Understandable Artificial Intelligence
Infrastructure Review
Building On Principals, A New Frontier
The EDS Infrastructure -- Big Picture
©2018 Economic Data Sciences
Cloud Resources
We seamlessly combine physical and cloud resources which can be dynamically managed
EDS Data Center
Combined Computing Power
Client Interface
Our Technology Stack
©2018 Economic Data Sciences
Scala
Zookeeper
Principals
©2018 Economic Data Sciences
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Asynchronous
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Actions occuring at the same time, in any order, without waiting on each other
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Current solutions are sequence oriented
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Distributed
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Connecting many smaller systems to work together
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Current solutions grow by sequencing faster
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Modular
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This principal means components can be easily repurposed
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As opposed to current monolithic designs
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Many underlying principals drive the design of the EDS infrastructure. In this section we will define and compare them with current solutions.
Principals (cont)
©2018 Economic Data Sciences
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Scalable and Redundant
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Built expecting failure and can easily 'drop-in' new resources live
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Current solutions would need to be shut down and migrated
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Analysis Focused not Storage Focused
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We store data multiple times, in multiple forms, focused on insight
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Current solutions minimize storage cost, but make insight costly
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Flexible Data Consumption
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Takes all data comers, SQL, NoSQL -- structure, unstructure
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Current solutions have strict data structure requirements
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These principals represent a revolution in solution design.
The EDS Infrastructure - Components
©2018 Economic Data Sciences
Each component of the technology stack leads towards our infrastructure principals and a more effective solution
| Cassandra | Database | Meets All | Puts Data of Any Kind Closer To Analysis For Speed and Flexibility |
| Hadoop | Distributed Disk | Meets All | Quickly Consumes Any Type of Data |
| Mesos | Scheduler | Meets All | Coordinates Tools for 'Drop-in' Flexibility |
| Zookeeper | Failover Coordinator | Meets All | Coordinates Redistribution of 'Duties' During Failures Or When Connections Are Dropped |
| Spark, Scala, and R | Analytics | Combined -- Meets All | When Combined, Tools Provide Analytics In Ways That Align With Principals |
| Play | Front-End | Meets All | Visualizes to client and collects client information |
| Component | Purpose | Principal | What It Does |
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The EDS Infrastructure - Security
©2018 Economic Data Sciences
- Point to point encryption over the network
- Data is never exposed without encryption
- Client to server encryption
- In line with best practices at banks. Each client session is encrypted regardless of where the client is connecting from
- Cloud compute not cloud storage
- We use the cloud for compute power not storage, we control the physical location of each data point
- Multi-tiered security layers
- Designed with multiple layers of security so each must be compromised separately
Always at the top of our list
EDS Infrastructure - Summary
©2018 Economic Data Sciences
- Scale and speed
- Add nearly an unlimited number of 'factors' - qualitative/quantitative
- Can run in seconds/minutes and so could be done 'live' for clients
- Each analysis is applied for all
- Data communication - goodbye to silos
- Each part of the business process can share information
- More in depth client feedback
- Better understanding of client interest
These features make possible real advances 'on the ground'
Get In Touch
©2018 Economic Data Sciences
info@EconomicDataSciences.com
info@EconomicDataSciences.com
