Personal Information
Organization / Workplace
Within 23 wards, Tokyo, Japan Japan
Occupation
Data Engineer at MapR Technologies #unrecruitable
Industry
Technology / Software / Internet
Website
www.mapr.com
About
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 ...
Tags
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
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Presentations
(12)Likes
(59)The Data Lake - Balancing Data Governance and Innovation
Caserta
•
7 years ago
Creating a Modern Data Architecture
Zaloni
•
7 years ago
Top 5 Mistakes When Writing Spark Applications
Spark Summit
•
7 years ago
What does devops culture mean for engineers
Dave Kerr
•
5 years ago
Data ops: Machine Learning in production
Stepan Pushkarev
•
6 years ago
Machine Learning Success: The Key to Easier Model Management
MapR Technologies
•
6 years ago
DevOps + DataOps = Digital Transformation
Delphix
•
5 years ago
DataOps: Nine steps to transform your data science impact Strata London May 18
Harvinder Atwal
•
5 years ago
DataOps: An Agile Method for Data-Driven Organizations
Ellen Friedman
•
6 years ago
Human in the loop: a design pattern for managing teams working with ML
Paco Nathan
•
6 years ago
Bridging the Gap Between Data Science & Engineer: Building High-Performance Teams
ryanorban
•
8 years ago
Transforming Insurance Analytics with Big Data and Automated Machine Learning
Cloudera, Inc.
•
7 years ago
Building Streaming Data Applications Using Apache Kafka
Slim Baltagi
•
6 years ago
Hype vs. Reality: The AI Explainer
Luminary Labs
•
7 years ago
KEY CONCEPTS FOR SCALABLE STATEFUL SERVICES
Mykola Novik
•
6 years ago
Running Apache Zeppelin production
Vinay Shukla
•
6 years ago
Deploying deep learning models with Docker and Kubernetes
PetteriTeikariPhD
•
7 years ago
Deep Learning - Convolutional Neural Networks
Christian Perone
•
8 years ago
Python for Image Understanding: Deep Learning with Convolutional Neural Nets
Roelof Pieters
•
8 years ago
Deep learning - Conceptual understanding and applications
Buhwan Jeong
•
9 years ago
Deep Learning through Examples
Sri Ambati
•
9 years ago
EPTS DEBS2011 Event Processing Reference Architecture and Patterns Tutorial v1 2
Paul Vincent
•
12 years ago
Productionizing dl from the ground up
Adam Gibson
•
8 years ago
Everyday I'm Shuffling - Tips for Writing Better Spark Programs, Strata San Jose 2015
Databricks
•
9 years ago
MLConf - Emmys, Oscars & Machine Learning Algorithms at Netflix
Xavier Amatriain
•
10 years ago
Lessons Learned from Building Machine Learning Software at Netflix
Justin Basilico
•
9 years ago
Spark Meetup @ Netflix, 05/19/2015
Yves Raimond
•
8 years ago
10 Lessons Learned from Building Machine Learning Systems
Xavier Amatriain
•
9 years ago
2015 data-science-salary-survey
Adam Rabinovitch
•
8 years ago
Personal Information
Organization / Workplace
Within 23 wards, Tokyo, Japan Japan
Occupation
Data Engineer at MapR Technologies #unrecruitable
Industry
Technology / Software / Internet
Website
www.mapr.com
About
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 ...
Tags
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
See more