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Webinar:
Why a Multi-Cloud Strategy
Matters for Your AI Platform
Tarik Bennett
tarik.bennett@alluxio.com
February 27th, 2023
Senior Solutions Engineer
@ Alluxio
Tarik Bennett
2
Balancing performance, scalability, and cost
Agnostic data layer
Best practices for hybrid and multi-cloud
Agenda
Managing Costs
Cloud Agility or Resource Availability
Training Efficiency
Primary Scenarios Addressed
Source: Gartner 2023
1. By 2028, the adoption of AI will culminate in over
50% of cloud compute resources… up from less
than 10% in 2023.
2. Global spending on public cloud services is forecast to increase 20.4% in 2024… the
source of growth will be combination of cloud vendor price increases and increased
utilization.
3. Deep learning models fed by images, internet-scale applications or even telemetry data
have ever growing data requirements.
AI Adoption is Ballooning Cloud Costs
● Efficient distributed computing
● Workload scheduling
● Modernizing or reducing legacy storage
● Minimizing data movement
● Improving data access
● Increasing scalability
Efficiencies via Platform Improvements
Source: Gartner 2023
According to the survey, almost half (47%) of C-suite
executives don’t feel prepared for the accelerating rate
of technological change.
Further, only 27% claim their organizations are ready to scale up generative AI, and 44% say it
will take more than six months to do so and take advantage of the potential benefits.
Scalability and Cloud Agility
Technical
● Improves scalability
● Enables hybrid cloud
● Expanded access to GPUs
● Best-of-breed AI tools available
Non-Technical
● Leverage in cloud negotiations
● Security and governance, privacy, etc
● Service resilience
● Flexible access to the most
cost-effective resources
Why Multi-Cloud?
Agility Comes with Some Overhead
● Data replication between DCs or regions
Multi-Cloud Challenges
Source: Alluxio
Agility Comes with Some Overhead
● Data replication between DCs or regions
● Disruptive, costly or prolonged migrations to upgrade
HDFS
Object
Store
Multi-Cloud Challenges
Agility Comes with Some Overhead
● Data replication between DCs or regions
● Disruptive, costly or prolonged migrations to upgrade
● Overlapping resources in cloud + on-prem
compute compute compute
Multi-Cloud Challenges
Agility Comes with Some Overhead
● Data replication between DCs or regions
● Disruptive, costly or prolonged migrations to upgrade
● Overlapping resources in cloud + on-prem
● Need to address non-technical requirements within CSPs
Multi-Cloud Challenges
Given Multi-Cloud Benefits for AI, You Can Optimize
● Simplify wherever possible
● Reduce replication wherever possible
● Finding cost efficiencies via caching or other means
● Increase data locality
● Unify data access
● Increase throughput of commodity storage
● Reduce bandwidth congestion
Best Practices
● Multi-Cloud architecture
○ Google Cloud Platform (GCP)
○ Oracle® Cloud Infrastructure (OCI)
● Data orchestration and caching
Uber Multi-Cloud Architecture (Future)
Source: Uber Jing Zhao 2024
Alluxio Intro
16
Alluxio Data Platform
High Performance data access, unified global view
18
Portability via Alluxio Kubernetes Operator
Reduced Data Replication
Source: Alluxio
Some data cannot be persisted in the cloud. Security teams will often
approve ephemeral cache, while other options will be denied.
High Performance Data Access
Sensitive model
training data
Data evicted
from the cache
Benefits of Caching for Sensitive Data
Standalone Cluster
High Performance Data Access Layer
Data from multiple sources served to GPU nodes
Virtual Caching Across Local
GPU Storage
Data source synced to Virtual Alluxio Storage and
shared between GPU nodes
Alluxio Deployment Options for AI
Case Study
BUSINESS BENEFIT:
TECH BENEFIT:
Increase GPU
utilization
50%
93%
File System
Training
Data
Training
Data
M
o
d
e
l
s
Training
Data
Models
Model
Training
Model
Training
Model
Deployment
Model
Inference
Downstream
Applications
Model
Update
Training Clouds Offline Cloud Online Cloud
APAC Quora CASE STUDY:
High Performance AI Platform for LLM
2 - 4X faster
time-to-market
Before Alluxio: (1) Low GPU Utilization, (2) Overloaded Storage, (3) Network Congestion & Slow Model Refresh
Thank You!

