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Palestra: Automating Machine Learning and Deep Learning Workflows

Track: Armazenamento e Processamento de Big Data

Sala: 4 São Francisco

Horário: 3:05pm - 3:50pm

Dia da semana: Segunda-feira

Slides: Download Slides

Nível: Intermediário

Persona: Arquiteto(a), Cientista de Dados, Desenvolvedor(a) Programador(a), Desenvolvedor(a) Sênior, DevOps, Gerente de Produto, Gerente de Projetos, Gestão (VP, CTO, CIO, Diretoria), Líder Técnico(a)

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Pontos Principais

  • Leveraging Kubernetes to make machine learning Ops (MLOps) scalable and portable;
  • Introducing more rigorous best practices to avoid repetition and duplication;
  • Improve data scientists and machine learning engineers productivity and knowledge distribution.


Machine Learning services are quickly becoming a big part of the software developer’s toolbox, in any domain. Databases or web development frameworks are a standard component of almost any non-trivial application, they integrate with not much special expertise. We expect to see similar layers for Machine Learning and Deep Learning in the near future with the same maturity.

Machine learning workflows are most of the time iterative, and typically involve several steps, i.g. creation of many intermediate datasets, modeling, evaluations, predictions, and deployment. These workflows tend to be repetitive and in some cases manual. In this presentation I will cover some of these workflows and how to automate them with the help of Polyaxon, an open source platform built on Kubenernetes to make machine learning, reproducible, scalable, and portable. We will be covering different workflows, from basic ones that automate repetitive tasks (e.g., create a dataset, post-process it, do augmentation, and finally model the result and make predictions) to sophisticated algorithms that enhance our machine learning arsenal (e.g., feature selection or hyperparameter optimization techniques).

Palestrante: Mourad Mourafiq

Author / Founder at Polyaxon

An engineer with more than 8 years of experience. He has been working in different roles involving quantitative trading, data analytics, software engineering, and team-leading at EIB, BNP Paribas, Seerene, Kayak, Dubsmash. He is currently working on a new open source platform for building, training, and monitoring large scale deep learning applications called Polyaxon.

Find Mourad Mourafiq at

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