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.