Project approach for a Machine Learning project

The project approach is defined by the lifecycle of the solution, and here I will focused just on the machine learning side of the project (other sections as infrastructure, education, operations…) will not be reviewed here. What is the lifecycle of a Machine Learning solution? The lifecycle is sequential and in reality is can be … Read more

Considerations for the project scope of a Machine Learning project

Let’s start with the basic questions What is the business problem to be solved? What is the situation AS-IS? What are the current pain points you are facing? How are you attending these pain points? Are the causes of the problem identified? What is the problem impact? What is the desired situation TO-BE? What are … Read more

Machine Learning project: Agile or Waterfall approach?

This question is so easy: agile approach. Why? Because it recognizes that the construction of the solution requires different loops. Reason 1: ML models change overtime Machine Learning projects are supported on ML models, and models change overtime. Why do a model change overtime? A model change overtime because the data used to train the … Read more

AWS Sagemaker

These are some notes about the basis of Sagemaker Sagemaker services SageMaker Neo optimizes the trained model and compiles it into an executable. Taking the target hardware where the model will be run as input; the compiler uses a ML model to apply performance optimizations on your model. Ground truth makes easy to label data. … Read more

CRISP-DM methodology

The cross-industry standard process for data mining or CRISP-DM is an open standard process framework model for data mining project planning, created in 1996. The process of CRISP-DM is into 6 phases or components: Business understanding – What does the business need? Data understanding – What data do we have / need? Is it clean? Data preparation – How do we organize … Read more

Box-cox transformation

These are reminder notes about Box-cox transformation. One of the problems that box-cox transformation tries to solve is “heteroscedasticity” (non-constant variance). This article explains the problem where you can apply box-cox transformation to solve it: https://blog.minitab.com/en/applying-statistics-in-quality-projects/how-could-you-benefit-from-a-box-cox-transformation SciPy has added an inverse Box-Cox transformation: https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.inv_boxcox.html Does Box-cox always work? The answer is NO. Box-cox does not … Read more