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MLOps Concepts

Discover how MLOps can take machine learning models from local notebooks to functioning models in production that generate real business value.

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2 Horas16 Videos47 Exercicios
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Descrição do Curso

Learn about Machine Learning Operations (MLOps)

Understanding MLOps concepts is essential for any data scientist, engineer, or leader to take machine learning models from a local notebook to a functioning model in production.

In this course, you’ll learn what MLOps is, understand the different phases in MLOps processes, and identify different levels of MLOps maturity. After learning about the essential MLOps concepts, you’ll be well-equipped in your journey to implement machine learning continuously, reliably, and efficiently.

Discover How Machine Learning Can be Scaled and Automated

How can we scale our machine learning projects using the minimum time and resources? And how can we automate our processes to reduce the need for manual intervention and improve model performance? These are fundamental Machine Learning questions that MLOps provides the answers to.

In this MLOps course, you’ll start by exploring the basics of MLOps, looking at the core features and associated roles. Next, you’ll explore the various phases of the machine learning lifecycle in more detail.

As you progress, you'll also learn about systems and tools to better scale and automate machine learning operations, including feature stores, experiment tracking, CI/CD pipelines, microservices, and containerization. You’ll explore key MLOps concepts, giving you a firmer understanding of their applications.
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Nas seguintes faixas

Engenheiro de aprendizado de máquina

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Machine Learning in Production in Python

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MLOps Fundamentals

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  1. 1

    Introduction to MLOps

    Grátis

    First, you’ll learn about the core features of MLOps. You’ll explore the machine learning lifecycle, its phases, and the roles associated with MLOps processes.

    Reproduzir Capítulo Agora
    What is MLOps?
    50 xp
    What is not MLOps?
    50 xp
    WhatOps for what?
    100 xp
    Different phases in MLOps
    50 xp
    The ML lifecycle
    50 xp
    Tasks per phase
    100 xp
    Roles in MLOps
    50 xp
    Your MLOps team
    50 xp
    Core roles in MLOps processes
    100 xp
  2. 3

    Deploying Machine Learning into Production

    In this chapter, you’ll dive into the concepts relevant to deploying machine learning into production, such as runtime environments, containerization, CI/CD pipelines, and deployment strategies.

    Reproduzir Capítulo Agora
  3. 4

    Maintaining Machine Learning in Production

    Finally, you’ll learn about maintaining machine learning in production, with concepts such as statistical and computational monitoring, retraining, different levels of MLOps maturity, and tools that can be used within the machine learning lifecycle to simplify processes.

    Reproduzir Capítulo Agora
Para Empresas

GroupTreinar 2 ou mais pessoas?

Obtenha acesso à biblioteca completa do DataCamp, com relatórios, atribuições, projetos e muito mais centralizados

Nas seguintes faixas

Engenheiro de aprendizado de máquina

Ir para a trilha

Machine Learning in Production in Python

Ir para a trilha

MLOps Fundamentals

Ir para a trilha

Colaboradores

Collaborator's avatar
George Boorman
Collaborator's avatar
James Chapman
Collaborator's avatar
Arne Warnke

Pre Requisitos

Understanding Machine LearningUnderstanding Data Engineering
Folkert Stijnman HeadshotFolkert Stijnman

ML Engineer

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