curso
Developing Machine Learning Models for Production
Intermediário
Actualizado 02/2025Iniciar curso gratuitamente
Incluído comPremium or Teams
TheoryMachine Learning4 horas13 vídeos44 exercícios2,850 XP5,215Certificado de conclusão
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Descrição do curso
Experiment and Document with Ease
Experimenting with ML models is often enjoyable but can be time-consuming. Here, you will learn how to design reproducible experiments to expedite this process while writing documentation for yourself and your teammates, making future work on the pipeline a breeze.Build MLOps Models For Production
You will learn best practices for packaging and serializing both models and environments for production to ensure that models will last as long as possible.Scale Up and Automate your ML Pipelines
By considering model and data complexity and continuous automation, you can ensure that your models will be scaled for production use and can be monitored and deployed in the blink of an eye.Once you complete this course, you will be able to design and develop machine learning models that are ready for production and continuously improve them over time.
Pré-requisitos
MLOps ConceptsSupervised Learning with scikit-learn1
Moving from Research to Production
2
Ensuring Reproducibility
3
ML in Production Environments
4
Testing ML Pipelines
Developing Machine Learning Models for Production
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