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KATANA GRAPH |
TM
Katana Graph
August 16, 2022 - Greg Steck
DBTA Panel Session
Graph Intelligence Platform for High-Performance
Computing AI and ML
Partner
KATANA GRAPH |
TM
Katana Graph
2
Keshav Pingali
CEO, Co-founder
Prof UT Austin,
Academia Europeana,
Fellow ACM, IEEE, AAAS
Chris Rossbach
CTO, Co-founder
Prof UT Austin
MSR, VMware, Canesta
Farshid Sabet
CBO
Intel, Movidius,
Aptina, SanDisk
LEADERSHIP
• 85 employees: 70 engineering (15 PhDs) + 15 business
• Leaders in graph, AI/ML, distributed/parallel systems
• Offices in: Austin, Bay Area, NYC, and Denver
• Founded in March 2020
COMPANY
Experienced Team
KATANA GRAPH |
TM
Why Graphs?
Real World is not represented as Table
3
Intel Confidential
Relational Columnar Hierarchic
Connected
entities attributes relations
“Graphs are the native format, the most natural data
structure of the world’s data.”
– Jensen Huang (Nvidia CEO)
Real
World
Routes
IOT
Financial
Telco
Life Science Social Net
KATANA GRAPH |
TM
Graph Compute domains
04
Graph
Database
(Query)
Graph AI
and
Machine
Learning
Graph
Analytics and
Mining
Graph Query
Graph AI
Graph Analytics
KATANA GRAPH |
TM
Katana Graph Technology Differentiation
5
SPEED and SIZE
Katana Graph performs
10x - 100x faster than
competing graph
platforms
SCALE
Scale OUT: Our platform
scales beyond 256
machines
FLEXIBILITY
Multiple Workloads : Unified
Graph AI/ML, Querying,
Analytics, 3rd Party
integrations
Cloud Native
Support AWS, GCP, Azure
Proven to Scale UP with new
CPU generations
Support heterogenous
hardware : CPU, GPU, xPUs,
Optane
Handles massive Graphs –
proven with 40B vertices,
50B edges graph
Containerized deployment
support
Separation of Storage and
Compute enabling Scale
up/down on demand
Support heterogeneous
data types
Open and Developer
friendly
Non-proprietary
OpenCypher support
Data Scientist friendly
Python Jupyter interface
REST API interface
AI framework
(i.e. PyTorchG) native
integration
Databricks (Spark), Ray,
DASK integration
Graph
Query
Graph
AI
Graph
Analytics
KATANA GRAPH |
TM
KATANA GRAPH |
TM
Graph Technology
Application Areas
06
Platforms
Finance
Healthcare
Retail
Energy Industrial
Telecom
Genomics Anti-Money
Laundering
Drug
Discovery
Identity
Graph
Precision
Medicine
Electronic
Circuit Design
Tools
Knowledge
Graph
Predictive
Monitoring
Intrusion
detection
Supply Chain
Optimization
Fraud
Detection
Real Time
Analytics
Customer
360
Recommendation
Social
Networks
KATANA GRAPH |
KATANA GRAPH |
Use Case – Financial Services
07
KATANA GRAPH |
Making credit decisions using Graph Neural Networks
8
Traditional consumer data represented as a graph
Node Classification Task:
Determine if Abby is a “good”
credit or a “bad” credit
Predict: Default / Non-Default
KATANA GRAPH |
Graph neural network message passing
9
1. Aggregate features
(e.g. mean, max, sum)
2. Apply neural network
KATANA GRAPH |
Why do GNN’s improve model accuracy?
10
Traditional machine learning
relies on modelers to perform
feature engineering and
aggregate which tradeline
features that are most
impactful.
GNNs can automate the
feature engineering process
and learn which features from
the tradeline are most
important for credit
decisioning
1. Ability to encode high-dimensional data
KATANA GRAPH |
Why do GNN’s improve model accuracy?
