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17º encontro da comunidade Data Science Joinville

17º encontro da comunidade Data Science Joinville

Gabriel (Gabu) Bellon

July 17, 2019
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  1. 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

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  2. Julien Simon
    AI Evangelist, AWS
    https://aws.amazon.com/deepracer/
    Reinforcement Learning for devs: toolkits, and application to autonomous race cars
    ● SageMaker <3

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  3. 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

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  4. 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

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  5. 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

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  6. ● 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

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  7. ● 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

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  8. 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

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  9. A Clinical Application of Deep Learning for NLP with Word-Embeddings
    Arnon Santos
    Data Scientist,
    Junto Seguros
    ● Apresentação;
    ● Catálogo de Protuários;

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  10. Time Series Forecasting for Cloud Resources Provisioning
    Leonardo Neri
    AI Manager, Accenture
    ● Provisionamento de máquinas;
    ● Forescast por Log;
    ● Prophet;
    ● “Eventos” fake simular
    sazonalidade;

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  11. 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

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  12. 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

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  13. 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

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  14. Ludwig, a Code-Free Deep Learning Toolbox
    Piero Molino
    Sr. Research Scientist, Uber AI
    ● https://uber.github.io/ludwig/

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  15. DataOps architecture for Machine
    Learning - brought to you by everis
    Apresentação
    Carlos Porto Filho
    Data Scientist, Everis
    Gustavo Castilhos
    Data Architect, everis

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  16. AI culture and semi-autonomous review approval - sponsored by Dafiti
    Ricardo Savii
    Data Scientist, Dafiti Group
    Apresentação

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  17. 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

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  18. 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

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  19. 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

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  20. 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)

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  21. Reproducibility with Data Version Control
    Victor Villas Bôas Chaves
    Data Engineer, Gupy
    ● Apresentação
    ● https://dvc.org/

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  22. Validating models in the real world
    Luis Moneda
    Data Scientist,
    Nubank
    Apresentação

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  23. ETL Orchestration with AWS Glue and AWS Step-functions
    Alexsandro Francisco dos
    Santos
    Data Engineer, Stoodi

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  24. 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

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  25. 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

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  26. 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

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  27. ● 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/

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