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
Senior Solutions Engineer
Cloudera

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@PaaSDev
Hadoop {Submarine} Project: Running deep learning workloads on YARN ,
Tim Spann (Cloudera)

@PaaSDev
IoT Edge Processing with Apache NiFi MiNiFi and Multiple Deep Learning Libraries

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

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

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

CLOUDERA DATA SCIENCE WORKBENCH
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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