At Monsanto, emerging technologies such as IoT, advanced imaging and geo-spatial platforms; molecular breeding, ancestry and genomics data sets have made us rethink how we approach developing, deploying, scaling and distributing our software to accelerate predictive and prescriptive decisions. We created a Cloud based Data Science platform for the enterprise to address this need. Our primary goals were to perform analytics@scale and integrate analytics with our core product platforms. As part of this talk, we will be sharing our journey of transformation showing how we enabled: a collaborative discovery analytics environment for data science teams to perform model development, provisioning data through APIs, streams and deploying models to production through our auto-scaling big-data compute in the cloud to perform streaming, cognitive, predictive, prescriptive, historical and batch analytics@scale, integrating analytics with our core product platforms to turn data into actionable insights.

1. Turn Data into Actionable Insights!!

2. About me:
Vishnu Alavur Kannan
Analytics Technical Platforms Lead
• 15+ years in IT, software engineer @heart
!
• Lead engineering teams through out my career
!
• Platform is just vaporware without passionate people
!
• A players make all the difference in software engineering
✓ 50:1, 100 :1, rarely on any other profession
@Monsanto for two reasons:
!
• I strongly believe in our commitment to sustainable agriculture
!
• I am able to do top-flight Engineering R&D
✓ Complex engineering challenges keeps me going
✓ Freedom to operate:
o Use the right tool for the right job
o Solving problems using cutting-edge technologies
o Open-source friendly, Open environment to contribute back

3. • Bringing a broad range of solutions to help
nourish our growing world
• Collaborating to help tackle some of the
world’s biggest challenges
• >20,000 employees in 66 countries
• >50% employees based outside of
the United States
• One of the 25 World’s Best Multinational
Workplaces by Great Place to Work Institute
Monsanto:
A Sustainable Agriculture Company

4. Our systems approach integrates technology platforms to
maximize farmer effectiveness
Crop Protection
• Weed Control (Roundup ®
Branded Agricultural Herbicides)
• Insect Control
• Disease Control
Breeding
• Stress Tolerance
• Disease Control
• Yield
• Vegetables, corn, cotton, soybeans, wheat, canolaBiologicals
• Weed Control
• Insect Control
• Virus Control
• Plant Health
Biotechnology
• Weed Control
• Insect Control
• Stress Tolerance
• Yield / Yield Protection
• NutrientsData Science
• Planting Script Creator
• Increased production
• Efficient land and water use
• Efficient nutrient use

5. https://www.youtube.com/watch?v=l5Tw0PGcyN0
Why do you do what you do?!
What’s the purpose?
How do you do what you do?
What the hell do you do?
THE GOLDEN CIRCLE
Simon Sinek

6. Identify the signals from the noise @SCALE
Volume
DATA AT SCALE
Variety
VARIOUS FORMS OF DATA
Velocity
STREAMING IOT
Veracity
DATA UNCERTAINITY
DIGITALMEDIA:280Exabytes
FB:300+Petabytesperday
* Information from multiple sources are adapted and incorporated
POINTS,POLYGONS,
RASTERS,VECTORS
CONNECTINGDATAACROSSSOURCES,
ISWHEREANALYSTSSPENDMOSTOFTHEIRTIME
SENSORSarede-factoto
gatherdata anddetect
anomaliesacrossdomains

7. Monsanto re-inventing Agriculture through Analytics
Other providers:
Cost
Qualit
y
Agility
• No hardware administration, less software
administration
• Eleven 9’s of data durability
• Harness state-of-the-art software services
!
• DevOps moving towards NoOps
• Provision Infrastructure in seconds:
infrastructure as code - automation
• Grow or shrink compute to match seasonal
workloads and pay smartly as we go
Scale: MON has ~1016+ bytes of data and growing rapidly
• Global Presence: Taking data driven
products & services closer to business
!
• Ability to accelerate feature
development, integrating analytics rapidly
into our workflows @scale
!
• Ingest, store & retrieve massive data sets,
by using the right data store to our
competitive advantage (NoSQL/SQL)
!
• Service diversity, Organizational maturity
IOT, Imagery, Geo-spatial, Genomics, Molecular Breeding…..

