17º encontro da comunidade Data Science Joinville

Transcript

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3. THE MOST DIVERSE CONFERENCE ON ML Dan Nichol Program Chair,

   AnalyticsFC Ana Paula Appel Researcher and Data Scientist, IBM Research Louis Dorard General Chair, PAPIs

4. Julien Simon AI Evangelist, AWS https://aws.amazon.com/deepracer/ Reinforcement Learning for devs:

   toolkits, and application to autonomous race cars • SageMaker <3

5. Leela Senthil Nathan Software Engineer, Stripe • Apresentação • Fraude

   de Cartão de Crédito • Separação de Samples de Falso Positivos X Falsos Negativos • O problema do desbalanceamento de amostras Lessons Learned from Building a Credit Card Fraud Model

6. Bias and bugs: implementing recommendations Guilherme Silveira Head of Education,

   Alura • Apresentação • Recomendação de cursos; • Vies e outliers; • Correlação x Casualidade; • A/B bem feitos

7. TensorFlow image inferencing: an adventure in Python and Go •

   Análise de imagens de pulmão • Tensorflow API: Python ou Go? • Problemas com GO • Python + Keras • Integração GO (HTTP, Flask and RabbitMQ) Vitor De Mario Tech Lead, NeuralMed

8. • Test A/B; • Relevância Estatistica; • Comparação de Forescast

   e A/B • Sazonalidade; • Prophet Where are the gains: Should I use A/B Tests for forecasting? Eder Martins Data scientist, SEEK

9. • Apresentação • DS e ML não precisa ser complicado;

   • Soluções Simples • Tecnico x Comum • Comunicação Adauto Braz Data Scientist, Stoodi I know what you did last session: clustering users with ML

10. Using Machine Learning to recommend jobs in User Cold Start

    Andryw Marques Data Scientist, SEEK • Apresentação; • Vaga x Candidato; • Features simples; • Teste A/B + Feature Flag; • Pesos para Features x Vaga

11. A Clinical Application of Deep Learning for NLP with Word-Embeddings

    Arnon Santos Data Scientist, Junto Seguros • Apresentação; • Catálogo de Protuários;

12. Time Series Forecasting for Cloud Resources Provisioning Leonardo Neri AI

    Manager, Accenture • Provisionamento de máquinas; • Forescast por Log; • Prophet; • “Eventos” fake simular sazonalidade;

13. How Big Data Powers Ambev’s Sales Machine - sponsored by

    Big Data Gustavo Ioschpe Founder & CEO, Big Data • Original onde só tinha Brahma; • Logistica; • Foto Local x Tipo de Bebida

14. How Machine Learning is Transforming the Way iFood Runs its

    Logistic - sponsored by Movile Arnaud Seydoux Logistic IA manager, iFood • Logistica de Entregas; • Tempo de Preparo x Tempo de Entrega; • Predição Motoboy; • Predição Cozinha; • Escalar x Modelo atualizado

15. Panel: Diversity in ML Larissa Lautert Data Scientist, Linx Impulse

    Larissa Lautert Data Scientist, Linx Impulse Ana Paula Appel Researcher and Data Scientist, IBM Research Ricardo Herrmann Artificial Intelligence Engineer, Olivia AI

16. Ludwig, a Code-Free Deep Learning Toolbox Piero Molino Sr. Research

    Scientist, Uber AI • https://uber.github.io/ludwig/

17. DataOps architecture for Machine Learning - brought to you by

    everis Apresentação Carlos Porto Filho Data Scientist, Everis Gustavo Castilhos Data Architect, everis

18. AI culture and semi-autonomous review approval - sponsored by Dafiti

    Ricardo Savii Data Scientist, Dafiti Group Apresentação

19. BERT: Multi-language approach for Q&A and NLP Applications • Bidirectional

    Encoder Representation of Transformers (BERT) ◦ Modelo de representação de linguagem ◦ Modelo pré treinado pode receber um ajuste-fino com apenas uma camada de saída adicional ◦ “While the empirical results are strong, in some cases surpassing human performance ...” • Question & Answers e outros problemas de NLP Horst Rosa Erdmann Lead Data Scientist, Everis

20. Fklearn: A functional library for machine learning Henrique Lopes Machine

    Learning Engineer, Nubank • https://github.com/nubank/fklearn/blob/master/docs/source/exam ples/fklearn_overview.ipynb

21. Multitask convolutional neural networks: saving GPU time • Companhia de

    varejo com mais 250 milhões de produtos • CNN (Convolutional Neural Networks) • Single task classifier (mais classificadores) • Multi task classifier (All in one go) Paulo Eduardo Sampaio Data science specialist, McKinsey & Company

22. Paulo Eduardo Sampaio Data science specialist, McKinsey & Company Multitask

    convolutional neural networks: saving GPU time • Companhia de varejo com mais 250 milhões de produtos • CNN (Convolutional Neural Networks) • Single task classifier (2 classificadores) • Multi task classifier (All in one go)

23. Reproducibility with Data Version Control Victor Villas Bôas Chaves Data

    Engineer, Gupy • Apresentação • https://dvc.org/

24. Validating models in the real world Luis Moneda Data Scientist,

    Nubank Apresentação

25. ETL Orchestration with AWS Glue and AWS Step-functions Alexsandro Francisco

    dos Santos Data Engineer, Stoodi

26. How Grupo ZAP is using data to empower real estate

    buyers, sellers, and renters in Brazil - sponsored by Grupo ZAP Lucas Vargas CEO, Grupo ZAP

27. Machine Learning for Natural Resources Bianca Zadrozny Research Manager, IBM

    • Mineração de Ouro; • Geologista x ML; • Geologista e ML; • Grande área x Pepita; • 3D x 2D

28. Panel: How much should we care about model interpretability? Renato

    Vicente Chief Scientist, Serasa Experian Sandor Caetano Chief Data Scientist, IFood / Movile Ivan Marin Data Scientist/System Architect, Daitan Group

29. • Youtube • Twitter • Site • Medium • Resumo

    1 • Resumo 2 • #papisio • https://www.linkedin.com/in/gabubellon/ • https://www.linkedin.com/in/fachini/ • https://www.linkedin.com/in/gplichoski/