SlideShare a Scribd company logo
1 of 47
Download to read offline
An Architecture for Agile Machine
Learning in Real-Time Applications
johann@ifwe.co@jssmith github.com/ifwe
Johann Schleier-Smith
if(we) Inc.
August 11, 2015

KDD, Sydney Australia
• Profitable startup actively pursuing big
opportunities in social apps
• Millions of users on existing products
• Thousands of social contacts per second
Overview
• Agile machine learning can be difficult—
but brings big benefits
• Key challenges in deployment and
feature engineering
• Solution in single path to data
production
development
serve
personalized
recommendations
data
collection
model
updates
production
development
serve
personalized
recommendations
data
collection
model
updates
study &
understand
train &
backtest
design new
models & features
production
development
serve
personalized
recommendations
data
collection
study &
understand
design new
models & features
model
updates
train &
backtest
model
updates
train &
backtest
write
spec
e-mail model
to engineers
request
engineering
why did
we want this?
QA
bug fixes
meetingswait
export
to Excel
check
parameters
Java development
new database
schema
model
updates
train &
backtest
• Shared path to data

• Shared feature definition code
production
development
serve
personalized
recommendations
data
collection
model
updates
study &
understand
train &
backtest
design new
models & features
• >10 million candidates
• >1000 updates/sec
• Must be responsive to current activity
• Users expect instant query results
Recommendation Engine

for Dating Product
Model
Model
Model
• Decompose likelihood of match between vote outcomes
and vote occurrence
• Logistic regression
• Real-time personalization through feature vector evolution
• Model parameters trained offline by data scientists
• Consider 1000s of features, select 50-100
Application APIs
& Business Logic
RDBMS
Application APIs
& Business Logic
RDBMS
Data Warehouse /
Hadoop
Application APIs
& Business Logic
RDBMS
Data Warehouse /
Hadoop
Streaming Logs
Application APIs
& Business Logic
RDBMS
Data Warehouse /
Hadoop
Streaming Logs
Application APIs
& Business Logic
RDBMS
production
development
Exploratory
Analysis
Training &
Backtesting
Data Warehouse /
Hadoop
Streaming Logs
Application APIs
& Business Logic
RDBMS
production
development
Exploratory
Analysis
Training &
Backtesting
Batch
Predictions
Data Warehouse /
Hadoop
Streaming Logs
Application APIs
& Business Logic
RDBMS
production
development
Exploratory
Analysis
Training &
Backtesting
Batch
Predictions
Predictive Services /
Ranking
Data Warehouse /
Hadoop
Streaming Logs
Application APIs
& Business Logic
RDBMS
production
development
Exploratory
Analysis
Training &
Backtesting
Batch
Predictions
Predictive Services /
Ranking
Data Warehouse /
Hadoop
Streaming Logs
Events
Time
Aggregation
first( )
last( )
count( )
sum( )
max( )
count( )
avg( ) min( )
Events
Time
Machine learning inputAggregation
first( )
last( )
count( )
sum( )
max( )
count( )
avg( ) min( )
Events
Time
Event History API
trait EventHistory {
def publishEvent(e: Event)
def getEvents(
startTime: Date,
endTime: Date,
eventFilter: EventFilter,
eventHandler: EventHandler
)
}
Event History API
trait EventHistory {
def publishEvent(e: Event)
def getEvents(
startTime: Date,
endTime: Date,
eventFilter: EventFilter,
eventHandler: EventHandler
)
}
Event History API
trait EventHistory {
def publishEvent(e: Event)
def getEvents(
startTime: Date,
endTime: Date,
eventFilter: EventFilter,
eventHandler: EventHandler
)
}
Event History API
trait EventHistory {
def publishEvent(e: Event)
def getEvents(
startTime: Date,
endTime: Date,
eventFilter: EventFilter,
eventHandler: EventHandler
)
}
+∞ for

