Learn how to find the optimal number of positions needed to manage an incoming traffic.

Photo by Lisanto on Unplash

Finding the right number of positions to use in a queue system, has been a study case for a long time now, it has applications in several fields and industries, for example finding the optimal number call centers agents, deciding the number of bankers in a support station, network traffic analysis and so on.

There are several methods to analyse this problem, in this article, we are going to take a look how to solve it using Erlang C with python’s Pyworkforce package.

1. Queue System

MLflow main components. Image by Databricks¹.

In this post, we are going through the main aspect of MLflow, an open source platform to manage the life cycle of machine learning models.

We’ll cover:

  • Basic setup of mlflow
  • Experiments
  • Logging metrics and parameters
  • Custom artifacts
  • Models registry
  • Model predictions

MLOps is a methodology for enabling collaboration across data scientist, it helps to gain control over different models versions, multiple experiments within the same problem and models management and deployment. There are several both open source and commercial solutions to approach this problem, we are going to take a look on MLflow.

According to the MLflow’s site:


In this post, you will learn how to:

* Train and save a machine learning model using Sckit-learn
* Create an API that can take incoming predictions requests
* Get your API running using Docker
* Test your API performance with Locust

Machine learning is definitely one of the hottest topics in data science, there is a lot of resources about how to train your model, from data cleaning, feature selection and how to choose between a lot of candidates and fine tune them.

At this point, everything must be working great on your computer, but when it comes to…

Rodrigo Arenas

Making data science easy

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