Data Science + Design Thinking: a Perfect Blend to Achieve the Best User Experience
  It’s one thing to rely on artificial intelligence, machine learning, and big data to make your product smarter.  And, quite another to build a product that’s so intuitive and easy-to-use that your customer falls in love with it. That’s the beauty of data science + design thinking. It’s... Read more
The Benefits of Cloud Native ML And AI
As big data gets more complex, companies are struggling to accommodate the storage and computing needs of average organizations, much less massive enterprises. This is where cloud-native ML and AI comes into play. What Does Cloud Native Mean? Your computing power is limited. No matter what kind of hardware... Read more
Logistic Regression with Python
Logistic regression was once the most popular machine learning algorithm, but the advent of more accurate algorithms for classification such as support vector machines, random forest, and neural networks has induced some machine learning engineers to view logistic regression as obsolete. Though it may have been overshadowed by more... Read more
Creating Multiple Visualizations in a Single Python Notebook
For a data scientist without an eye for design, creating visualizations from scratch might be a difficult task. But as is the case with most problems, a solution awaits thanks to Python. Those drawn to using Python for data analysis have been spoiled, as more advanced libraries have made... Read more
What is TensorFlow?
It would be a challenge nowadays to find a machine learning engineer who has heard nothing about TensorFlow. Initially created by Google Brain team for some internal purposes, such as spam filtering on Gmail, it was open-sourced in 2015 and became the most popular deep learning framework in the... Read more
5 Mistakes You’re Making With DataOps
Data is the driver for just about every modern business, and as companies consume more data more intelligently, there’s a need for better community and higher buy-in. DataOps stands to do to data what DevOps did to development.   [Related Article: Data Ops: Running ML Models in Production the... Read more
10 Best Data Science Platforms
A data science platform can change the way you work. It’s more than just a tool, it’s a way to wrangle data and turn every member of your team into a high performing unit, capable of pivoting and scaling without missing a beat. The right one is transformative to... Read more
7 Steps to Go From Data Science to Data Ops
Not too long ago, data operation wasn’t on the radar, but now that it’s all people talk about, how can you move efficiently from data science to data ops? Gil Benghiat, co-founder of Data Kitchen, shares seven steps to do just that. [Related Article: The Difference Between Data Scientists... Read more
Introduction to R Shiny
Alyssa is a speaker for ODSC East 2019 this April 30 to May 3! Attend her talk “Data Visualization with R Shiny.” What is R Shiny? Shiny is an R package that enables you to build interactive web apps using both the statistical power of R and the interactivity... Read more
The Anatomy of K-Means Clustering
Let’s say you want to classify hundreds (or thousands) of documents based on their content and topics, or you wish to group together different images for some reason. Or what’s even more, let’s think you have that same data already classified but you want to challenge that labeling. You... Read more