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Alluxio Monthly Webinar | Why a Multi-Cloud Strategy Matters for Your AI Platform

  • 1. Webinar: Why a Multi-Cloud Strategy Matters for Your AI Platform Tarik Bennett tarik.bennett@alluxio.com February 27th, 2023
  • 2. Senior Solutions Engineer @ Alluxio Tarik Bennett 2
  • 3. Balancing performance, scalability, and cost Agnostic data layer Best practices for hybrid and multi-cloud Agenda
  • 4. Managing Costs Cloud Agility or Resource Availability Training Efficiency Primary Scenarios Addressed
  • 5. Source: Gartner 2023 1. By 2028, the adoption of AI will culminate in over 50% of cloud compute resources… up from less than 10% in 2023. 2. Global spending on public cloud services is forecast to increase 20.4% in 2024… the source of growth will be combination of cloud vendor price increases and increased utilization. 3. Deep learning models fed by images, internet-scale applications or even telemetry data have ever growing data requirements. AI Adoption is Ballooning Cloud Costs
  • 6. ● Efficient distributed computing ● Workload scheduling ● Modernizing or reducing legacy storage ● Minimizing data movement ● Improving data access ● Increasing scalability Efficiencies via Platform Improvements
  • 7. Source: Gartner 2023 According to the survey, almost half (47%) of C-suite executives don’t feel prepared for the accelerating rate of technological change. Further, only 27% claim their organizations are ready to scale up generative AI, and 44% say it will take more than six months to do so and take advantage of the potential benefits. Scalability and Cloud Agility
  • 8. Technical ● Improves scalability ● Enables hybrid cloud ● Expanded access to GPUs ● Best-of-breed AI tools available Non-Technical ● Leverage in cloud negotiations ● Security and governance, privacy, etc ● Service resilience ● Flexible access to the most cost-effective resources Why Multi-Cloud?
  • 9. Agility Comes with Some Overhead ● Data replication between DCs or regions Multi-Cloud Challenges Source: Alluxio
  • 10. Agility Comes with Some Overhead ● Data replication between DCs or regions ● Disruptive, costly or prolonged migrations to upgrade HDFS Object Store Multi-Cloud Challenges
  • 11. Agility Comes with Some Overhead ● Data replication between DCs or regions ● Disruptive, costly or prolonged migrations to upgrade ● Overlapping resources in cloud + on-prem compute compute compute Multi-Cloud Challenges
  • 12. Agility Comes with Some Overhead ● Data replication between DCs or regions ● Disruptive, costly or prolonged migrations to upgrade ● Overlapping resources in cloud + on-prem ● Need to address non-technical requirements within CSPs Multi-Cloud Challenges
  • 13. Given Multi-Cloud Benefits for AI, You Can Optimize ● Simplify wherever possible ● Reduce replication wherever possible ● Finding cost efficiencies via caching or other means ● Increase data locality ● Unify data access ● Increase throughput of commodity storage ● Reduce bandwidth congestion Best Practices
  • 14. ● Multi-Cloud architecture ○ Google Cloud Platform (GCP) ○ Oracle® Cloud Infrastructure (OCI) ● Data orchestration and caching Uber Multi-Cloud Architecture (Future) Source: Uber Jing Zhao 2024
  • 16. 16 Alluxio Data Platform High Performance data access, unified global view
  • 17.
  • 18. 18 Portability via Alluxio Kubernetes Operator
  • 20. Some data cannot be persisted in the cloud. Security teams will often approve ephemeral cache, while other options will be denied. High Performance Data Access Sensitive model training data Data evicted from the cache Benefits of Caching for Sensitive Data
  • 21. Standalone Cluster High Performance Data Access Layer Data from multiple sources served to GPU nodes Virtual Caching Across Local GPU Storage Data source synced to Virtual Alluxio Storage and shared between GPU nodes Alluxio Deployment Options for AI
  • 23. BUSINESS BENEFIT: TECH BENEFIT: Increase GPU utilization 50% 93% File System Training Data Training Data M o d e l s Training Data Models Model Training Model Training Model Deployment Model Inference Downstream Applications Model Update Training Clouds Offline Cloud Online Cloud APAC Quora CASE STUDY: High Performance AI Platform for LLM 2 - 4X faster time-to-market Before Alluxio: (1) Low GPU Utilization, (2) Overloaded Storage, (3) Network Congestion & Slow Model Refresh