8. Vision
A year ago as we started…

9. Integrated
Extended
Enhanced
Scalable
Enable Analytics @SCALE for the Enterprise
Reliable
FieldDevices
Apps
Apps
Devices
DevicesApps
DevicesApps
Data
M
odels
M
odels
M
odels
M
odels
Business
Unit-
1
Business
Unit-
2
Business
Unit3
D
igital
Business
Open

10. Integrate Analytics with Product Platforms
Data Data Science@scale Analytical Models
Turn Data Into Actionable Insights
….
….
APIs
Data

11. Predictive Product Placement @scale
PFO
PFO
Topography
Site boundary
Zones
Experiment metadata
Planter A/B line
Automap
Elevation
Soil
Weather
Topography
Zones
Location Data Assets
Geo-spatial Catalog

12. Analytics as a Service
In Collaboration with IT & Business
Scale across teams internalizing a self-service model

13. Internalize the needs to stay ahead of the curve
Addressing analytics needs based on persona
!
Descriptive
What happened?
!
Diagnostic
Why did it happen?
!
Predictive
What will happen?
!
Prescriptive
What should I do?
!
Cognitive
What can be learnt?
Hindsight Insight Foresight
10’s K of users 1’s K 100’s
Science@Scale
Information Pro-Consumers
Information Consumers
Data ScientistsBusiness Users
Business Analysts Statisticians
Business Intelligence
Ad-hoc Analysis Statistical Analysis
!Data DiscoveryReports
Dashboards
Drill Down Machine Learning
Inferential
CausalExploratory
Machine
Power Users
10’s
Computational Biologists
Neural Networks
Outsight
Systems
Natural Language Processing

14. Discovery Analytics – Development Environments
Non-prime
Exploratory
Prime
R & D
Development Environments @SCALE
• Big-data Infra. & DevOps
• Data Provisioning @scale
• Model Deployments @scale
• Big-data workloads
• Computational pipelines
• Transformation pipelines
• Training pipelines
• Sizing & Auto-scaling
• Cloud Best practices
• 24/7 availability
• Monitoring
• Alerting
• ELK stack
• ….
Analytical models
@SCALE
• Co-engineering
• Involve us sooner
• Thinking scale ahead
accelerating Time to Market
• Model development &
refactoring
• R, Asreml, Python, OPL…
• Java, Scala, Clojure…
• Infrastructure as code
• AWS, GCP, AzureML
• Docker, Kubernetes
• Distributed computing
• Architecture
• Solutions Design
• Development
!• API integrations
• KAFKA integrations
• OAUTH2 Integrations
• Security/ISO collaborations
Build it once, deploy frameworks as needed for user groups: Bundled in a centralized eco-system
Non-prod to Prod
BLUE / GREEN

15. Discovery Analytics Development Environments
Data Scientists, Developers and Novice Users
From Discovery to Production
Culture, approach and adoption
Know
Your
Users
For Community
By Community
!
Tailor by Needs
Balance Freedom
with Governance
!
!
!
Drive
User
Adoption
Environments
iteratively served to
everyone @monsanto
Enable analytical capabilities @scale for the enterprise integrated with
Product Platforms
As of today, # of unique data scientists across groups utilizing our discovery analytics environments
Model maturity Global Scalability
Core teams : Train the trainee to share knowledge and best practices utilizing the environment

16. Business Capabilities
Make the platform robust, sharing a few use cases

17. Environmental Classification @scale
Engineered using Discovery Analytics - Development Environment
Data
Provisioning
APIs
Data Transformation QA/QC
Rules
Scala
Python
Scikit
API
API

18. !
• Collaborations with Data Science Teams: Co-engineering R based machine learning model to a
Scala based model training pipeline for scalability
!
• EMR (Amazon Hadoop) & DataProc (GCP) using Apache Spark Computation Engine @scale
• Iterative ON-DEMAND framework, auto-scaling up-to N number of nodes
!
• Training pipeline integration with APIs & co-engineering continuum
Molecular Breeding: Training Pipeline @Scale
Engineered using Discovery Analytics - Development Environment
Data
DATA LEARNER MODEL 1

