how to interpret a p-value.
Level Up: Mastering statistics with Python – part 2
Investigate a dataset with summary statistics and some basic data visualizations using the Python libraries NumPy, pandas, matplotlib, and Seaborn.
Level Up: Mastering statistics with Python
In today’s tech industry, statistics and data science are becoming increasingly important and valuable skills.
Ben Popper
Director of Content
How to put machine learning models into production
The goal of building a machine learning model is to solve a problem, and a machine learning model can only do so when it is in production and actively in use by consumers. As such, model deployment is as important as model building.
