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MLOps Journey at Swisscom
AIUseCases,ArchitectureandFutureVision
Joana Soares Machado
Maxime Darçot
24.02.2023
Outline
2
• Overview: AI Use Cases at Swisscom
• Challenges
• AI Use Cases Deep Dive
• MLOps Principles
• Future Outlook: The AWS Move
MLOps
journey
at
Swisscom
Overview
AI Use Cases at Swisscom
3
Infrastructure Analytics
Anomaly Detection, Network Optimization,
Customer Centric Monitoring, Mobility Insights
Conversational AI
Conversational AI for Swisscom’s Products &
Services, Innovation & Research
Data Services
Data Lake & Warehouse, Enablers, Data and
Analytics Tools in Self-service
B2B Analytics
Business Insights about B2B customers and
Process Performance, KPIs definition
Mass Market Analytics
SC Touchpoints Insights, Pricing Simulations for
Offers, Recommender and Offer System to Sales
Data, Analytics
& AI Business Analytics
Analytics solutions for Sales, Logistics, Finance,
Controlling and HCM, Management Reporting
4
MLOps
journey
at
Swisscom
5
350
experts in
Data, Analytics & AI
Skill Overview
• Data Science / AI Solution Design
• Software Development
• Data Engineering
• Natural Language Processing
• Business Engineering & Design
• Reporting & Analysis Specialists
• User Experience Design
Data & Infrastructure Overview
• >660 servers' on-premise infrastructure
• >40 Data, Analytics and AI services &
platforms & > 150 tools
• 10.73 Peta Bytes (PB) data
• 8.17 Mio. real-time messages per second
• 4’756 monthly active users of dashboard
tools
MLOps
journey
at
Swisscom
Challenges
6
7
MLOps
journey
at
Swisscom
Data Regulations
CH-only, on-prem
Data Formats
Tabular, time series
Challenges: Diversity of...
Scalability Req.
Nb of models, SLAs
• Millions of models @INI
(due to anomaly detection)
• 100s of models (@B2C & @B2B)
• Customer facing applications
Tech Stacks
Clouds, legacy
• k8s on AWS & SBD
• Internal Application
Cloud (CF)
AI Applications
NLP, ASR, Real-time
• 40 product instances @DNA
• 350 Users @DNA
• ML Engineers
• Business Analysts
• 3000 Users @Swisscom
Deep Dive
AI Use Cases
8
9
Digital Marketplace
35M API calls/day
Software Applications
300M Function calls/day
Business Processes
80K Processes/day
Network Infrastructure
20B Network interactions/day
MLOps
journey
at
Swisscom
Anomaly Detection Use Cases at Swisscom
Source: Towards Data Science
Time series Anomaly Detection
10
Data Sourcing
(Time series)
Real-time
Anomaly
Detection
UI + Alerting
Outage
Detection Insights
Feedback
MLOps
journey
at
Swisscom
Anomaly Detection Pipeline
/
Daily
Data Preparation Training + Batch
Prediction
11
MLOps
journey
at
Swisscom
Anomaly Detection Pipeline: Open Source Tools
CI/CD Version Control System Workflow Orchestration
ML Resource
Infrastructure
ML Metadata Store ML Model Serving Pipeline Monitoring
• Measure lags and pipeline availability.
• Different aspects of quality:
− Training data validation;
− Deploying models with good accuracy;
− Logging information about models and
errors, for transparency and reproducibility.
• SLI/SLO alerting: target over time period.
12
MLOps
journey
at
Swisscom
MLOps: Managing the ML Lifecycle
Source: Sensu
13
MLOps
journey
at
Swisscom
Experiment Tracking with Mlflow
14
MLOps
journey
at
Swisscom
Monitoring over time – Model Accuracy
15
MLOps
journey
at
Swisscom
Monitoring over time – Requests and Processing Time
16
MLOps
journey
at
Swisscom
Conversational AI – Dialog flow
16
Language: EN
Intent: Customer has a technical issue
Topics: Internet
Hello
I see that your internet connection
has been blocked because you
have 3 outstanding bills
Do you wish to get more time to
pay these bills?
Hello and welcome to Swisscom,
how may we help you today?
My internet stopped working
Yes please
Would you be able to pay by the
end of next month?
Sure, even by the 15th
Alright I’ve unblocked your
connection, you have until April
15th to pay these 3 bills.
