Mathieu Dumoulin

53 Followers

12 SlideShares 53 Followers 46 Followings

12 SlideShares 53 Followers 46 Followings

If there is anything I am good at, it's the ability to understand a business problem and translate it into working, state of the art technology. I combine professional level skills of a big data architect, data engineer, machine learning engineer and data scientist. In Machine learning, Recently I've been working a lot with Hadoop (MapR's distribution) and Apache Spark, Apache Drill, Elasticsearch/Kibana and Kafka/MapR Streams for real-time event-driven processing. On the machine learning side, I have strong practical experience with supervised learning, especially applied to unstructured (text) data in English, Japanese and French. Within these data-related specialties, I am more of ...

mapr big data machine learning apache hadoop deep learning apache spark h2o enterprise hadoop spark streaming kafka microservices container orchestration indroduction introduction mapreduce distribué fondamental java machine learning datarobot h2o caffeonspark containers converged distributed tensorflow benchmarks kubernetes predictive maintenance large scale iot real-time flink software architecture sensor data science strata singapore 2106 cep scalable

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Introduction à Hadoop

11 years ago • 4550 Views

Presentation Hadoop Québec

11 years ago • 2987 Views

MapReduce: Traitement de données distribué à grande échelle simplifié

11 years ago • 2528 Views

Introduction aux algorithmes map reduce

11 years ago • 1603 Views

Real world machine learning with Java for Fumankaitori.com

9 years ago • 10684 Views

Distributed Deep Learning on Spark

8 years ago • 1174 Views

Real-World Machine Learning - Leverage the Features of MapR Converged Data Platform

8 years ago • 1329 Views

Streaming Architecture to Connect Everything (Including Hybrid Cloud) - Strata Singapore 2016

8 years ago • 1237 Views

CEP - simplified streaming architecture - Strata Singapore 2016

8 years ago • 3934 Views

MapR and Machine Learning Primer

8 years ago • 357 Views

State of the Art Robot Predictive Maintenance with Real-time Sensor Data

8 years ago • 1938 Views

Converged and Containerized Distributed Deep Learning With TensorFlow and Kubernetes

7 years ago • 2134 Views

The Data Lake - Balancing Data Governance and Innovation

Caserta • 9 years ago

Creating a Modern Data Architecture

Zaloni • 8 years ago

Top 5 Mistakes When Writing Spark Applications

Spark Summit • 9 years ago

What does devops culture mean for engineers

Dave Kerr • 7 years ago

Data ops: Machine Learning in production

Stepan Pushkarev • 7 years ago

Machine Learning Success: The Key to Easier Model Management

MapR Technologies • 7 years ago

DevOps + DataOps = Digital Transformation

Delphix • 6 years ago

DataOps: Nine steps to transform your data science impact Strata London May 18

Harvinder Atwal • 7 years ago

DataOps: An Agile Method for Data-Driven Organizations

Ellen Friedman • 7 years ago

Human in the loop: a design pattern for managing teams working with ML

Paco Nathan • 7 years ago

Bridging the Gap Between Data Science & Engineer: Building High-Performance Teams

ryanorban • 9 years ago

Transforming Insurance Analytics with Big Data and Automated Machine Learning 

Cloudera, Inc. • 8 years ago

Building Streaming Data Applications Using Apache Kafka

Slim Baltagi • 7 years ago

Hype vs. Reality: The AI Explainer

Luminary Labs • 8 years ago

KEY CONCEPTS FOR SCALABLE STATEFUL SERVICES

Mykola Novik • 8 years ago

Running Apache Zeppelin production

Vinay Shukla • 8 years ago

Deploying deep learning models with Docker and Kubernetes

PetteriTeikariPhD • 8 years ago

Deep Learning - Convolutional Neural Networks

Christian Perone • 9 years ago

Python for Image Understanding: Deep Learning with Convolutional Neural Nets

Roelof Pieters • 10 years ago

Deep learning - Conceptual understanding and applications

Buhwan Jeong • 10 years ago

Deep Learning through Examples

Sri Ambati • 10 years ago

EPTS DEBS2011 Event Processing Reference Architecture and Patterns Tutorial v1 2

Paul Vincent • 14 years ago

Productionizing dl from the ground up

Adam Gibson • 10 years ago

Everyday I'm Shuffling - Tips for Writing Better Spark Programs, Strata San Jose 2015

Databricks • 10 years ago

MLConf - Emmys, Oscars & Machine Learning Algorithms at Netflix

Xavier Amatriain • 11 years ago

Lessons Learned from Building Machine Learning Software at Netflix

Justin Basilico • 10 years ago

Spark Meetup @ Netflix, 05/19/2015

Yves Raimond • 10 years ago

10 Lessons Learned from Building Machine Learning Systems

Xavier Amatriain • 10 years ago

Ehtsham Elahi, Senior Research Engineer, Personalization Science and Engineering Group at Netflix at MLconf SEA - 5/01/15

MLconf • 10 years ago

2015 data-science-salary-survey

Adam Rabinovitch • 9 years ago

If there is anything I am good at, it's the ability to understand a business problem and translate it into working, state of the art technology. I combine professional level skills of a big data architect, data engineer, machine learning engineer and data scientist. In Machine learning, Recently I've been working a lot with Hadoop (MapR's distribution) and Apache Spark, Apache Drill, Elasticsearch/Kibana and Kafka/MapR Streams for real-time event-driven processing. On the machine learning side, I have strong practical experience with supervised learning, especially applied to unstructured (text) data in English, Japanese and French. Within these data-related specialties, I am more of ...

mapr big data machine learning apache hadoop deep learning apache spark h2o enterprise hadoop spark streaming kafka microservices container orchestration indroduction introduction mapreduce distribué fondamental java machine learning datarobot h2o caffeonspark containers converged distributed tensorflow benchmarks kubernetes predictive maintenance large scale iot real-time flink software architecture sensor data science strata singapore 2106 cep scalable

See more