Power BI - Improve your development process by using multiple environments
Learn in this article why and how to use different environments when developing products in Power BI.
Newsletter for February 2022
Ekimetrics is pleased to release its second Newsletter of 2022! This cheat sheet will brief you quickly about the latest news in the fields of Data Science, Machine Learning, Data Engineering and Application Development. Warning this one is particularly ruled by DeepMind so be prepared!
Demand Forecasting - Improving Supply Chain operations in uncertain times
Learn in this article how we industrialized a solution for warehouse demand forecasting to help Operations managers better allocate resources for more than 20.000 referenced products
Ekimetrics implements Data Mesh. Here is why, and why you should join us
This article describes one of the inspirational patterns that drive our projects’ data architectures, the Data Mesh.
Building a datalake - Part 1 - Usable, Useful, Used, or how to avoid dataswamp and empty shell traps
In this article, learn how to create your first datalake following best practices in order to make it robust, evolutive and central to your company's information architecture, as well as take advantage of the opportunities it presents.
Which AutoML platform to choose to start your Data Science project?
How do Automatic Machine Learning solutions work? What are the most popular AutoML solutions out there, and what do you need to know about them to make the right choice for your use case? Find answers to these questions and many others in this article.
Hackathon Stories - Ensuring access to affordable and clean energy
Read this article to see how and why Ekimetrics organize hackathons to foster creativity, innovation and team work in our Data teams. Also learn important tips for organizing your own hackathons.
Customer Journey Clustering - How to use sequential modeling techniques to handle customer journey data
Enrich your understanding of customer journeys through clustering using image embedding, autoencoders and unsupervised learning.
Introduction to Pyepidemics - epidemiological modeling in Python
We created Pyepidemics, an open-source library to simulate epidemics (SIR, SEIHDR, COVID19). We detail in this article what you can build with the library.