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A Journey to build an Modern AI Platform.pptx
- 2. © 2023 Thoughtworks
Table of contents
Modern AI Landscape 3
Foundational Elements 9
● Activated Data 13
● Feature & Feedback - Org Culture 15
● Democratize Experimentation 23
● Applied Observability 26
Q & A
2
- 5. © 2023 Thoughtworks
The Myths
5
Just deploy different
tools and integrate it
Plethora of tools in
the org to maintain.
Product silos built on
different tech stack.
Product Integrations
is a struggle
It’s only needed
when there are many
models
End up stymied and
disorganized
Production release is
a marathon struggle
Models gets stale
over a period of time,
due to missing
feedbacks and drifts
Production lifecycle
looks the same as a
regular software
Doesn’t support ever
evolving and open-
loop feedback
No fallback plan to
support drifts and
declining performance
Optimizing model
performance and
service SLA’s become
a struggle
Businesses need not
get involved. It’s
science.
Models remain
experimental
Models remain as
“Black box”, for
inference
AI Governance
become a struggle
- 6. © 2023 Thoughtworks 6
AI Maturity Model - Value Quotient
● Look for critical business points where
human interaction or human expertise
adds value
● Find a starting point to show incremental
values [for ex: in terms of OKRs]
● Technology modernization roadmap
needs to designed to have more
transformative and strategic impacts.
● Businesses to be empowered by AI
enabled inference and analytics
Value Quotient
- 7. © 2023 Thoughtworks
For the enterprise
Reduce model stall, by increasing business adoption
Moving model ownership close to the businesses
Improve auditability standards by enabling high transparency
Reduce model debts around experimental and stale models
Enable scalable AI by means of model governance and explanability
Reduce cycle time for model refresh, in case of model decay
Bring data, science and businesses to work on the same table
Modern AI
Platform
- 12. © 2023 Thoughtworks
Activated Data
Data Governance and
Reliable re-processing
in-built
[Data Contracts,
Schema Registry, DLQ,
Retry Vs Reprocessing,
Alerts]
Data Quality Scans
[Raw Data Lake,
Continuous Feedback,
Drift Pattern]
Data as Product
[Product Warehouse,
Data validations,
Incremental
Materialisation, Self
Serve Catalog]
Orchestration with
Observability
[Consumer workflows,
Metadata with Patterns,
Proactive views for
operations]
Real time data
ingestion as the default
architecture
[ Kappa, CDC, Object
Storage, Data Formats,
Rollup]
Activated Data
- 13. © 2023 Thoughtworks
Features & Feedback - An Org Culture
Marketplace for Features
[Connected Models, Live Trends,
Feature Lineage for model
explanability, Experiments with
Feedback Loop]
Service Layer
[Grouped Features, Data Integrity,
Batch Training, Live Inferencing,
Incremental Materialization,
Reprovision feature service based
on feedback]
Feature Spaces for Product Teams
[ Product owned stores, Embedded Data
Governance, Self-serve freshness]
Features & Feedback
- 14. © 2023 Thoughtworks
Democratize Experimentation
Onboarding
[Component Templates,
Ethics sensitive, Idea
Onboarding Templates, Live
Experiment Templates]
Continuous Model
Governance
[Model Lineage Registry,
Benchmark Pipelines,
Leaderboards, Time Series
Dashboards
Serving Workbench
[‘N’ Model Servers,
Deployment templates,
Load testing templates,
Ensemble Learning
templates]
Playground with
Integrations
[ Integrated with Feature
Store, Orchestrator,
Experiment Analyzer,
Transformers]
Democratize Experimentation
- 15. © 2023 Thoughtworks
Applied Observability
Shared Responsibility
[Model Performance Drift Detection, Persona
related metrics, Entire workflow traceability,
Concurrent data layers, Active learning]
Sensors Embedded
[ Model serving template with Sensors,
telemetry backed metrics, Traceability to
features, Near Real Time decisions ]
Applied Observability
- 17. © 2023 Thoughtworks 17
Takeaways
● “No One Size Fits All”, it needs to be custom stitched and glued
● Streaming data as a default architecture for all consumers
● Data as a product and businesses to govern
● Features and Feedback for decision makers - Its a default culture
● Playground for experimentation - Entire ecosystem integrated
● Model evaluation on real data remains as the acceptance criteria
● Onboarding templates from ideation to observability
● “Production” as a first class citizen and build for it
● Persona based sensor so as to achieve shared responsibility
