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