Data science holds tremendous potential for organizations to uncover new insights and drivers of revenue and profitability. Big Data has brought the promise of doing data science at scale to enterprises, however this promise also comes with challenges for data scientists to continuously learn and collaborate. Data Scientists have many tools at their disposal such as notebooks like Juypter and Apache Zeppelin & IDEs such as RStudio with languages like R, Python, Scala and frameworks like Apache Spark. Given all the choices how do you best collaborate to build your model and then work through the development lifecycle to deploy it from test into production ? In this session learn the attributes of a modern data science platform that empowers data scientists to build models using all the data in their data lake and foster continuous learning and collaboration. We will show a demo of DSX with HDP with the focus on integration, security and model deployment and management. Speakers: Sriram Srinivasan, Senior Technical Staff Member, Analytics Platform Architect, IBM Vikram Murali, Program Director, Data Science and Machine Learning, IBM

1. © 2017 IBM Corporation
Scaling Data Science on Big Data
Vikram Murali
Program Director, Data Science & Machine Learning, IBM
Sriram Srinivasan
STSM & Architect, Data Science & Machine Learning, IBM

2. IBM Analytics
3 © 2017 IBM Corporation<#>
Data Scientist Pain Points
 Where is the data I need to drive business insights?
 I don’t want to know Hadoop/Hive etc
How do I collaborate and share my work with others?
 What is the best visualization technique to tell my story?
 How do I bring my familiar R/Python libraries to this new Data Science platform?
 How do I learn to use the latest libraries/Technique? (TensorFlow, Scikit learn, XGBoost,…) and
how do I ensure the right set of compute resources for these ?
How are my Machine Learning Models performing & how to improve them?
 I have this Machine Learning Model, how do I deploy it in production?
Machine Learning & Data Science

3. IBM Analytics
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Challenges for the Enterprise
 Ensure secure data access & auditability - for governance and compliance
 Control and Curate access to data and for all open source libraries used
 Explainability and reproducibility of machine learning activities
 Improve trust in analytics and predictions
 Efficient Collaboration and versioning of all source, sample data and models
 Repeatability of process
 Establish Continuous integration practices
 Agility in delivery
 Publish/Share and identify provenance/ lineage with confidence
 Visibility and Access control
 Effective Resource utilization and ability to scale-out on demand
 Balance resources amongst different data scientists, machine learning practioners' workloads
Machine Learning & Data Science

4. IBM Analytics
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Why has this been hard ?
 Rigid toolsets & absence of an integrated platform
 Have to choose one and only one approach
 Cannot easily connect all of the capabilities required
 Difficult to navigate between the various tools used
 Fragmented and time consuming practices
 Result of using multiple disjoint environments
 Separate on-ramp/community for each tool/environment
 Does not yield meaningful meta data or complete data lineage
 Analytical Silos
 Difficult to maintain and version control project assets
 Limited means of collaborating with teams
 Results are difficult to share and audit
Machine Learning & Data Science
 Resource Management Complexity
 Lack of scalable infrastructure
 Inflexible resource prioritization
techniques

5. IBM Analytics
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Introducing IBM Data Science Experience
• Projects and Version Control
• Spark-in-DSX and Remote Spark
• IBM Machine Learning tech - algorithms
& more
• Platform Manager – for easy
administration
• Compute Elasticity support
IBM Data Science Experience
Community Open Source IBM Added Value
• Find tutorials and datasets
• Connect with other Data Scientists
• IBM ML Hub for expert assistance
• Open Source evangelism
• Fork and share projects, samples
• Code in Scala/Python/R/SQL
• Zeppelin & Jupyter Notebooks
• RStudio IDE
• Anaconda distribution
• Add your favorite libraries
Machine Learning & Data Science

6. IBM Analytics
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IBM Data Science Experience
DSX on Public Cloud DSX Desktop DSX Local on Private Cloud
 PayGo consumption with as-a-service
delivery, up & running in seconds
 Integrated with IBM Spark-as-a-Service for
compute, IBM Object Store for data, as well
as other platform assets
 Immediate cloud collaboration via RStudio
and Jupyter notebooks
 Easily installed on your laptop or PC
 Won’t scale beyond the hardware available on
your machine
 Access to RStudio and Jupyter notebooks,
powered by one small Spark worker operating
locally on your machine
 Load CSV data files into Data Frames
 Scalable DSX cluster deployed on your
private infrastructure
 Dockerized containers via Kubernetes
 DSX Local can also deploy with
Hortonworks Data Platform on-premises
 LDAP for user management and
authentication
 Easy collaboration, versioning with Projects
& git
Built-in Zeppelin & Jupyter Notebooks
and RStudio for visualizing and coding on
data science tasks using Python, R, &
Scala.
Built-in Spark parallelizes & accelerates
data science tasks.
Machine Learning & Data Science

