How to Scale an AI Platform: It’s Not Just About “Speeds and Feeds”
There are many ways to achieve scale in AI and machine learning (ML) — scale up, scale out, elastic scale. But taking a more granular
There are many ways to achieve scale in AI and machine learning (ML) — scale up, scale out, elastic scale. But taking a more granular
Instead of focusing on “Automated Machine Learning” or AutoML, maybe we should focus on “Automated Data Management” or AutoDM? You probably know that feeling. You
Suppose you could develop an AI application without having to lift a finger. To some degree that is the goal of Automated Machine Learning, known
An AI-focused neural network software engineer walks into a data shop says hello to the shopkeeper. “I’ll have two data preparation functions, one testing and
Data preparation is an important part of a predictive modeling project. Correct application of data preparation will transform raw data into a representation that allows
So before we begin, perhaps a little bit about myself. I have been working in data science since 2004 when I was in my second
How does the scikit-learn machine learning library for Python compare to the mlr package for R? Following along with a machine learning workflow through each
Summary: If you’re still writing code to clean and prep your data you’re missing big opportunities for efficiency and consistency with modern data prep platforms.
Feature selection/creation/transformation is one of the most commonly overlooked areas in model building by aspiring Data Scientists. Usually, the task of model building gets reduced
Feature selection is one of the most important tasks in machine learning. Learn how to use a simple random search in Python to get good
The job role of a data analyst in today’s tech market is broadly defined. In general, experts talk about data analysts as people who collect
In machine learning, a feature is another word for an attribute or input, or an independent variable. Feature engineering is a process of preparing inputs