Thank you
Intent: Customer confirms
Topics: None
Intent: Customer confirms
Topics: None
Intent: Customer thanks us
Topics: None
Language
Detection
Intent & Topic
classifiers
General Dialog
Manager
Billing APIs
Intent: Customer greets us
Topics: None
Background call to billing API -> Customer is blocked
Entity
Recognition
Entity: 15th (Date)
Domain
classifier
Domain: None
Domain: Technical support
Domain: Billing
Technical
Support Dialog
Manager
Billing Dialog
Manager
General Dialog
Manager
Background call to billing API -> Change due dates
17
Speech
Recognition
Language
Detection
Entity
Recognition
Machine
Translation
Intent & Topic
classifiers
Retrieval
System
Domain
classifier
…
Monitoring Reporting
Model Storage
CI / CD Error alerting
Secure data
storage
Flow design
tool
Data labeling
tools
End to end
testing tool
Training
pipelines
Billing APIs
CRM
…
Hotline
Whatsapp
TV Box
E-mail
Letter / Fax
...
Input APIs
Dialog
Managers
Conversational AI stack
18
Input
Channels
AI/ML layer
Runtime foundations layer
Offline tools
layer
Input APIs Integration
layer
Dialog
Managers
Conversational AI stack
19
Speech
Recognition
Language
Detection
Entity
Recognition
Machine
Translation
Intent & Topic
classifiers
Retrieval
System
Domain
classifier
…
Monitoring Reporting
Model Storage
CI / CD Error alerting
Secure data
storage
Flow design
tool
Data labeling
tools
End to end
testing tool
Training
pipelines
Billing APIs
CRM
…
Hotline
Whatsapp
TV Box
E-mail
Letter / Fax
...
Input APIs
Dialog
Managers
Conversational AI – ML Operations
20
Intent & Topic
classifiers
Domain
classifier
Model Storage
CI / CD
Secure data
storage
Data labeling
tools
Training
pipelines
Conversational AI – ML Operations
Intent & Topic
classifiers
Domain
classifier
Model Storage
CI / CD
Secure data
storage
Data labeling
tools
Training
pipelines
21
Intent & Topic
classifiers
Domain
classifier
Model Storage
CI / CD
Secure data
storage
Data labelling
tools
Training
pipelines
Conversational AI – ML Operations
22
MLOps
journey
at
Swisscom
Conversational AI: MLOps Tools
CI/CD Version Control System Workflow Orchestration
ML Resource
Infrastructure
Secure Data Storage AI Labelling Tool ML Model Serving
MLOps Principles
23
24
(ML) Product Lifecycle
SW
Product
1.
Development
2.
Integration
3.
Testing
4.
Deployment
5.
Feedback
6.
Monitoring
7.
Operations
ML Product
2.
Data
Preparation
1.
Data
Collection
3.
Data
Integration
4.
Data
Transformation
5.
Model Training
6.
Model
Registration
7.
Model
Deployment
8.
Model
Evaluation
9.
Model
Monitoring
A. Data
Engineering
B. Model
Engineering
C. Model
Operations
MLOps
journey
at
Swisscom
25
Tools for the MLOps Principles over the Data Product Lifecycle
Principle
Collaboration [P5]
Reproducibility [P3]
CI/CD automation [P1]
Workflow orchestration [P2]
Continuous ML training/evaluation [P6]
Feedback loops [P9]
ML metadata tracking/logging [P7]
Continuous monitoring [P8]
Versioning of data, code, model [P4]
Adapted from: Machine Learning Operations (MLOps): Overview, Definition, and Architecture
C. Model
Operations
A. Data
Engineering
B. Model
Engineering
Version
Control
System
P4, P5
CI/CD
Component
P1, P6, P9
ML Resource
Infrastructure
P6
Feature Store
System
P3, P4
Workflow
Orchestration
Component
P2, P3, P6
ML Model
Registry
P3, P4
Monitoring
Component
P8, P9
ML Metadata
Store
P4, P7
ML Model
Serving
Component
P1
MLOps
journey
at
Swisscom
MLOps
journey
at
Swisscom
26
MLOps Harmonization – A Central Solution at Swisscom?
Standardized solution
Easy to start new project
No need to reinvent the wheel every time
Collaborate
and exchange best practices
• How much of the requirements does it cover?
• How good is the user experience?
• What is outlook on future requirements?
• What is outlook on future UX ?
27
MLOps Solutions - Evaluation Dimensions
• Development & Integration Costs.
• Operations & Maintenance Costs.
• Infrastructure (incl. Licenses) Costs.
• Outlook on cost evolution.
User Needs & UX Costs & Efficiency
MLOps
journey
at
Swisscom
Personas / Roles
• Yes-Code vs No-Code:
− Data Engineer,
Data Scientist,
(ML)Ops Engineer,
SW Engineer.
