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:
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:
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…
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.
A series of articles about Azure Machine Learning SDK and how to use the SDK to build an end-to-end ML pipelines.
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…
Solution Architect and Senior Software Engineer who has a great passion in machine learning, data science and DevOps.