In this webinar, we introduce Cloudera Machine Learning, Cloudera’s new cloud-native machine learning platform powered by Kubernetes that makes it easy to quickly provision environments and scale resources for end-to-end data engineering and machine learning workflows, with secure data access and a unified experience across on-premises, public and hybrid cloud environments.

1. INTRODUCING CLOUDERA MACHINE LEARNING
Cloud-Native Platform for Enterprise Scale Machine Learning
Tristan Zajonc, CTO for Machine Learning

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DISCLAIMER
The information in this document is proprietary to Cloudera. No part of this document may be reproduced
or disclosed without the express prior written permission of Cloudera.
This document is not binding upon Cloudera in any way (including with respect to Cloudera’s course of
business, product strategy, future releases, or development commitments), and is not a part of or subject to
any license agreement or other agreement you may have with Cloudera. Cloudera makes no commitments
about any future developments or functionality in any Cloudera product. The development, release, and
timing of release of any software features or functionality described in this document remains at the
discretion of Cloudera and you should not rely on any statements about development plans or anticipated
functionality in this document when making any purchasing decisions.
Cloudera assumes no responsibility for errors or omissions in this document. Cloudera does not warrant
the accuracy or completeness of the information, text, graphics, links or other items contained within this
material. This document is provided without a warranty of any kind, either express or implied, including but
not limited to the implied warranties of merchantability, fitness for a particular purpose or non-infringement.
Cloudera shall have no liability for damages of any kind including without limitation direct, special, indirect
or consequential damages that may result from the use of these materials.”

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Only
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Let’s get started. Have you
• Used Spark?
• Used Cloudera Data Science Workbench?
• Used Kubernetes?
• Used GPUs for deep learning?
POLL QUESTION 1

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THE OPPORTUNITY: THE INDUSTRIALIZATION OF AI
PROTECT
business
CONNECT
products &
services (IoT)
DRIVE
customer insights
Thousands of use cases for ML and AI to unlock value within the enterprise
● Predictive maintenance
● Logistics optimization
● Self-driving cars
● Medical diagnostics
● Marketing effectiveness
● Next best action
● Insider threat
prevention
● Fraud prevention
● Payment integrity

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A TIPPING POINT OF DATA, COMPUTE, AND ALGORITHMS
Three major trends enable the potential of machine learning
COMPUTEDATA! ALGORITHMS

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MACHINE LEARNING AT UBER, NETFLIX, AND FACEBOOK
Industrialized AI requires requires new supporting tools and platforms
Facebook
FBLearner
Uber
Michelangelo
Netflix
Recommendation
Platform

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UBER
Michelangelo: Uber’s Machine Learning Platform
Source: https://eng.uber.com/michelangelo/
DATA CENTER
PUBLIC CLOUD

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NETFLIX
Netflix recommendation platform
Source: https://medium.com/netflix-techblog/netflix-at-spark-ai-summit-2018-5304749ed7fa
PUBLIC CLOUD

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FACEBOOK
Facebook’s AI infrastructure
Source: https://www.slideshare.net/KarthikMurugesan2/facebook-machine-learning-infrastructure-2018-slides
DATA CENTER

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LESSON 1: ML AT SCALE REQUIRES A UNIFIED DATA STRATEGY
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

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LESSON 2: EMERGING CONSENSUS ON MODERN ML STACK
Big Data
Platform
ML/AI
Frameworks
Container
Infrastructure

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LESSON 3: AGILITY REQUIRES SELF-SERVICE PLATFORM
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

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LESSON 4: LEVERAGE THE BEST INFRASTRUCTURE FOR TASK
Non-Cloud Private Cloud Public Cloud PaaS
I want to
maximize
• Cost-efficiency • Control, elasticity,
and convenience
• Control, elasticity,
and convenience
• Agility
I want to
minimize
• Dependence on
unproven technology
• Resource contention
between departments
• Dependence on data
center floor space
• Dependence on IT
and therefore need
as simple as possible
I want to
standardize
• On whatever
provides the best
ROI
• On a single
environment for the
entire data center
• On a single cloud
provider for all
infrastructure needs
• On whatever is
easiest to use
I want to
store my data
• On premises
because cheaper
and/or more secure
• On premises due to
company /
government mandate
• In the cloud because
easier
• In the cloud because
easier

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TODAY: CLOUDERA DATA SCIENCE WORKBENCH
Accelerate Machine Learning from Research to Production on CDH/HDP
For data scientists
• Experiment faster
Use R, Python, or Scala with on-
demand compute and secure
CDH data access
• Work together
Share reproducible research
with your whole team
• Deploy with confidence
Get to production repeatably
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

