Deep learning expands boundaries of the possible. Detecting fraud. Predicting claims. Diagnosing cancer. Deep learning solves these problems and many others. However, organizations struggle to make deep learning work. Cloudera—with tools like the Cloudera Data Science Workbench—helps you bring deep learning to your data, for new insights and applications. A demonstration of Cloudera Data Science Workbench is included in the webinar.

1. 1© Cloudera, Inc. All rights reserved.
Deep Learning with Cloudera
Thomas W. Dinsmore
Arun Krishnakumar

2. 2© Cloudera, Inc. All rights reserved.
●Deep Learning: A Proven Technique
●Deep Learning with Cloudera
●How to Move Forward with Deep Learning
●Questions
Deep Learning with Cloudera

3. 3© Cloudera, Inc. All rights reserved.
Deep Learning: A Proven Technique

4. 4© Cloudera, Inc. All rights reserved.

5. 5© Cloudera, Inc. All rights reserved.

6. 6© Cloudera, Inc. All rights reserved.

7. 7© Cloudera, Inc. All rights reserved.
Machine Learning: algorithms and
methods that extract useful patterns
from data.

8. 8© Cloudera, Inc. All rights reserved.
Machine Learning Categories
Linear
Models
Categorical
Models
Bayesian
Methods
Decision
Trees
Artificial
Neural
Networks
Ensemble
Models
Kernel-
Based
Methods
Latent
Variable
Analysis
Cluster
Analysis
Association
Rules
Learning
Evolutionary
Algorithms
Genetic
Algorithms

9. 9© Cloudera, Inc. All rights reserved.
Machine Learning Categories
Linear
Models
Categorical
Models
Bayesian
Methods
Decision
Trees
Neural
Networks
Ensemble
Models
Kernel-
Based
Methods
Latent
Variable
Analysis
Cluster
Analysis
Association
Rules
Learning
Evolutionary
Algorithms
Genetic
Algorithms
Deep
Learning

10. 10© Cloudera, Inc. All rights reserved.
Nodes, the “DNA” of neural networks
Weights
(input from
other nodes)
Transfer
Function
Activation
Function
To other nodes

11. 11© Cloudera, Inc. All rights reserved.
A simple neural network

12. 12© Cloudera, Inc. All rights reserved.
Neural network layers
Input
Hidden
Output

13. 13© Cloudera, Inc. All rights reserved.
Neural network architectures

14. 14© Cloudera, Inc. All rights reserved.
A neural network is “deep” if it has >1 hidden layer
Input Layer
Hidden Layers
Output Layer
…

15. 15© Cloudera, Inc. All rights reserved.
Deep convolutional network

16. 16© Cloudera, Inc. All rights reserved.
Deep recurrent network

17. 17© Cloudera, Inc. All rights reserved.
Deep learning frameworks

18. 18© Cloudera, Inc. All rights reserved.
Advantages
● Learns higher-level features
● Detects complex interactions
These, in turn, make DL practical for:
● High-cardinality target variables
● High-dimension data
● Unlabeled data
Disadvantages
● Technical challenge
● Opaqueness
● Overfitting
● Computationally intensive
● Deployment challenges
Deep learning: why or why not?

19. 19© Cloudera, Inc. All rights reserved.
The Deep Learning “Silo”
Data Platform Deep Learning
Platform
• Latency
• Security issues
• Governance issues
• Deployment issues

20. 20© Cloudera, Inc. All rights reserved.
Deep Learning in Cloudera

21. 21© Cloudera, Inc. All rights reserved.
Bring deep learning to your data (not vice-versa)

22. 22© Cloudera, Inc. All rights reserved.
GPUCPU
• Single-node
training
CDH
CPU
CDH
CPU
• Distributed training
• Transfer learning
• Inference
Deep Learning with Cloudera: On Premises or in the
CloudCloudera Data
Science
Workbench
Apache Spark in
Cloudera

