Edge to AI: Analytics from Edge to Cloud with Efficient Movement of Machine Data

Edge to AI: Analytics from Edge to Cloud with
Efficient Movement of Machine Data
TIMOTHY SPANN JOHN KUCHMEK
Field Engineer Solutions Engineer
Cloudera Cloudera

2 © Cloudera, Inc. All rights reserved.
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Introduction
Tim Spann has been running meetups in Princeton on Big Data technologies since 2015.
Tim has spoken at many international conferences on Apache NiFi, Deep Learning and
Streaming.
https://community.hortonworks.com/users/9304/tspann.html
https://dzone.com/users/297029/bunkertor.html
https://www.meetup.com/futureofdata-princeton/
https://dzone.com/articles/integrating-keras-tensorflow-yolov3-into-apache-ni

Introduction
John Kuchmek recently joined cloudera. Previously he worked at American Water as a data
engineer and a data scientist where he worked extensively with both NiFi and Hadoop.
https://dataworkssummit.com/san-jose-2018/session/bridging-the-gap-
achieving-fast-data-synchronization-from-sap-hana-by-leveraging-hdp-hdf/

DATAFLOW

CLOUDERA FLOW MANAGEMENT
● Web-based user interface
● Highly configurable
● Out-of-the-box data provenance
● Designed for extensibility
● Secure
● NiFi Registry
○ DevOps support
○ FDLC
○ Versioning
○ Deployment

300+ PROCESSORS FOR DEEPER ECOSYSTEM INTEGRATION
Hash
Extract
Merge
Duplicate
Scan
GeoEnrich
Replace
ConvertSplit
Translate
Route Content
Route Context
Route Text
Control Rate
Distribute Load
Generate Table Fetch
Jolt Transform JSON
Prioritized Delivery
Encrypt
Tail
Evaluate
Execute
Fetch
HTTP
Syslog
Email
HTML
Image
HL7
FTP
UDP
XML
SFTP
AMQP
WebSocket

MINIFI EDGE AGENTS
• Edge data collection powered by MiNiFi
• MiNiFi – smaller footprint than NiFi
•Guaranteed delivery
•Data buffering
•Prioritized queuing
•Flow-specific QoS
•Data provenance
•Designed for extension
•C++ / Java agents
•Tensorflow support
• Designed for IoT

MACHINE LEARNING

MACHINE LEARNING AT CLOUDERA
Our philosophy
We empower our customers to
run their business on data with an
open platform:
● Your data
● Open algorithms
● Running anywhere
We accelerate enterprise data science
We help clients build their AI factory

OUR APPROACH
Modern enterprise platform, tools and expert guidance to help you unlock
business value with ML/AI
Agile platform to build,
train, and deploy many
scalable ML applications
Enterprise data science
tools to accelerate team
productivity
Expert guidance,
services & training to
fast track value & scale

© Cloudera, Inc. All rights reserved. 13© Cloudera, Inc. All rights reserved.
WE DELIVER AN ENTERPRISE DATA CLOUD
IoT, Ingest &
Streaming
Data
Engineering
Data
Warehouse
Operational
Database
Machine
Learning
Catalog | Schema | Migration | Security | Governance
Hybrid
Cloud
Public
Multi-Cloud
Edge
Datacenter

MACHINE LEARNING IS BUILT ON DATA MANAGEMENT
We deliver an Enterprise Data Cloud for any data, anywhere, from the edge to AI
DataFlow &
Streaming
Data
Engineering
Data
Warehouse
Operational
Database
Machine
Learning
Catalog | Schema | Migration | Security | Governance
Hybrid
Cloud
Public
Multi-Cloud
Edge
Datacenter
Enterprise grade
Secure, performant and compliant
Scalable
Elastic, cost-effective and lower TCO
Runs anywhere
Public cloud, on-premises, multi, hybrid

PLATFORMS FOR INDUSTRIALIZED AI
Manage pipelines + models
Deploy models
Automate pipelines
Monitor performance
DEPLOYDEVELOP
Make teams more productive
Explore data
Develop reports, pipelines, models
Collaborate with peers
TRAIN
Scale resources efficiently
Train models
Tune parameters
Track performance
End-to-end machine learning infrastructure for teams building at scale
MANAGE
Run anywhere with a common architecture
Manage access and resources
Scale cost with usage

