Personal Information
Organização/Local de trabalho
Within 23 wards, Tokyo, Japan Japan
Cargo
Data Engineer at MapR Technologies #unrecruitable
Setor
Technology / Software / Internet
Site
www.mapr.com
Sobre
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 ...
Marcadores
mapr
big data
machine learning
microservices
kafka
streaming
spark
hadoop
enterprise
h2o
apache spark
deep learning
apache hadoop
container orchestration
containers
converged
tensorflow
kubernetes
predictive maintenance
iot
real-time
sensor
data science
cep
scalable
strata singapore 2106
software architecture
flink
large scale
benchmarks
distributed
caffeonspark
java machine learning datarobot h2o
mapreduce distribué fondamental
introduction
indroduction
Ver mais
Apresentações
(12)Gostaram
(59)The Data Lake - Balancing Data Governance and Innovation
Caserta
•
Há 7 anos
Creating a Modern Data Architecture
Zaloni
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Top 5 Mistakes When Writing Spark Applications
Spark Summit
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What does devops culture mean for engineers
Dave Kerr
•
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Data ops: Machine Learning in production
Stepan Pushkarev
•
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Machine Learning Success: The Key to Easier Model Management
MapR Technologies
•
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DevOps + DataOps = Digital Transformation
Delphix
•
Há 5 anos
DataOps: Nine steps to transform your data science impact Strata London May 18
Harvinder Atwal
•
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DataOps: An Agile Method for Data-Driven Organizations
Ellen Friedman
•
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Human in the loop: a design pattern for managing teams working with ML
Paco Nathan
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Bridging the Gap Between Data Science & Engineer: Building High-Performance Teams
ryanorban
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Transforming Insurance Analytics with Big Data and Automated Machine Learning
Cloudera, Inc.
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Building Streaming Data Applications Using Apache Kafka
Slim Baltagi
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Hype vs. Reality: The AI Explainer
Luminary Labs
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KEY CONCEPTS FOR SCALABLE STATEFUL SERVICES
Mykola Novik
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Running Apache Zeppelin production
Vinay Shukla
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Deploying deep learning models with Docker and Kubernetes
PetteriTeikariPhD
•
Há 7 anos
Deep Learning - Convolutional Neural Networks
Christian Perone
•
Há 8 anos
Python for Image Understanding: Deep Learning with Convolutional Neural Nets
Roelof Pieters
•
Há 8 anos
Deep learning - Conceptual understanding and applications
Buhwan Jeong
•
Há 9 anos
Deep Learning through Examples
Sri Ambati
•
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EPTS DEBS2011 Event Processing Reference Architecture and Patterns Tutorial v1 2
Paul Vincent
•
Há 12 anos
Productionizing dl from the ground up
Adam Gibson
•
Há 8 anos
Everyday I'm Shuffling - Tips for Writing Better Spark Programs, Strata San Jose 2015
Databricks
•
Há 9 anos
MLConf - Emmys, Oscars & Machine Learning Algorithms at Netflix
Xavier Amatriain
•
Há 10 anos
Lessons Learned from Building Machine Learning Software at Netflix
Justin Basilico
•
Há 9 anos
Spark Meetup @ Netflix, 05/19/2015
Yves Raimond
•
Há 8 anos
10 Lessons Learned from Building Machine Learning Systems
Xavier Amatriain
•
Há 9 anos
2015 data-science-salary-survey
Adam Rabinovitch
•
Há 8 anos
Personal Information
Organização/Local de trabalho
Within 23 wards, Tokyo, Japan Japan
Cargo
Data Engineer at MapR Technologies #unrecruitable
Setor
Technology / Software / Internet
Site
www.mapr.com
Sobre
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 ...
Marcadores
mapr
big data
machine learning
microservices
kafka
streaming
spark
hadoop
enterprise
h2o
apache spark
deep learning
apache hadoop
container orchestration
containers
converged
tensorflow
kubernetes
predictive maintenance
iot
real-time
sensor
data science
cep
scalable
strata singapore 2106
software architecture
flink
large scale
benchmarks
distributed
caffeonspark
java machine learning datarobot h2o
mapreduce distribué fondamental
introduction
indroduction
Ver mais