After having decided on the best type of machine learning model and building it, we need to be aware that a working model will probably have to deal with different patterns of data from those used to train it. This … Read More
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Guide for monitoring machine learning models
In the dynamic landscape of data science, building and deploying machine learning models is just the beginning. To ensure sustained success and optimal performance, continuous monitoring of these models is crucial. Model monitoring in the data science pipeline involves tracking, … Read More
Data Science Assessment: how to analyze a project’s viability
A importância dos dados é amplamente reconhecida em vários setores e saber aproveitá-los representa um fator essencial que pode trazer uma vantagem competitiva substancial ou mesmo transformar a estrutura fundamental de uma empresa. A área da Data Science tem uma … Read More
Machine Learning model deployment: Why it matters and how to do it
You’ve spent weeks, maybe months, perfecting your machine learning model. The data is clean, the features are engineered and the model’s performance is stellar. But none of that really matters until your model is deployed – until it’s out there, … Read More
Data Science assessment: how to create machine learning models
In the vast landscape of data science, where information converges into insights, the journey of a data scientist is akin to that of an alchemist. Each dataset holds the promise of revealing hidden patterns, guiding decisions, and shaping the future. … Read More