19. Cognitive Analytics Pipeline
!
• Collaborations with Cognitive Analytics Data science
team to build:
• An integrated Predictive Product Pipeline from
inception to commercialization
!
Built on:
!
• Apache Airflow (incubating): DAG based model
chaining & workflow management platform
• Models written in Python, R
• Parallelism achieved via Celery workers
• Being customized now to utilize Spark
!
• Apache Parquet - Columnar Storage Format on a file
system; extremely parallelizable
!
• Facebook Presto query engine to query parquet’s via
SQLs through REST APIs – highly performant
!
• Cloud Analytics platform integration
• Co-engineering solutions @scale mining millions
of data points to derive actionable insights
Workflow
DAGs
Libraries
Engineered using Discovery Analytics - Development Environment

20. Deep learning @SCALE
Discovery Analytics Development Environments integrated with CloudML on GCP
Collect
Store Train
Predict
Evaluate
Training
Pipeline
Retrain
• First Ever Deep Learning platform for the
Enterprise
!
• Perform Deep Learning @scale on CloudML using
TensorFlow via Jupyter from Prime environment
!
• Integrated with data, Inputs, Outputs and
Metadata including Tensor Board to monitor your
model training runs
Discovery Analytics - Workflow
Production Deployment - Workflow

21. DATA INGESTION AND TRANSFORMATION VIA API’s AND STREAMS
Streaming
Business Intelligence
RUN ANALYTICS@SCALE IN THE CLOUD
Collaborative Data Science - DISCOVERY ANALYTICS
DATA DRIVEN PRODUCTS
KAFKA Streams Data Warehouse*Big-data
Model outputs via APIs & Streams
In-house/Third Party: Platforms
AWS, GCP, Cloudera, DataStax, IBM, Azure, Domino labs…
Prescriptive PredictiveCognitive Historical
Models - Deep Learning, Computational Pipelines, Classification & Simulation Engines
Turn Data into Actionable Insights

22. Our Journey of Transformation
We have just scratched our surface:
!
• Science@scale – Our Cloud Analytics Platform is only a year old
!
• Talent, Behavior and Platform as our 3 key pillars of focus
!
• Talent:
• Building big-data and cloud analytics engineering team
from the ground up – 150+ interviews, 15 people team now
• Targeting A players, nurture the team on new technologies, build leaders
!
• Behavior/Cultural Mind shift: Data Science & IT Engineering operating as ONE TEAM
• Two extreme spectrums
• Finding the sweet spot in the middle has been the cultural shift
• Data science teams have been very supportive, adapting to change
• Bringing in IT best practices: Agile methodologies, versioning, CI….
• Train the trainee approach to enable adoption across the enterprise
• Leverage the best of both worlds by co-engineering solutions
• Collaboration is our new competitive advantage
!
• Platform: We are at ground zero now, continuing to deliver Minimum Viable Products each sprint
• Continue to mature & stay cutting edge on technologies
• Build vs. Buy [Cost, Time, Quality]
• Miles to go before we sleep

23. https://www.youtube.com/watch?v=l5Tw0PGcyN0
Why do you do what you do?!
What’s the purpose?
How do you do what you do?
What the hell do you do?
THE GOLDEN CIRCLE
Simon Sinek
• Help identify the signals from the noise @scale
An Enterprise Cloud Analytics platform to serve:
• Analytics as a service enabling Discovery Analytics
environments for the data science community
• Predictive, prescriptive, streaming,
cognitive, IOT edge analytical capabilities @scale
• Big Data Cloud Analytics Engineering
• Internalize data science needs thinking scale ahead

24. Thank You
Visit us at engineering.monsanto.com
We are looking for passionate big data
cloud analytics engineers to join our team.
https://www.linkedin.com/in/vishnukannan