11
Consumers can be
connected through various
paths (e.g. sharing same
subscriber)
GNN model can learn from
these relationships to
uncover credit signals
2. Learning from complex relationships between consumers
KATANA GRAPH |
KATANA GRAPH
TM
12
Thank You
Contact:
Greg Steck
gsteck@katanagraph.com

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Graph Intelligence Platform for High-Performance Computing AI and ML

  • 1. KATANA GRAPH | TM Katana Graph August 16, 2022 - Greg Steck DBTA Panel Session Graph Intelligence Platform for High-Performance Computing AI and ML Partner
  • 2. KATANA GRAPH | TM Katana Graph 2 Keshav Pingali CEO, Co-founder Prof UT Austin, Academia Europeana, Fellow ACM, IEEE, AAAS Chris Rossbach CTO, Co-founder Prof UT Austin MSR, VMware, Canesta Farshid Sabet CBO Intel, Movidius, Aptina, SanDisk LEADERSHIP • 85 employees: 70 engineering (15 PhDs) + 15 business • Leaders in graph, AI/ML, distributed/parallel systems • Offices in: Austin, Bay Area, NYC, and Denver • Founded in March 2020 COMPANY Experienced Team
  • 3. KATANA GRAPH | TM Why Graphs? Real World is not represented as Table 3 Intel Confidential Relational Columnar Hierarchic Connected entities attributes relations “Graphs are the native format, the most natural data structure of the world’s data.” – Jensen Huang (Nvidia CEO) Real World Routes IOT Financial Telco Life Science Social Net
  • 4. KATANA GRAPH | TM Graph Compute domains 04 Graph Database (Query) Graph AI and Machine Learning Graph Analytics and Mining Graph Query Graph AI Graph Analytics
  • 5. KATANA GRAPH | TM Katana Graph Technology Differentiation 5 SPEED and SIZE Katana Graph performs 10x - 100x faster than competing graph platforms SCALE Scale OUT: Our platform scales beyond 256 machines FLEXIBILITY Multiple Workloads : Unified Graph AI/ML, Querying, Analytics, 3rd Party integrations Cloud Native Support AWS, GCP, Azure Proven to Scale UP with new CPU generations Support heterogenous hardware : CPU, GPU, xPUs, Optane Handles massive Graphs – proven with 40B vertices, 50B edges graph Containerized deployment support Separation of Storage and Compute enabling Scale up/down on demand Support heterogeneous data types Open and Developer friendly Non-proprietary OpenCypher support Data Scientist friendly Python Jupyter interface REST API interface AI framework (i.e. PyTorchG) native integration Databricks (Spark), Ray, DASK integration Graph Query Graph AI Graph Analytics
  • 6. KATANA GRAPH | TM KATANA GRAPH | TM Graph Technology Application Areas 06 Platforms Finance Healthcare Retail Energy Industrial Telecom Genomics Anti-Money Laundering Drug Discovery Identity Graph Precision Medicine Electronic Circuit Design Tools Knowledge Graph Predictive Monitoring Intrusion detection Supply Chain Optimization Fraud Detection Real Time Analytics Customer 360 Recommendation Social Networks
  • 7. KATANA GRAPH | KATANA GRAPH | Use Case – Financial Services 07
  • 8. KATANA GRAPH | Making credit decisions using Graph Neural Networks 8 Traditional consumer data represented as a graph Node Classification Task: Determine if Abby is a “good” credit or a “bad” credit Predict: Default / Non-Default
  • 9. KATANA GRAPH | Graph neural network message passing 9 1. Aggregate features (e.g. mean, max, sum) 2. Apply neural network
  • 10. KATANA GRAPH | Why do GNN’s improve model accuracy? 10 Traditional machine learning relies on modelers to perform feature engineering and aggregate which tradeline features that are most impactful. GNNs can automate the feature engineering process and learn which features from the tradeline are most important for credit decisioning 1. Ability to encode high-dimensional data
  • 11. KATANA GRAPH | Why do GNN’s improve model accuracy? 11 Consumers can be connected through various paths (e.g. sharing same subscriber) GNN model can learn from these relationships to uncover credit signals 2. Learning from complex relationships between consumers
  • 12. KATANA GRAPH | KATANA GRAPH TM 12 Thank You Contact: Greg Steck gsteck@katanagraph.com