New update in our blog by our Data Engineer Head Cristòfol Torrens.
An Airflow DAG can become very complex if we start including all dependencies in it, and furthermore, this strategy allows us to decouple the processes, for example, by teams of data engineers, by departments, or any other criteria.
In our new article Advanced Airflow. Cross-DAG task and sensor dependencies we share with you some ways to solve problems related to the complexity of data engineering itself. An example of an hypothetical case, we’ll see the problem and solve it.
You can read the full article below:
Seen on networks
Guide for Creating Machine Learning Pipelines using PySpark MLlib on Google Colab
This article focuses on exploring Machine Learning using Pyspark. It’s focus remains to choose the best model using cross-validation and hyperparameter tuning followed by making predictions on Pyspark.
Mimicking associative learning
Associative learning, a critical learning principle to improve an individual’s adaptability, has been emulated by few organic electrochemical devices.
The implementation of the associative learning circuit as well as the understanding of the non-volatile material represent critical advances for organic electrochemical devices in neuromorphic applications.
You can read the full at https://www.nature.com/articles/s41467-021-22680-5
AI adjusts prices according to expiration date
Wasteless, is the name of a startup that uses artificial intelligence to adjust the prices of all products about to expire so that the consumer buys them on time.
Its name makes it quite clear what the idea behind it is: spend less. By placing electronic price tags and making use of an AI to control everything, the supermarket can have product prices always updated in real time that are lower as the expiration date approaches.
And so far, the summary of week 18 of this 2021. We invite you to share this article with your contacts. See you in networks!