PAPIs.io

Predicting the 2018 Oscar Winners with Machine Learning - Poul Petersen (BigML)

The future of machine learning is decentralized - Alex Ingerman (Google)

DIY Predictive Modeling Cluster with Kubernetes, Dask and JupyterHub - Olivier Grisel (INRIA)

Deep Learning at the edge with AWS DeepLens - Julien Simon (AWS)

Machine Learning, Technical Debt, and You - D. Sculley (Google)

Supercharging Deep Learning with the Unity Engine - Arthur Juliani (Unity)

Automated Machine Learning: Mostly Unhelpful - Charles Parker (BigML)

Scaling Machine Learning as a Service — LI Erran Li (Uber) at #papis2016

A renaissance for decision tree learning — Cynthia Rudin (Duke University) #papis2016

The privacy bounds of human behavior — Yves-Alexandre de Montjoye (MIT & Imperial College)

The emergent opportunity of Big Data for Social Good - Nuria Oliver (Telefonica) #PAPIsConnect

Machine Learning Services Benchmark: choosing the right tools - Inês Almeida #PAPIsConnect

Adversarial Attacks for ML/DL - Eduardo Valle ( UNICAMP)

From data to AI with the Machine Learning Canvas - Louis Dorard (PAPIs)

Extracting Entities and Relations in Portuguese Documents - Natalie Vargas (Accenture)

The limits of decision making with artificial intelligence - Martin Goodson (Evolution AI)

Smart Data through Automatic Data Search and Extraction - David Arnu (RapidMiner)

PAPIs.io LATAM 2018 - Full Conference Day 2

Open Source Machine Learning Deployment - Clive Cox (Seldon)