7. IBM Analytics
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Machine Learning Workflow in Data Science Experience
 Machine learning detects if models fall out of spec — and automatically triggers retraining
 Fully integrated model management means data scientists, app developers & operations can use
the same environment
Machine Learning & Data Science
Data
Live
SystemIngest
Data
Processing
Model
Training
Deployment &
Management
Creating samples &
Cleansing
Automating Data Science Workloads Scalable
Deployment
Feedback Loop
 Historical
 Streaming
 Data visualization
 Feature transform
and engineering
 Model selection
and evaluation
 Pipelines, not
only models
 Versioning
 Predict when
given new data
 Monitoring and
live evaluation
Models
lose accuracy
Data Scientists
+ Researchers
ML Engineers
+ Production Engineers
Data
Engineers

8. IBM Analytics
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Data Science
Experience
Machine Learning Everywhere – An Open Platform
 Add your favorite libraries
 Publish Open APIs for secure ML applications
Machine Learning & Data Science

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DSX Local Architecture
Machine Learning & Data Science

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DSX Scale out in Kubernetes is simple
 DSX-Spark scale-out is automatically done by adding more compute nodes (via “Daemon Sets”)
 Remote Spark can be independently scaled out as usual (say in Hadoop/Yarn)
 Individual workload Isolation and scale-out in pods
 Each DSX individual user (or an entity, in general) gets a Kubernetes namespace assigned, making
metering simple.
 All containers (pods) for that user gets spawned in that namespace, such as for tools – Jupyter/Zeppelin
(Python) or R/RStudio as well as other non-spark jobs.
 Namespace provides total quota for that user with resource requests and limits set in each pod
deployment
 “Shared” services are load balanced (with HA support) across all user access by typical
Kubernetes techniques, such as via replicas of pods & DNS-routing via Kubernetes services.
Machine Learning & Data Science

11. IBM Analytics
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Data Science Experience with Hortonworks Data Platform
Big Data
DSX

12. IBM Analytics
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Data Science Experience with HDP –Roadmap
DSX & HDP interoperability
Side-by-Side Installation
DSX on-the-edge integrated &
optimized for HDP deployments
DSX Jupyter, RStudio &
Zeppelin and Machine Learning
services enabled for HDP data
sources
Yarn managed Spark leveraged
by DSX, via Livy
• Spark jobs pushed to HDP
cluster
Single Cluster
DSX Within HDP Cluster
Dedicated nodes for DSX in the
HDP cluster with Ambari-based
installation/configuration.
Deploy & scale DSX with Yarn
managing DSX as a top-level
application
Knox, Ranger & Atlas integration
for authentication, authorization &
governance
Fully Yarn Managed DSX
Workloads
HDP embeds Kubernetes in Yarn,
enables launch and integration of
Kubernetes pods as Yarn
containers
Yarn manages all workloads in a
granular fashion across the entire
HDP cluster
• Python & R workloads (non-
spark) also managed by Yarn
• GPU affinity , especially for
Deep Learning Jobs
Today Q4 2017 1st Half 2018
1
Machine Learning & Data Science

13. IBM Analytics
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Goal: Enterprise IaaS for Data Scientists
 Efficient Compute Resource Management for large-scale Analytics, Machine Learning and Deep
Learning workloads
－Enable Data Scientists to procure resources from a shared compute “grid” for any kind of activity from
interactive notebooks & IDEs to training Jobs or scheduled scripts and Apps.
－All compute manifested as Docker containers/Kubernetes pods
 HDP/Yarn as the Resource Manager
－Enable all workloads, whether Map Reduce or Spark Jobs or DSX/ML activities to be uniformly handled by
the HDP/Yarn scheduler.
－Manage Queue Priorities, balancing of workloads and scale-out for the whole cluster providing best
utilization of all resources.
 Yarn and Kubernetes - the best of both worlds !
Machine Learning & Data Science

14. IBM Analytics
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Call to Action
Experience DSX & ML Today…
IBM DSX at http://datascience.ibm.com
DSX Local recorded demos
Machine Learning: https://www.youtube.com/watch?v=htGZ1Iomeec
Connecting to external Spark: https://www.youtube.com/watch?v=rA0Rlb2M_oI
Spark submit from external app: https://www.youtube.com/watch?v=TETAT9pC9_o
Administration experience: https://www.youtube.com/watch?v=htGZ1Iomeec
Birds of a Feather
session
6pm Thursday, C 4.5
Machine Learning & Data Science

15. © 2017 IBM Corporation
THANK YOU
IBM Data Science Experience
Vikram Murali
Program Director, Data Science & Machine Learning, IBM
Sriram Srinivasan
STSM & Architect, Data Science & Machine Learning, IBM