23. 23© Cloudera, Inc. All rights reserved.
Accelerates data science from
development to production with:
●Secure self-service data access
●On-demand compute
●Support for Python, R, and Scala
●Project dependency isolation for
multiple library versions
●Workflow automation, version
control, collaboration and sharing
Cloudera Data Science Workbench
Self-service data science for the enterprise

24. 24© Cloudera, Inc. All rights reserved.
A modern data science architecture
CDH CDH
Cloudera Manager
gateway nodes CDH nodes
●Built on Docker and Kubernetes
●Runs on dedicated gateway nodes
●User sessions run in isolated
“engine” containers which:
○Host Kerberos-authenticated
Python/R/Scala runtimes
○Interact with Spark via YARN
client mode (Driver runs in
container, workers on CDH)
●Single-cluster only (for now)
Hive, HDFS, ...
CDSW CDSW
...
Master
...
Engine
EngineEngine
EngineEngine

25. 25© Cloudera, Inc. All rights reserved.
“Our data scientists want GPUs, but we
can’t find a way to deliver multi-tenancy.
If they go to the cloud on their own, it’s
expensive and we lose governance.”
●Extend existing CDSW benefits to
GPU-optimized deep learning tools
●Schedule & share GPU resources
●Train on GPUs, deploy on CPUs
●Works on-premises or cloud
Accelerated deep learning on-demand with GPUs
Data Science Workbench
GPUCPU
CDH
CPU
CDH
CPU
single-node
training
distributed
training, scoring
Multi-tenant GPU support on-premises or
cloud

26. 26© Cloudera, Inc. All rights reserved.
Demo

27. 27© Cloudera, Inc. All rights reserved.
“Spark is becoming a de facto data science
foundation.”
-- Gartner, Magic Quadrant for Data Science Platforms

28. 28© Cloudera, Inc. All rights reserved.
● Apache Spark is well-established in the enterprise
○Robust ecosystem
○Supports many different data sources
○Large and growing user community
●Run deep learning on existing clusters
○Transfer learning
○ Inference
● Simplifies integration with other ML tools, pipelines
Deep learning on Apache Spark

29. 29© Cloudera, Inc. All rights reserved.
Deep learning in Cloudera with Apache Spark
• Two packages:
• CaffeOnSpark
• TensorFlowOnSpark
• Developed by Yahoo
• Python and Scala APIs
• All DL architectures
• Integrated pipeline
• Open source DL library
• Developed by Skymind
• Built on JVMs
• Supports CPUs and
GPUs
• Java, Scala, Python APIs
• Training and inference
• Imports models from:
• TensorFlow
• Caffe
• Torch
• Theano
• Deep learning framework
• Developed by Intel
• Supports CPUs only
• Leverages Intel MKL
• Scala, Python APIs
• Imports models from:
• TensorFlow
• Caffe
• Torch
Spark Packages DL4J BigDL

30. 30© Cloudera, Inc. All rights reserved.
● Train in Cloudera Data Science Workbench
○ Works with all frameworks
○ GPUs on demand
● Deploy in Apache Spark
● Your data remains in place
● Bring deep learning to your data, not the other way around
Deep learning with Cloudera.

31. 31© Cloudera, Inc. All rights reserved.
Cloudera Customers Use Deep Learning

32. 32© Cloudera, Inc. All rights reserved.

33. 33© Cloudera, Inc. All rights reserved.

34. 34© Cloudera, Inc. All rights reserved.

35. 35© Cloudera, Inc. All rights reserved.
Moving Forward…

36. 36© Cloudera, Inc. All rights reserved.
● Stay focused on solving business problems
● Choose pilot projects carefully
○ Image, video classification and tagging
○ Object recognition
○ Handwriting recognition
○ Speech recognition
○ Speech translation
○ Text processing
● Organize data flows first
● Embrace open source frameworks
● Leverage transfer learning
● Don’t create new silos
● Use (mostly) mainstream hardware
How to Move Forward with Deep Learning

37. 37© Cloudera, Inc. All rights reserved.
Questions

38. 38© Cloudera, Inc. All rights reserved.
Thank you
Your name and contact info