PAPIs Latam 2019 has ended

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PAPIs Latam 2019

08:30

Welcome & Registration

09:00

Building a Simple Recommender System From Scratch — Training Workshop Dafiti Group — Room LYONWillian Fuks Deep Learning Kickstart with Keras — Training Workshop Dafiti Group — Room MARSEILLESPaula Moraes • Felipe Salvatore Machine Learning for Managers — Training Workshop Dafiti Group — Room PARISLouis Dorard Machine Learning on AWS with SageMaker — Training Workshop Dafiti Group — Room VERSAILLESJulien Simon

08:15

Welcome & Registration Expo space

09:00

Opening remarks Room 8Dan Nichol • Ana Paula Appel • Louis Dorard

09:30

Reinforcement Learning for devs: toolkits, and application to autonomous race cars Room 8Julien Simon

10:00

Lessons Learned from Building a Credit Card Fraud Model Room 8Leela Senthil Nathan

10:30

Expo - Networking - Coffee Expo space

11:00

Bias and bugs: implementing recommendations Room 9Guilherme Silveira Invoice Payment to Optimize Cash Collection Room 8Ana Paula Appel Training and deploying ML models with Kubeflow and TensorFlow Extended (TFX) — sponsored by CI&T Room 7Fábio Uechi • Gabriel Moreira • Rodrigo de Freitas Pereira

11:30

TensorFlow image inferencing: an adventure in Python and Go Room 9Vitor De Mario Where are the gains: Should I use A/B Tests for forecasting? Room 8Eder Martins

12:00

Deploy your Deep Learning models in serverless architectures Room 9Adriano Dennanni I know what you did last session: clustering users with ML Room 8Adauto Braz

12:30

Expo - Networking - Lunch Expo space

14:00

K8s-workqueue: Simplified Kubernetes ML Batch Jobs Room 9Chinmay Nerurkar Using Machine Learning to recommend jobs in User Cold Start Room 8Andryw Marques Introduction to classification with SKLearn — sponsored by Caelum Room 7

14:30

Airflow on Kubernetes: a modern approach to ETL workflows. Room 9Raphael Sampaio A Clinical Application of Deep Learning for NLP with Word-Embeddings Room 8Arnon Santos

15:00

Time Series Forecasting for Cloud Resources Provisioning Room 8Leonardo Neri

15:30

Expo - Networking - Coffee Expo space

16:00

How Big Data Powers Ambev’s Sales Machine - sponsored by Big Data Room 8Gustavo Ioschpe

16:30

How Machine Learning is Transforming the Way iFood Runs its Logistic - sponsored by Movile Room 8Arnaud Seydoux

17:00

Panel: Diversity in ML Room 8Esther Colombini • Larissa Lautert • Ana Paula Appel • Ricardo Herrmann

17:30

Drinks! Expo space

08:15

Welcome & Registration Expo space

09:00

Ludwig, a Code-Free Deep Learning Toolbox Room 8Piero Molino

09:30

DataOps architecture for Machine Learning - brought to you by everis Room 8Carlos Porto Filho • Gustavo Castilhos

10:00

AI culture and semi-autonomous review approval - sponsored by Dafiti Room 8Ricardo Savii

10:30

Expo - Networking - Coffee Expo space

11:00

BERT: Multi-language approach for Q&A and NLP Applications Room 8Horst Rosa Erdmann Fklearn: A functional library for machine learning Room 9Henrique Lopes Using pre-trained models for classification where samples are scarce - sponsored by Dafiti Room 7Ricardo Savii

11:30

Multitask convolutional neural networks: saving GPU time Room 8Paulo Eduardo Sampaio

11:40

Reproducibility with Data Version Control Room 9Victor Villas Bôas Chaves

12:00

AI informs, humans choose -- or do they? Room 8Bianca Ximenes

12:30

Expo - Networking - Lunch Expo space

14:00

Game Theory and Model Interpretability Room 8Gabriel Cypriano Saca Validating models in the real world Room 9Luis Moneda Everything you need to know about Tensorflow 2.0 — sponsored by Daitan Room 7

14:30

Trading strategies using deep reinforcement learning Room 8Suraj Shinde • Cristyan Rufino Gil Morales

14:40

ETL Orchestration with AWS Glue and AWS Step-functions Room 9Alexsandro Francisco dos Santos

15:00

Attention-Based Neural Networks for Relational Data Room 8Scott Brownlie

15:30

Expo - Networking - Coffee Expo space

16:00

How Grupo ZAP is using data to empower real estate buyers, sellers, and renters in Brazil - sponsored by Grupo ZAP Room 8Lucas Vargas

16:30

Machine Learning for Natural Resources Room 8Bianca Zadrozny

17:00

Panel: How much should we care about model interpretability? Room 8Renato Vicente • Ivan Marin • Sandor Caetano

17:30

Closing remarks Room 8Ana Paula Appel • Dan Nichol • Louis Dorard