Developing Machine Learning Models for Production
Shift to an MLOps mindset, enabling you to train, document, maintain, and scale your machine learning models to their fullest potential.
Comece O Curso Gratuitamente4 horas13 vídeos44 exercícios4.746 aprendizesDeclaração de Realização
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Descrição do Curso
Much of today’s machine learning-related content focuses on model training and parameter tuning, but 90% of experimental models never make it to production, mainly because they were not built to last. In this course, you will see how shifting your mindset from a machine learning engineering mindset to an MLOps (Machine Learning Operations) mindset will allow you to train, document, maintain, and scale your models to their fullest potential.
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.
Treinar 2 ou mais pessoas?
Obtenha acesso à sua equipe à plataforma DataCamp completa, incluindo todos os recursos.Nas seguintes faixas
MLOps Fundamentals
Ir para a trilha- 1
Moving from Research to Production
GratuitoThis 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.
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Ensuring Reproducibility
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.
Designing reproducible experiments50 xpML reproducibility50 xpIdentifying Key Aspects for Reproducibility and Reliability50 xpSorting Reliability Elements100 xpSorting Aspects of Reproducibility and Documentation Practices100 xpFeature engineering50 xpClassifying Feature Engineering Goals & Techniques100 xpIdentifying Data Manipulation and Transformation Techniques50 xpFeature selection50 xpData and model versioning50 xpModel store50 xpVersioning training data50 xpEnsuring reproducibility100 xp - 3
ML in Production Environments
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.
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Testing ML Pipelines
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.
Model reliability50 xpCategorizing Testing Strategies for ML Pipeline Readiness and Staleness100 xpAddressing Model Staleness in Machine Learning Pipelines50 xpTesting data50 xpPicking the right test50 xpConsistency check50 xpTesting models50 xpPredicting customer lifetime value50 xpClassifying fairness100 xpTesting machine learning pipelines100 xpCongratulations!50 xp
Treinar 2 ou mais pessoas?
Obtenha acesso à sua equipe à plataforma DataCamp completa, incluindo todos os recursos.Nas seguintes faixas
MLOps Fundamentals
Ir para a trilhacolaboradores
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