From Making Sense of Edge Computing by Cody Bumgardner
Conceptually, edge computing is concerned with when it’s best to migrate computational functionally toward source of data and when it is best to move the data itself. This abstract concept of function versus data migration drives not only the fundamental motivations of edge computing, but also the broader field of distributed systems. The act of distributing processes makes even the simplest tasks more complicated.
From Interpretable AI by Ajay Thampi
From Fighting Churn with Data by Carl Gold
This article discusses using machine learning algorithms to forecast churn risks.
From Fighting Churn with Data by Carl Gold
This article discusses using machine learning algorithms to forecast churn risks.
From Azure Data Engineering by Richard Nuckolls
This article discusses using Azure Data Lake Analytics (ADLA) for batch queries.
From Grokking Streaming Systems by Josh Fischer and Ning Wang
| From AI as a Service by Peter Elger and Eóin Shanaghy
This article discusses how to add AI to existing platforms. |
From Grokking Artificial Intelligence Algorithms by Rishal Hurbans
What you’ll learn in this article:
§ The lifecycle of a genetic algorithm.
§ Designing and developing a genetic algorithm to solve problems.
§ The parameters for configuring a genetic algorithm lifecycle based on different scenarios, problems, and data sets.
From Data Pipelines with Apache Airflow by Bas P. Harenslak and Julian Rutger de Ruiter
This article gives an overview of the Apache Airflow architecture
From Making Sense of Edge Computing by Cody Bumgardner & Caylin Hickey
