Global Blog

The edition of dbt coalesce was taking place in New Orleans. And we learned a ton about the analytics engineering landscape.

Snowflake | How we automated the management of an account with more than 50 users while complying with data governance standards

A guide on how to use counterfactual forecasting to estimate the cost-effectiveness of past in-store promotions in retail.

How to estimate the impact of channels between Sales and Marketing? The Media Mix Modeling is the solution, Statistics are the main resource.

After my 1st story on code optimisation to reduce my computing time by 90%, I was interested in knowing the CO2eq impact saved by my changes.

Jeffrey Kane, Senior Data Scientist, explains why ML Engineer should be in your data science projects from day one.

The field and future of data engineering is evolving quickly. Discover 3 major trends I see become prominent in the coming years.

This guide will help you figure whether Facebook Prophet is appropriate or not for your forecasting project.

String filters in Pandas is something you should avoid as the scalar_compare operator leads to performance bottlenecks.

Key 2021 data & ML trends… and what they mean for 2022

A step-by-step guide to ease datasets comparison via a ready-to-use Structured Query Language template

A deep-dive on how we built state of the art custom machine learning models to estimate customer propensity to buy a product using Google Analytics data.

Until now we have mainly talked about forecasting regular products that have been on the shelf for quite some time. But what about products that have been very recently launched ?

A guide to being aware of the risks you are exposed to and a few tips to protect against them.

This article is the third part of a series in which we go through the process of logging models using Mlflow, serving them on Kubernetes engine and finally scaling them up according to our application needs. Although this article could be used independently to test any API response, we recommend reading our two previous articles (part1 and part2) on how to deploy a tracking instance and serve a model as an API with Mlflow. In the following, we will be interested in the scalability issue and address it with few experiments to understand k8s cluster behavior and give recommendations on how to handle high loads.

25 October 2021 This article is the second part of a series in which we go through the process of logging models using Mlflow, serving them as an API endpoint, and finally scaling them up according to our application needs. We encourage you to read our previous article in which we show how to deploy a tracking instance on k8s and check the hands-on prerequisites (secrets, environment variables…) as we will continue to build upon them here. In the following, we show how to serve a machine learning model that is already registered in Mlflow and expose it as an API endpoint on k8s.

22 October 2021 MLflow is a commonly used tool for machine learning experiments tracking, models versioning, and serving. In our first article of the series “Serving ML models at scale”, we explain how to deploy the tracking instance on Kubernetes and use it to log experiments and store models.

22 september 2021 You need a baseline for your latest time series forecasting project? You want to explain the decision-making process of a predictive model to a business audience? You would like to understand if car prices are seasonal before buying a new one? We might have something for you! This article introduces Streamlit Prophet, a web app to help data scientists train, evaluate and optimize forecasting models in a visual way. Forecasts are made with Prophet, a fast and easily interpretable model.

27 July 2021 Here are some guidelines to build trustworthy machine learning solutions without falling into ethical pitfalls.