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ML in the Wild [clear filter]
Tuesday, June 25
 

11:00 GMT-03

Invoice Payment to Optimize Cash Collection
Predicting invoice payment is valuable in multiple industries and supports decision-making processes in most financial workflows. This work presents a prototype developed as a solution devised during a partnership with a multinational bank to support collectors in predicting invoices payment. The proposed prototype reached up to 83.5% of accuracy, which improved the prioritization of customers and supported the daily work of collectors and we expect to support researchers dealing with the problem of invoice payment prediction to get insights and examples of how to tackle issues present in real data

Speakers
avatar for Ana Paula Appel

Ana Paula Appel

Researcher and Data Scientist, IBM Research
Ana Paula is a Research Staff Member in IBM Research - Brazil, currently work with large amount of data to do Science WITH Data and Science OF Data at IBM Research Brazil. My technical interesting are in data mining and machine learning area specially in graph mining techniques for... Read More →


Tuesday June 25, 2019 11:00 - 11:20 GMT-03
Room 8 Av. Rebouças, 3970 - Pinheiros, São Paulo - SP, 05402-600, Brazil

11:30 GMT-03

Where are the gains: Should I use A/B Tests for forecasting?
In this talk, we will analyze some theoretical aspects related to the results of an A/B test, in scenarios where Machine Learning models are involved. We will discuss the risks of taking results of an A/B test, or other purely comparative methodology, as a prediction of future gains. We will review the main practical and theoretical differences between A/B test and forecasting, when conducting ML related projects. This discussion targets a mixed audience, embracing people from technical and business areas. Also, it will be illustrated with real world examples taken from actual A/B tests.

Speakers
avatar for Eder Martins

Eder Martins

Data scientist, SEEK
Eder Martins is a senior Machine Learning engineer passionate about solving problems. Currently, he is working on ML projects related to search and recommendation for online recruitment markets at Seek AI GDP team. MSc in Computer Science at UFMG, his interests include: Recommendation... Read More →


Tuesday June 25, 2019 11:30 - 11:50 GMT-03
Room 8 Av. Rebouças, 3970 - Pinheiros, São Paulo - SP, 05402-600, Brazil

12:00 GMT-03

I know what you did last session: clustering users with ML
Understanding user behaviour is one of the main challenges of any company. In this presentation, we’ll talk about how we explored users’ activity to find common patterns among them in three important moments of the customer journey. Through our experience with this project, we hope you’ll learn a bit about how to select the right data to analyze, how to find out which data actually matters, what are the challenges facing unsupervised learning algorithms in real life, how to check if your results make sense, and how you can use the power of storytelling to communicate results.

Speakers
avatar for Adauto Braz

Adauto Braz

Data Scientist, Stoodi
Adauto Braz - graduated at Aeronautics Institute of Technology (ITA) as a computer engineering in 2018 - is a Data Scientist, currently working at the Data team at Stoodi - an education startup. At Stoodi, he’s worked with Data Engineering, Data Analysis, Data Culture and NLP.


Tuesday June 25, 2019 12:00 - 12:20 GMT-03
Room 8 Av. Rebouças, 3970 - Pinheiros, São Paulo - SP, 05402-600, Brazil

14:00 GMT-03

Using Machine Learning to recommend jobs in User Cold Start
Industry still struggles to design recommenders able to provide useful recommendations for users who have not interacted with any item (User Cold Start scenario). In this talk, we will show how to connect new candidates to their desired jobs in one of the main online employment marketplaces in Latin America. The solution involves, first, modeling user and item profiles based on content features and then using Machine Learning to predict interactions between them. A / B testing evidenced the proposed solution is able to enhance user engagement.

Speakers
avatar for Andryw Marques

Andryw Marques

Data Scientist, SEEK
Andryw Marques is a Data Scientist at Seek-AI GDP team, currently working with projects related to Search and Recommendation for online employment. He has experience in fields such as Machine Learning, Information Retrieval and Data Analysis. Andryw received his Master and Bachelor's... Read More →


Tuesday June 25, 2019 14:00 - 14:20 GMT-03
Room 8 Av. Rebouças, 3970 - Pinheiros, São Paulo - SP, 05402-600, Brazil

14:30 GMT-03

A Clinical Application of Deep Learning for NLP with Word-Embeddings
Clinical coding represents the transposition of clinical findings and diagnostics into codes contained in the International Classification of Diseases (ICD). To perform such task, hospitals assign the role of “clinical coder” to the person responsible for reading the whole clinical documentation and assigning the ICD codes accordingly. This research aims to present a deep learning framework for natural language processing (NLP) capable of automating this task by interpreting (and further classifing) the text contained within these documents, by using "self-taught" word embeddings (learned from the database itself) as input.

Speakers
avatar for Arnon Santos

Arnon Santos

Data Scientist, Junto Seguros
MSc. (PUC-PR) in Computing for Health Sciences, researcher in IA and Machine Learning and member of the Brazilian Computing Society. My main research is based on the usage of Deep Learning applied to natural language processing, where I seek to expand the current state-of-art of natural... Read More →


Tuesday June 25, 2019 14:30 - 14:50 GMT-03
Room 8 Av. Rebouças, 3970 - Pinheiros, São Paulo - SP, 05402-600, Brazil

15:00 GMT-03

Time Series Forecasting for Cloud Resources Provisioning
Forecasting cloud resources consumption is one of the main challenges faced by IT operations teams in capacity planning tasks. Those teams often should make predictions for periods with no corresponding previous data whose cloud transactions activity changes, for instance, on a holiday. In this talk, it is discussed an approach which is able to model this future behavior with synthesized data. The procedure uses small samples to synthesize data to fit a time series forecasting model which applies interpretable parameters to be adjusted by domain knowledge analysts.

Speakers
avatar for Leonardo Neri

Leonardo Neri

AI Manager, Accenture
Leonardo has a Ph.D. in Signal Processing and Pattern Recognition. His academic research is on Speech Processing field, specialized on Speaker Diarization.He is currently working at Accenture with data science, optimization problems, natural language processing, and machine learning... Read More →


Tuesday June 25, 2019 15:00 - 15:20 GMT-03
Room 8 Av. Rebouças, 3970 - Pinheiros, São Paulo - SP, 05402-600, Brazil