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AI and Machine Learning @ AWS, O'Reilly Author @ Data Science on AWS, Founder @ PipelineAI, Formerly Databricks, Netflix,
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datascienceonaws.com
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Presentations
(83)Likes
(17)Introduction to Emoji Data Science (Open Data Science Conference, 2017)
Hamdan Azhar
•
6 years ago
Machine Learning logistics
Ted Dunning
•
6 years ago
Deploying deep learning models with Docker and Kubernetes
Research Fellow
•
7 years ago
Monitoring Kubernetes with Prometheus (Kubernetes Ireland, 2016)
Brian Brazil
•
7 years ago
JP version - Beyond Shuffling - Apache Spark のスケールアップのためのヒントとコツ
Holden Karau
•
8 years ago
Java on zSystems zOS
Tim Ellison
•
8 years ago
What's New in IBM Java 8 SE?
Tim Ellison
•
8 years ago
Real time Analytics with Apache Kafka and Apache Spark
Rahul Jain
•
9 years ago
Spark After Dark: Real time Advanced Analytics and Machine Learning with Spark
Chris Fregly
•
8 years ago
A New Year in Data Science: ML Unpaused
Paco Nathan
•
8 years ago
Sf data mining_meetup
Adam Gibson
•
9 years ago
Doing-the-impossible
Ted Dunning
•
9 years ago
Netflix: From Clouds to Roots
Brendan Gregg
•
9 years ago
OLAP with Cassandra and Spark
Evan Chan
•
9 years ago
Spark the next top compute model
Dean Wampler
•
9 years ago
Surge 2013: Maximizing Scalability, Resiliency, and Engineering Velocity in the Cloud
Coburn Watson
•
10 years ago
Oozie @ Riot Games
Matt Goeke
•
10 years ago
Personal Information
Organization / Workplace
San Francisco Bay Area United States
Occupation
AI and Machine Learning @ AWS, O'Reilly Author @ Data Science on AWS, Founder @ PipelineAI, Formerly Databricks, Netflix,
Industry
Technology / Software / Internet
Website
datascienceonaws.com
Tags
spark
spark streaming
spark sql
kafka
apache spark
big data
graphx
approximations
cassandra
spark cassandra connector
mllib
bloom filter
recommendations
alternating least squares
collaborative filtering
matrix factorization
count min sketch
sketch algorithms
hyperloglog
tensorflow
machine learning
artificial intelligence
spark ml
spark mllib
advanced spark and tensorflow meetup
kubernetes
gpu
tensorflow serving
machine learning pipeline
tensorflow distributed
aws sagemaker
keras
distributed tensorflow
aws
nvidia
ml pipelines
python
pytorch
xla
high performance
distributed systems
pipeline.ai
model deployment
data science on aws
tensorflow ai
model predictions
model training
amazon web services
cuda
kubeflow
ai pipeline
google cloud platform
tensorrt
c++
neural networks
bert
tfx
airflow
mlflow
distributed training
multi-armed bandit
traffic routing
istio
scikit-learn
open source
jit compiler
continuous deployment
linear algebra
redis
spark elasticsearch connector
redshift
json
performance tuning
textual analysis
real-time processing
lambda architecture
reinforcement learning
pandas
quantum computing
s3
tensorflow extended
xgboost
redis ai
redis streams
model explainability
ai platform
tpu
hyper-parameter tuning
tensorflow xla
model optimization
streaming data
model serving
google cloud ml
azure ml
open data science conference
model runtime
canary deployment
high scalability
r
java
queue
numpy
jupyterhub
aot compiler
fp16
docker
jupyter
netflix
predicate pushdown
data sources api
avro
parquet
rest
orc
data frames
dynamodb
catalyst optimizer
mesos
performance
natural language processing
nlp
batch processing
blinkdb
graph processing
scale
ray
software
aws cloud
feature engineering
sagemaker data wrangler
fairness
bias
model debugger
model drift
training-serving skew
model monitoring
bell pair
amplitude amplification
phase manipulation
relative phase
magnitude
amplitude
photons
circle notation
entanglement
superposition
quantum processing unit
qpu
post-quantum cryptography
quantum advantage
quantum supremecy
quantum simulations
qubit
amazon braket
nir eyal
slack
zoom
advanced kubeflow meetup
grateful dead
peter drucker
flywheel
oreilly media
oreilly book
workshop
amazon kendra
chatbot
amazon lex
text classification
amazon comprehend
a2i
human-in-the-loop
augmented ai
aws deequ
aws data wrangler
matplotlib
emr
local zones
outposts
ec2
recap
reinvent
hyper-parameter optimization
katib
tpu kubernetes spark spark ml
swift
redis.ai
tensorflow federated
tensorflow encrypted
udf
privacy
model tracking
continuous experiments
data pipeline
model convergence
reduced precision
fp32
profiling
half-precision
tf.data
model inference
predictive analytics
hardware-specific model optimization
ai for ai
cntk
onnx
mxnet
chainer
tenesorflow
system engineering
kubernetes meetup
mapr
object detection
video classification
model management
a/b testing
big data spain
pipeline ingestion
accelerators
odsc
hdfs
hybrid-cloud
metrics
high availability
intelligent routing
intelligent infrastructure
predicting
scoring
real time
dataset
tensorboard
parameter server
hadoop
scipy
numba
on-premise
hybrid cloud
cloud
azure
visualizations
embedded devices
compiler
functional
jackson streaming
hive
combinators
tesla
int8
p100
k80
malloc
half precision
pascal
convolutional neural networks
quantization
inference
cudamalloc
cudnn
google
scheduler
cluster
tensorfllow
algorithms
momentum
gradient descent
zeppelin
nifi
notebooks
elasticsearch
kafka streams
ipython
jdbc
analytics
partition pruning
erlich
nosql
csv
cache alignment
merge sort
optimization
tungsten
spill to disk
sort-merge join
netty
timsort
compression
asynchronous
cpu cache locality
hash
sort
task scheduling
saturate network
partitioning
throughput
shuffle
scala
algebird
best practices
tuning
data ingestion
sampling
etl
tachyon
monitoring
kinesis
big data kinesis spark streaming approximations la
spark streaming kinesis aws
See more