− Business Analyst,
Product Manager.
Future Outlook
The AWS Move
28
29
We want to converge our warehouses & Big Data lake into a hybrid lake house
architecture
From
on prem only
Data Lake & Warehouse
Warehouses
Swisscom Data
Warehouse
ONDS
B2B DWH
Swisscom
Big Data Platform
Data Lake
Hybrid Data Factory
+
To
a hybrid
Data Lakehouse
One
Data Platform
MLOps
journey
at
Swisscom
30
One Data Platform – High-level Architecture
Store &
Process
Applications
& Data
Functions
Exposure
Gatekeeper
Gatekeeper
MLOps DWH
Analytic
s
e.g. Time
Series
Custom
Apps
e.g.
Advanced
Processing
Ingestion
Storage
Transformations
Ingestion
Storage
Transformations
Data Hub
On Premise (for very specific workloads) AWS (whenever possible)
Gatekeeper
Gatekeeper
Data Access
DWH
Analytics
e.g. Time
Series
MLOps
Custom
Apps
e.g.
Advanced
Processing
real-time
/
batch
real-time
/
batch
CI
/CD
Data
Management
Foundation
3 2 1
4
6
5
7 8
9
4
MLOps
journey
at
Swisscom
• AWS Sagemaker
• All-in-one, yet modular
• Including no-code option
• Amazon MWAA
• Managed Airflow (orchestration)
• DataRobot
• All-in-one
• Including no-code option
31
MLOps - Commercial Product Candidates
• All-in-one
• Databricks, Snowflake
• Single components
• Fiddler AI (model performance management)
• Arize (observability)
• CometML (model management & monitoring)
• Astronomer (orchestration)
Concrete Options Other Possibilities
MLOps
journey
at
Swisscom
32
MLOps – Open Source Tools
MLOps
journey
at
Swisscom
Pros
• Free solutions, community built.
• Continuously improved and well tested.
• Independent solutions for each component:
• Fully customizable and modular.
• Easy switch when better options available.
• Infrastructure agnostic (Hybrid cloud).
Cons
• Needs to be managed by Swisscom engineers.
• May be more costly than “managed”solutions in
terms of infrastructure.
• Most solutions don’t offer a “no-code”option.
33
MLOps – Hybrid Solution (AWS & Open source)
MLOps
journey
at
Swisscom
Bringing together the best of both worlds?
• Huge diversity of AI use cases.
• Need for harmonization in the MLOps area.
• The AWS move brings benefits but also questions.
• A hybrid setup will likely be a main part of the solution.
• This is work in progress, our MLOps journey is not at an
end yet.
Summary
34
MLOps
journey
at
Swisscom
35
Thank you!
Maxime Darçot
maxime-darcot
Joana Soares Machado
joana-soares-machado

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MLOps journey at Swisscom: AI Use Cases, Architecture and Future Vision

  • 1. MLOps Journey at Swisscom AIUseCases,ArchitectureandFutureVision Joana Soares Machado Maxime Darçot 24.02.2023
  • 2. Outline 2 • Overview: AI Use Cases at Swisscom • Challenges • AI Use Cases Deep Dive • MLOps Principles • Future Outlook: The AWS Move MLOps journey at Swisscom
  • 3. Overview AI Use Cases at Swisscom 3
  • 4. Infrastructure Analytics Anomaly Detection, Network Optimization, Customer Centric Monitoring, Mobility Insights Conversational AI Conversational AI for Swisscom’s Products & Services, Innovation & Research Data Services Data Lake & Warehouse, Enablers, Data and Analytics Tools in Self-service B2B Analytics Business Insights about B2B customers and Process Performance, KPIs definition Mass Market Analytics SC Touchpoints Insights, Pricing Simulations for Offers, Recommender and Offer System to Sales Data, Analytics & AI Business Analytics Analytics solutions for Sales, Logistics, Finance, Controlling and HCM, Management Reporting 4 MLOps journey at Swisscom
  • 5. 5 350 experts in Data, Analytics & AI Skill Overview • Data Science / AI Solution Design • Software Development • Data Engineering • Natural Language Processing • Business Engineering & Design • Reporting & Analysis Specialists • User Experience Design Data & Infrastructure Overview • >660 servers' on-premise infrastructure • >40 Data, Analytics and AI services & platforms & > 150 tools • 10.73 Peta Bytes (PB) data • 8.17 Mio. real-time messages per second • 4’756 monthly active users of dashboard tools MLOps journey at Swisscom