INDUSTRIALIZED AI REQUIRES LARGER DATA PLATFORM
Streaming
Ingest
Batch Ingest
Machine
Learning Tools
BI Tools and
SQL Editors
Data Products
DATA, METADATA, SECURITY, GOVERNANCE, WORKLOAD MANAGEMENT
MACHINE
LEARNING
DATA
ENGINEERING
DATA
WAREHOUSE
OPERATIONAL
DATABASE

MACHINE LEARNING PHASES
Where to Connect to Apache NiFi

Speed of Data Model Training Model Scoring Use Case
Batch
Batch
Batch
Batch Reporting,
Analytics,
Applications
Online
DS Applications/
Interactive
Dashboards
Streaming
In-stream
Streaming
Applications
Incremental/Online In-stream
Streaming
Applications
Training, Scoring and Monitoring

CLOUDERA DATA SCIENCE WORKBENCH
Accelerate machine learning from research to production
For data scientists
• Experiment faster
Use R, Python, or Scala with on-
demand compute and secure
CDH/HDP data access
• Work together
Share reproducible research
with your whole team
• Deploy with confidence
Get to production repeatedly
and without recoding
For IT professionals
• Bring data science to the data
Give your data science team
more freedom while reducing
the risk and cost of silos
• Secure by default
Leverage common security and
governance across workloads
• Run anywhere
On-premises or in the cloud

ACCELERATED DEEP LEARNING WITH GPUS
Multi-tenant GPU support on-premises or cloud
• Extend CDSW to deep learning
• Schedule & share GPU resources
• Train on GPUs, deploy on CPUs
• Works on-premises or cloud
CDSW
GPUCPU
CDH
CPU
CDH
CPU
single-node
training
distributed
training, scoring
“Our data scientists want GPUs, but
we need multi-tenancy. If they go to
the cloud on their own, it’s expensive
and we lose governance.”
GPU On CDH coming in C6

DEMONSTRATION

INTRODUCING MODELS
Machine learning models as one-click microservices (REST APIs)
Model APIs made easy!
1. Choose Python/R file, e.g. score.py
2. Choose function, e.g. forecast
f = open('model.pk', 'rb')
model = pickle.load(f)
def forecast(data):
return model.predict(data)
3. Choose resources

Select a Project, Create a Session, Load Libraries and Data

Load a File and Run It

Install Python Libraries for Python 2 or Python 3

Test your function with an argument

Create a model from that file and function

CLOUDERA DATA SCIENCE WORKBENCHList All The Models

CLOUDERA DATA SCIENCE WORKBENCHDeploy the Model

CLOUDERA DATA SCIENCE WORKBENCHCheckout The Build

CLOUDERA DATA SCIENCE WORKBENCHTest the Model

CLOUDERA DATA SCIENCE WORKBENCHValidate the Model Results

CLOUDERA DATA SCIENCE WORKBENCHMonitor The Running Models

CLOUDERA DATA SCIENCE WORKBENCHInvoke the Model From Apache NiFi In Flow

CLOUDERA DATA SCIENCE WORKBENCHQuery Results of Classification in Flow
{ "class1": "cat", "cpu": 38.3, "end": "1549672761.1262221",
"host": "gluoncv-apache-mxnet-29-50-7fb5cfc5b9-sx6dg", "memory": 14.9,
"pct1": "98.15670800000001",
"shape": "(1, 3, 566, 512)", "systemtime": "02/09/2019 00:39:21",
"te": "3.380652666091919"
}
CLOUDERA DATA-IN-MOTION (APACHE NIFI)

CLOUDERA DATA SCIENCE WORKBENCHIntegrating Calls to CDSW Jobs
CLOUDERA DATA-IN-MOTION (APACHE NIFI)

CLOUDERA DATA SCIENCE WORKBENCHPySpark Job for HDFS Storage

CLOUDERA DATA SCIENCE WORKBENCHPySpark Job Receiving REST API

CLOUDERA DATA SCIENCE WORKBENCHNiFi Job Integration

CLOUDERA DATA SCIENCE WORKBENCHDisplay Data

Edge to AI: Analytics from Edge to Cloud with Efficient Movement of Machine Data

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Edge to AI: Analytics from Edge to Cloud with Efficient Movement of Machine Data