In the last post, we discussed the planning poker as one of the famous agile estimation techniques. Let’s dig into more details about that estimation technique. When using this technique, we have to consider the following:

  • This technique is consensus-driven approach and not voting, so don’t take the estimation of the majority as the final estimation of the user story.
  • Estimate some few user stories at the beginning to make them as a baseline or reference for the team’s estimation is a good practice. It makes the estimation process easier, faster.
  • Don’t take the average of the estimations given by…

Source: Wikipedia

I really like what Mike Cohn explained in one of his posts about story points, story points are about effort, despite so, when calculating the story points there are some factors we have to consider; these factors affect our estimation directly:

  • Complexity: how complex the user story is.
  • Risk: what are the anticipated risks related to this user story or feature?
  • Uncertainty (doubt): how uncertain the requirements of this user story are?

These considerations can be incorporated when estimating the story points, but only to the extent they affect the expected effort to make the user story done. We don’t…


Story Points

The estimation using story points is a relative sizing, so you can usually refer to the smallest or the easiest user story to be estimated as a reference story, then you relatively size the other stories. The scale used for the story points is exponential scale (such as Fibonacci sequence) to encourage team restricting their estimates to lower ranges. If we pick up large numbers on the scale, this implies high level of uncertainty and indicates that those user stories are very big (Epics), so we have to break them down to refine their estimates and lower the uncertainty.

Relative…


A series of articles about Azure Machine Learning SDK and how to use the SDK to build an end-to-end ML pipelines.

Graphic Credit: Microsoft for Azure Machine Learning

Introduction

Azure ML provides us with great tools to build, fine-tune and operationalize machine learning models. Azure ML service covers supervised machine learning scenarios either classification or regression problems, in addition to the unsupervised problems such as clustering techniques and dimensionality reduction.

This article is part of a series about Azure ML SDK and how to use it to build an end-to-end machine learning pipelines at scale.

In the first part of this article, I will give an introduction about…

Osama Mosaad

Solution Architect and Senior Software Engineer who has a great passion in machine learning, data science and DevOps.

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