  • 7. 7 MLOps journey at Swisscom Data Regulations CH-only, on-prem Data Formats Tabular, time series Challenges: Diversity of... Scalability Req. Nb of models, SLAs • Millions of models @INI (due to anomaly detection) • 100s of models (@B2C & @B2B) • Customer facing applications Tech Stacks Clouds, legacy • k8s on AWS & SBD • Internal Application Cloud (CF) AI Applications NLP, ASR, Real-time • 40 product instances @DNA • 350 Users @DNA • ML Engineers • Business Analysts • 3000 Users @Swisscom
  • 9. 9 Digital Marketplace 35M API calls/day Software Applications 300M Function calls/day Business Processes 80K Processes/day Network Infrastructure 20B Network interactions/day MLOps journey at Swisscom Anomaly Detection Use Cases at Swisscom Source: Towards Data Science Time series Anomaly Detection
  • 10. 10 Data Sourcing (Time series) Real-time Anomaly Detection UI + Alerting Outage Detection Insights Feedback MLOps journey at Swisscom Anomaly Detection Pipeline / Daily Data Preparation Training + Batch Prediction
  • 11. 11 MLOps journey at Swisscom Anomaly Detection Pipeline: Open Source Tools CI/CD Version Control System Workflow Orchestration ML Resource Infrastructure ML Metadata Store ML Model Serving Pipeline Monitoring
  • 12. • Measure lags and pipeline availability. • Different aspects of quality: − Training data validation; − Deploying models with good accuracy; − Logging information about models and errors, for transparency and reproducibility. • SLI/SLO alerting: target over time period. 12 MLOps journey at Swisscom MLOps: Managing the ML Lifecycle Source: Sensu
  • 15. 15 MLOps journey at Swisscom Monitoring over time – Requests and Processing Time
  • 16. 16 MLOps journey at Swisscom Conversational AI – Dialog flow 16 Language: EN Intent: Customer has a technical issue Topics: Internet Hello I see that your internet connection has been blocked because you have 3 outstanding bills Do you wish to get more time to pay these bills? Hello and welcome to Swisscom, how may we help you today? My internet stopped working Yes please Would you be able to pay by the end of next month? Sure, even by the 15th Alright I’ve unblocked your connection, you have until April 15th to pay these 3 bills. Thank you Intent: Customer confirms Topics: None Intent: Customer confirms Topics: None Intent: Customer thanks us Topics: None Language Detection Intent & Topic classifiers General Dialog Manager Billing APIs Intent: Customer greets us Topics: None Background call to billing API -> Customer is blocked Entity Recognition Entity: 15th (Date) Domain classifier Domain: None Domain: Technical support Domain: Billing Technical Support Dialog Manager Billing Dialog Manager General Dialog Manager Background call to billing API -> Change due dates
  • 17. 17 Speech Recognition Language Detection Entity Recognition Machine Translation Intent & Topic classifiers Retrieval System Domain classifier … Monitoring Reporting Model Storage CI / CD Error alerting Secure data storage Flow design tool Data labeling tools End to end testing tool Training pipelines Billing APIs CRM … Hotline Whatsapp TV Box E-mail Letter / Fax ... Input APIs Dialog Managers Conversational AI stack
  • 18. 18 Input Channels AI/ML layer Runtime foundations layer Offline tools layer Input APIs Integration layer Dialog Managers Conversational AI stack
  • 19. 19 Speech Recognition Language Detection Entity Recognition Machine Translation Intent & Topic classifiers Retrieval System Domain classifier … Monitoring Reporting Model Storage CI / CD Error alerting Secure data storage Flow design tool Data labeling tools End to end testing tool Training pipelines Billing APIs CRM … Hotline Whatsapp TV Box E-mail Letter / Fax ... Input APIs Dialog Managers Conversational AI – ML Operations
  • 20. 20 Intent & Topic classifiers Domain classifier Model Storage CI / CD Secure data storage Data labeling tools Training pipelines Conversational AI – ML Operations Intent & Topic classifiers Domain classifier Model Storage CI / CD Secure data storage Data labeling tools Training pipelines
  • 21. 21 Intent & Topic classifiers Domain classifier Model Storage CI / CD Secure data storage Data labelling tools Training pipelines Conversational AI – ML Operations
  • 22. 22 MLOps journey at Swisscom Conversational AI: MLOps Tools CI/CD Version Control System Workflow Orchestration ML Resource Infrastructure Secure Data Storage AI Labelling Tool ML Model Serving
