1. Início
  2. NOVA Information Management School
  3. Mestrado em Ciência de Dados e Métodos Analíticos Avançados
  4. Especialização em Business Analytics
  5. Operações de Aprendizagem Automática

Operações de Aprendizagem Automática

Objetivos

Being MLOps a practice that focuses on deploying, testing, monitoring, automating ML models in large-scale production environments, and recognizing its importance on the quality standards and simplification of data management process, the students will learn the best practices of its implementation. As an answer to the problem of scaling ML operations to the needs of the business and/or users of ML models in a standard way, the students will understand how, after the implementation of the MLOps, It?s easier to align models with business needs, as well as regulatory requirements.

Caracterização geral

Código

200293

Créditos

3.5

Professor responsável

Nuno Filipe Rosa Agostinho

Horas

Semanais - A disponibilizar brevemente

Totais - A disponibilizar brevemente

Idioma de ensino

Português. No caso de existirem alunos de Erasmus, as aulas serão leccionadas em Inglês

Pré-requisitos

TBD

Bibliografia

Introducing MLOps: How to Scale Machine Learning in the Enterprise 1st Edition by Mark Treveil et al.

Machine Learning Engineering by Andriy Burkov

Beginning MLOps with MLFlow: Deploy Models in AWS SageMaker, Google Cloud, and Microsoft Azure by Sridhar Alla and Suman Kalyan Adari

Método de ensino

TBD

Método de avaliação

Final project of creating a ML end-to-end cycle 70%

Exam 30%

Conteúdo

1-MLOps Fundamentals

2-Model experimentation, deployment, and testing using MLFlow

3-Model monitoring: data drift vs concept drift

4-Model monitoring with NannyML

5-How to make a ML project development much more efficient using Kedro

6-Understanding the Main Kubernetes

7-Introducing Kubeflow pipelines