Classificando objetivos e técnicas de Feature Engineering

Neste exercício, você vai conhecer algumas técnicas essenciais de preparação de dados e os principais objetivos de feature engineering. Você precisará classificar os itens fornecidos nas categorias corretas, como técnica de preparação de dados ou objetivo de feature engineering. Entender esses conceitos vai ajudar você a melhorar seus modelos de machine learning e aproveitar melhor seus dados.

Este exercicio faz parte do curso

Desenvolvendo Modelos de Machine Learning para Produção

exercicio interativo prático

Transforme teoria em prática com um dos nossos exercicio interativos

Iniciar exercicio

Este exercicio faz parte do curso

Desenvolvendo Modelos de Machine Learning para Produção

IntermediárioNível de habilidade

4.8+

Comece o curso gratuitamente

This chapter will provide you with the skills and knowledge needed to move your machine learning models from the research and development phase into a production environment. You will learn about the process of moving from a research prototype to a reliable, scalable, and maintainable system.

Exercise 1: Adopting an MLOps mindset Exercise 2: What is a key aspect of MLOps?Exercise 3: What is technical debt?Exercise 4: Writing maintainable ML code Exercise 5: Version control Exercise 6: Code organization Exercise 7: Writing effective ML documentation Exercise 8: Why document labeling methods?Exercise 9: MLOps best practices

In this chapter, you’ll learn about the importance of reproducibility in machine learning, and how to ensure that your models remain reproducible and reliable over time. You’ll explore various techniques and best practices that you can use to ensure the reproducibility of your models.

Exercise 1: Projetando experimentos reproduzíveis Exercise 2: Reprodutibilidade em ML Exercise 3: Identificando aspectos essenciais para reprodutibilidade e confiabilidade Exercise 4: Classificando elementos de confiabilidade Exercise 5: Organizando Aspectos de Reprodutibilidade e Práticas de Documentação Exercise 6: Engenharia de features Exercise 7: Classificando objetivos e técnicas de Feature Engineering

Exercicio Atual

Exercise 8: Identificando técnicas de manipulação e transformação de dados Exercise 9: Seleção de variáveis (feature selection)Exercise 10: Versionamento de dados e modelos Exercise 11: Model store Exercise 12: Versionamento de dados de treino Exercise 13: Garantindo reprodutibilidade

In Chapter 3, you’ll examine the various challenges associated with deploying machine learning models into production environments. You’ll learn about the various approaches to deploying ML models in production and strategies for monitoring and maintaining ML models in production.

Exercise 1: Packaging ML models Exercise 2: Goal of serialization and containerization Exercise 3: Model packaging order Exercise 4: Scalability Exercise 5: Scaling Exercise 6: Model complexity Exercise 7: Categorizing scaling techniques Exercise 8: Automation Exercise 9: Benefits of continuous delivery Exercise 10: CI/CD steps Exercise 11: Putting ML models into production

In the final chapter, you’ll learn about the various ways to test machine learning pipelines and ensure they perform as expected. You’ll discover the importance of testing ML pipelines and learn techniques for testing and validating ML pipelines.

Exercise 1: Model reliability Exercise 2: Categorizing Testing Strategies for ML Pipeline Readiness and Staleness Exercise 3: Addressing Model Staleness in Machine Learning Pipelines Exercise 4: Testing data Exercise 5: Picking the right test Exercise 6: Consistency check Exercise 7: Testing models Exercise 8: Predicting customer lifetime value Exercise 9: Classifying fairness Exercise 10: Testing machine learning pipelines Exercise 11: Congratulations!