  • 24. 24 (ML) Product Lifecycle SW Product 1. Development 2. Integration 3. Testing 4. Deployment 5. Feedback 6. Monitoring 7. Operations ML Product 2. Data Preparation 1. Data Collection 3. Data Integration 4. Data Transformation 5. Model Training 6. Model Registration 7. Model Deployment 8. Model Evaluation 9. Model Monitoring A. Data Engineering B. Model Engineering C. Model Operations MLOps journey at Swisscom
  • 25. 25 Tools for the MLOps Principles over the Data Product Lifecycle Principle Collaboration [P5] Reproducibility [P3] CI/CD automation [P1] Workflow orchestration [P2] Continuous ML training/evaluation [P6] Feedback loops [P9] ML metadata tracking/logging [P7] Continuous monitoring [P8] Versioning of data, code, model [P4] Adapted from: Machine Learning Operations (MLOps): Overview, Definition, and Architecture C. Model Operations A. Data Engineering B. Model Engineering Version Control System P4, P5 CI/CD Component P1, P6, P9 ML Resource Infrastructure P6 Feature Store System P3, P4 Workflow Orchestration Component P2, P3, P6 ML Model Registry P3, P4 Monitoring Component P8, P9 ML Metadata Store P4, P7 ML Model Serving Component P1 MLOps journey at Swisscom
  • 26. MLOps journey at Swisscom 26 MLOps Harmonization – A Central Solution at Swisscom? Standardized solution Easy to start new project No need to reinvent the wheel every time Collaborate and exchange best practices
  • 27. • How much of the requirements does it cover? • How good is the user experience? • What is outlook on future requirements? • What is outlook on future UX ? 27 MLOps Solutions - Evaluation Dimensions • Development & Integration Costs. • Operations & Maintenance Costs. • Infrastructure (incl. Licenses) Costs. • Outlook on cost evolution. User Needs & UX Costs & Efficiency MLOps journey at Swisscom Personas / Roles • Yes-Code vs No-Code: − Data Engineer, Data Scientist, (ML)Ops Engineer, SW Engineer. − Business Analyst, Product Manager.
  • 29. 29 We want to converge our warehouses & Big Data lake into a hybrid lake house architecture From on prem only Data Lake & Warehouse Warehouses Swisscom Data Warehouse ONDS B2B DWH Swisscom Big Data Platform Data Lake Hybrid Data Factory + To a hybrid Data Lakehouse One Data Platform MLOps journey at Swisscom
  • 30. 30 One Data Platform – High-level Architecture Store & Process Applications & Data Functions Exposure Gatekeeper Gatekeeper MLOps DWH Analytic s e.g. Time Series Custom Apps e.g. Advanced Processing Ingestion Storage Transformations Ingestion Storage Transformations Data Hub On Premise (for very specific workloads) AWS (whenever possible) Gatekeeper Gatekeeper Data Access DWH Analytics e.g. Time Series MLOps Custom Apps e.g. Advanced Processing real-time / batch real-time / batch CI /CD Data Management Foundation 3 2 1 4 6 5 7 8 9 4 MLOps journey at Swisscom
  • 31. • AWS Sagemaker • All-in-one, yet modular • Including no-code option • Amazon MWAA • Managed Airflow (orchestration) • DataRobot • All-in-one • Including no-code option 31 MLOps - Commercial Product Candidates • All-in-one • Databricks, Snowflake • Single components • Fiddler AI (model performance management) • Arize (observability) • CometML (model management & monitoring) • Astronomer (orchestration) Concrete Options Other Possibilities MLOps journey at Swisscom
  • 32. 32 MLOps – Open Source Tools MLOps journey at Swisscom Pros • Free solutions, community built. • Continuously improved and well tested. • Independent solutions for each component: • Fully customizable and modular. • Easy switch when better options available. • Infrastructure agnostic (Hybrid cloud). Cons • Needs to be managed by Swisscom engineers. • May be more costly than “managed”solutions in terms of infrastructure. • Most solutions don’t offer a “no-code”option.
  • 33. 33 MLOps – Hybrid Solution (AWS & Open source) MLOps journey at Swisscom Bringing together the best of both worlds?
  • 34. • Huge diversity of AI use cases. • Need for harmonization in the MLOps area. • The AWS move brings benefits but also questions. • A hybrid setup will likely be a main part of the solution. • This is work in progress, our MLOps journey is not at an end yet. Summary 34 MLOps journey at Swisscom
  • 35. 35 Thank you! Maxime Darçot maxime-darcot Joana Soares Machado joana-soares-machado