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TODAY: CURRENT CDSW ARCHITECTURE
Containerized gateway to CDH/HDP cluster for data scientists
• Benefits
• Easy to attach for self-service data science
• Seamless connectivity to existing clusters
• Isolated, reproducible user environments
• Tradeoffs
• Requires sizing, managing two clusters
• Requires dependency coordination
• Not optimized for distributed training
• Complex, inelastic setup in public cloud
CDH/HDP CDH/HDP
Cloudera Manager / Ambari
gateway node(s) CDH/HDP nodes
Hive, HDFS, ...
CDSW CDSW
...
Master
...
Engine
EngineEngine
EngineEngine

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INTRODUCING

18. 18 © Cloudera, Inc. All rights reserved. 18
enable the end-to-end machine learning lifecycle for teams
building thousands of applications across petabytes of data
with an enterprise-ready platform built for the cloud
Rapid provisioning and
experimentation
DevOps mindset
From development to
production
Team
collaboration
and shared
context
Any
framework,
your data, your
models
Secure by default
CPUs, GPUS, TPUs,
and beyond
Massive, diverse
data
Automation Runs anywhere

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CLOUDERA MACHINE LEARNING (PRIVATE PREVIEW)
Cloud-native enterprise machine learning platform
DATA SCIENCE DATA ENGINEERING MODEL OPERATIONS
CLOUDERA ML RUNTIME
Python/R, Spark, TensorFlow, CPU/GPU-Optimized
Interactive Development Batch Pipelines Predictive APIs
Full capability of CDSW
Rapid cloud provisioning
and elastic autoscaling
Unified data engineering and
ML with seamless
dependency management
Multi-cloud portability
powered by Kubernetes
Connects to HDFS or
cloud object storage
and shared metadata
Accelerated deep learning
with distributed GPU training
* Initially targeted for cloud managed
K8s services, then OpenShift
KUBERNETES
EKS, AKS, GKE, OpenShift
DATA CATALOG
GOVERNANCESECURITY LIFECYCLEWORKLOAD XM
STORAGE Amazon
S3
Microsoft
ADLS
HDFS KUDU

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Requires and extends CDH/HDP, pushing
distributed compute to the cluster
CDH / HDP CDH / HDP
Cloudera Manager / Ambari
gateway node(s) CDH/HDP nodes
Hive, HDFS, ...
CDSW CDSW
...
Master
...
Engine
EngineEngine
EngineEngine
Cloud optimized, elastic distributed compute;
can optionally use CDH / HDP
Kubernetes
(EKS, GKE, AKS,
OpenShift)
CDH / HDP CDH / HDP
Hive, HDFS,
HMS, Sentry,
Navigator
CML CML
...
Master
...
Engine
EngineEngine
EngineEngine
Object
Store
Altus Director / CloudBreak
OPTIONAL
DATA SCIENCE WORKBENCH vs. CLOUDERA MACHINE LEARNING
Cloud ML
APIs

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• Lightweight form-factor optimized for cloud and private cloud
• Elastic compute (CPU / GPU) in the cloud
• Unified workflow for distributed data engineering and machine learning
• Simple dependency management, particularly for ML workloads
• python: pip install, R: install.packages, etc
WHY SPARK ON KUBERNETES?

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cldr ml run --cmd “python train.py”

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from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
results = spark.sparkContext
.parallelize(xrange(0, 1000))
.map(f)
.collect()

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DEMO

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OPERATIONAL EXPERIENCE
✓ Simple installation and management
✓ Lower costs with elastic compute
✓ Easier to support through self-service
✓ Cloud agility everywhere
RESEARCH EXPERIENCE
✓ Instant access to resources
✓ Faster model training and iteration
✓ Unified data engineering and ML
✓ Easier to onboard team members
DEPLOYMENT EXPERIENCE
✓ Easier to scale out compute
✓ Faster deployment of models
✓ Better reproducibility and isolation
✓ Flexible deployment targets
BENEFITS OF A CLOUD-NATIVE ML PLATFORM

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What capability of Cloudera ML is most interesting for your workloads?
• Simple installation and management
• Autoscaling in the cloud
• Dependency management for Spark
• Distributed GPU training
• Cloud experience on-premise
• Potential multi-tenancy with other Kubernetes workloads
POLL QUESTION 2

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Request access to the private preview
tiny.cloudera.com/CML
QUESTIONS?

28. THANK YOU

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Kubernetes
CMLCML
Engine
Spark
Driver
Spark
Executor
Logging Logging
Project
Volume
Project
Volume
Spark
Executor
Project
Volume
Hive, HDFS,
HMS, Sentry,
Navigator
- Create user service accounts and namespace
for quota management & security
- Configure Spark driver / executor
configuration to connect to K8S API
- Populate kerberos related information for
accessing HDFS and other CDH services
- Populate dependency management related
configurations using pod template: image,
volumes, CPU/GPU/memory resources, etc
- Configure Spark logging and metric
information to push to external storage
HOW DOES SPARK IN CML WORK?