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This post is the continuation of Project approach for a Machine Learning project, and it focus on the schedule of a given project on the context of machine learning. The creation of project schedules is something that can be done if you are able to estimate the efforts and you know the availability of the … Read more
Background and goal This post covers different aspects of a Machine Learning (ML) project, looking at it from the project / program manager point of view. This post is being updated as new feedback is received and new experiences are lived. This post is being divided in different posts as its so large to digest. … Read more
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
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
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
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
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
This code is the result of some work I have done on LSTM. Purpose: predict the next day price with the use of a LSTM neural network and just one property. How: I have normalized the data, I have used just 1 parameter as input (close price), I have built the code using basic functions … Read more
During year 4 – Q4 I have focused on the writing of books, and I still have to finish the English version of the “Cuaderno de trabajo para dibujar estrategias“. I will focus on Wardley Maps and completing my PMP renewal (I have 55 PDUs to complete). The plan So the V2MOM for this quarter will be: Vision: Work … Read more
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