real-time

streaming
Events
Alice updates profile
Bob opens app
Bob sees Alice in recommendations
Bob swipes yes on Alice
Alice receives push notification
Alice sees Bob in recommendations
Alice sends message to Bob
Time
Online feature stateEvents
Alice updates profile
Bob opens app
Bob sees Alice in recommendations
Bob swipes yes on Alice
Alice receives push notification
Alice sees Bob in recommendations
Alice sends message to Bob
Time
Machine learning inputOnline feature stateEvents
Alice updates profile
Bob opens app
Bob sees Alice in recommendations
Bob swipes yes on Alice
Alice receives push notification
Alice sees Bob in recommendations
Alice sends message to Bob
Time
RDBMS
Application APIs
& Business Logic
Event History Repository
Ranking
Real-Time
State Updates
RDBMS
Application APIs
& Business Logic
Event History Repository
Ranking
Real-Time
State Updates
RDBMS
Application APIs
& Business Logic
Event History Repository
Ranking
Real-Time
State Updates
RDBMS
Application APIs
& Business Logic
Event History Repository
Ranking
Real-Time
State Updates
State Updates
Exploratory
Analysis
Training &
Backtesting
production
development
RDBMS
Application APIs
& Business Logic
Event History Repository
Ranking
Real-Time
State Updates
State Updates
Exploratory
Analysis
Training &
Backtesting
production
development
RDBMS
Application APIs
& Business Logic
Event History Repository
Ranking
Real-Time
State Updates
State Updates
Exploratory
Analysis
Training &
Backtesting
production
development
RDBMS
Application APIs
& Business Logic
Event History Repository
Ranking
Real-Time
State Updates
State Updates
Exploratory
Analysis
Training &
Backtesting
production
development
RDBMS
Application APIs
& Business Logic
Event History Repository
Ranking
Real-Time
State Updates
State Updates
Exploratory
Analysis
Training &
Backtesting
production
development
RDBMS
Application APIs
& Business Logic
Event History Repository
Ranking
Real-Time
State Updates
State Updates
Exploratory
Analysis
Training &
Backtesting
production
development
Monitoring
RDBMS
Application APIs
& Business Logic
Event History Repository
Ranking
Real-Time
State Updates
State Updates
Exploratory
Analysis
Training &
Backtesting
production
development
• Single path to data for real-time streaming and history
• Shared feature engineering code for development and production
• Team shares access to code and data
• Fine-grained alignment of feature state and prediction outcomes
• Temporally accurate modeling ensured (no looking ahead)
Event History API
15 new models released and tested within 6 months
>30% cumulative improvement in usage shown in A/B testing
0
500,000
1,000,000
1,500,000
2,000,000
Apr 2013 Jul 2013 Oct 2013 Jan 2014 Apr 2014
DailyUniqueUsers
Matchers
Voters
New model released
A/Btestupdated
• Open source implementation derived from
if(we)’s proprietary platform
• Provides Scala DSL for building online
features from event history
• Examples include dating recommendations
and product search with learning to rank
• Not yet ready for scale or production
• Seeking collaborators
Production Serving Data Science
Ranking R MatlabPython
Feature Engineering
Event History API
Kafka
Streaming data
Storm
Historical data
S3 NFSHDFS
Antelope Open Source Vision
Agile Machine Learning with Event History
• Solving deployment yields quick product cycles
• All data saved and retrieved as time-ordered events
• Single path to data for both historical and real-time access
• Same feature engineering code used in development and production
• Agile success
• Team shares access to code and data
• Production product iterations measured in days rather than months
github.com/ifwe/antelope@jssmith

More Related Content

What's hot

Design Patterns for Machine Learning in Production - Sergei Izrailev, Chief D...
Design Patterns for Machine Learning in Production - Sergei Izrailev, Chief D...Design Patterns for Machine Learning in Production - Sergei Izrailev, Chief D...
Design Patterns for Machine Learning in Production - Sergei Izrailev, Chief D...
Sri Ambati
 
MLOps and Reproducible ML on AWS with Kubeflow and SageMaker
MLOps and Reproducible ML on AWS with Kubeflow and SageMakerMLOps and Reproducible ML on AWS with Kubeflow and SageMaker
MLOps and Reproducible ML on AWS with Kubeflow and SageMaker
Provectus
 
Zipline—Airbnb’s Declarative Feature Engineering Framework
Zipline—Airbnb’s Declarative Feature Engineering FrameworkZipline—Airbnb’s Declarative Feature Engineering Framework
Zipline—Airbnb’s Declarative Feature Engineering Framework
Databricks
 

What's hot (20)

ML-Ops: From Proof-of-Concept to Production Application
ML-Ops: From Proof-of-Concept to Production ApplicationML-Ops: From Proof-of-Concept to Production Application
ML-Ops: From Proof-of-Concept to Production Application
 
Design Patterns for Machine Learning in Production - Sergei Izrailev, Chief D...
Design Patterns for Machine Learning in Production - Sergei Izrailev, Chief D...Design Patterns for Machine Learning in Production - Sergei Izrailev, Chief D...
Design Patterns for Machine Learning in Production - Sergei Izrailev, Chief D...
 
Hamburg Data Science Meetup - MLOps with a Feature Store
Hamburg Data Science Meetup - MLOps with a Feature StoreHamburg Data Science Meetup - MLOps with a Feature Store
Hamburg Data Science Meetup - MLOps with a Feature Store
 
Using H2O for Mobile Transaction Forecasting & Anomaly Detection - Capital One
Using H2O for Mobile Transaction Forecasting & Anomaly Detection - Capital OneUsing H2O for Mobile Transaction Forecasting & Anomaly Detection - Capital One
Using H2O for Mobile Transaction Forecasting & Anomaly Detection - Capital One
 
Big Data & Analytics - Use Cases in Mobile, E-commerce, Media and more
Big Data & Analytics - Use Cases in Mobile, E-commerce, Media and moreBig Data & Analytics - Use Cases in Mobile, E-commerce, Media and more
Big Data & Analytics - Use Cases in Mobile, E-commerce, Media and more
 
Ml ops past_present_future
Ml ops past_present_futureMl ops past_present_future
Ml ops past_present_future
 
[AI] ML Operationalization with Microsoft Azure
[AI] ML Operationalization with Microsoft Azure[AI] ML Operationalization with Microsoft Azure
[AI] ML Operationalization with Microsoft Azure
 
Practical Machine Learning
Practical Machine LearningPractical Machine Learning
Practical Machine Learning
 
Pm.ais ummit 180917 final
Pm.ais ummit 180917 finalPm.ais ummit 180917 final
Pm.ais ummit 180917 final
 
Building Real Time Targeting Capabilities - Ryan Zotti, Subbu Thiruppathy - C...
Building Real Time Targeting Capabilities - Ryan Zotti, Subbu Thiruppathy - C...Building Real Time Targeting Capabilities - Ryan Zotti, Subbu Thiruppathy - C...
Building Real Time Targeting Capabilities - Ryan Zotti, Subbu Thiruppathy - C...
 
MLOps and Reproducible ML on AWS with Kubeflow and SageMaker
MLOps and Reproducible ML on AWS with Kubeflow and SageMakerMLOps and Reproducible ML on AWS with Kubeflow and SageMaker
MLOps and Reproducible ML on AWS with Kubeflow and SageMaker
 
Feature drift monitoring as a service for machine learning models at scale
Feature drift monitoring as a service for machine learning models at scaleFeature drift monitoring as a service for machine learning models at scale
Feature drift monitoring as a service for machine learning models at scale
 
Ml infra at an early stage
Ml infra at an early stageMl infra at an early stage
Ml infra at an early stage
 
ML-Ops: Philosophy, Best-Practices and Tools
ML-Ops:Philosophy, Best-Practices and ToolsML-Ops:Philosophy, Best-Practices and Tools
ML-Ops: Philosophy, Best-Practices and Tools
 
Richard Coffey (x18140785) - Research in Computing CA2
Richard Coffey (x18140785) - Research in Computing CA2Richard Coffey (x18140785) - Research in Computing CA2
Richard Coffey (x18140785) - Research in Computing CA2
 
Vertex AI: Pipelines for your MLOps workflows
Vertex AI: Pipelines for your MLOps workflowsVertex AI: Pipelines for your MLOps workflows
Vertex AI: Pipelines for your MLOps workflows
 
H2O World - Building a Smarter Application - Tom Kraljevic
H2O World - Building a Smarter Application - Tom KraljevicH2O World - Building a Smarter Application - Tom Kraljevic
H2O World - Building a Smarter Application - Tom Kraljevic
 
Analysing data analytics use cases to understand big data platform
Analysing data analytics use cases  to understand big data platformAnalysing data analytics use cases  to understand big data platform
Analysing data analytics use cases to understand big data platform
 
Zipline—Airbnb’s Declarative Feature Engineering Framework
Zipline—Airbnb’s Declarative Feature Engineering FrameworkZipline—Airbnb’s Declarative Feature Engineering Framework
Zipline—Airbnb’s Declarative Feature Engineering Framework
 
MLOps Using MLflow
MLOps Using MLflowMLOps Using MLflow
MLOps Using MLflow
 

Viewers also liked

Apply Machine Learning to Microservices
Apply Machine Learning to MicroservicesApply Machine Learning to Microservices
Apply Machine Learning to Microservices
Kai Wähner
 
Lessons Learned from Building Machine Learning Software at Netflix
Lessons Learned from Building Machine Learning Software at NetflixLessons Learned from Building Machine Learning Software at Netflix
Lessons Learned from Building Machine Learning Software at Netflix
Justin Basilico
 

Viewers also liked (20)

Machine Learning system architecture – Microsoft Translator, a Case Study : ...
Machine Learning system architecture – Microsoft Translator, a Case Study :  ...Machine Learning system architecture – Microsoft Translator, a Case Study :  ...
Machine Learning system architecture – Microsoft Translator, a Case Study : ...
 
Machine Learning In Production
Machine Learning In ProductionMachine Learning In Production
Machine Learning In Production
 
Amazon Machine Learning
Amazon Machine LearningAmazon Machine Learning
Amazon Machine Learning
 
Meetup7 integration microservices_machine_learning
Meetup7 integration microservices_machine_learningMeetup7 integration microservices_machine_learning
Meetup7 integration microservices_machine_learning
 
Production and Beyond: Deploying and Managing Machine Learning Models
Production and Beyond: Deploying and Managing Machine Learning ModelsProduction and Beyond: Deploying and Managing Machine Learning Models
Production and Beyond: Deploying and Managing Machine Learning Models
 
Apply Machine Learning to Microservices
Apply Machine Learning to MicroservicesApply Machine Learning to Microservices
Apply Machine Learning to Microservices
 
Learning to Rank with Deep Visual Semantic Features - Kamelia, Seniors Data S...
Learning to Rank with Deep Visual Semantic Features - Kamelia, Seniors Data S...Learning to Rank with Deep Visual Semantic Features - Kamelia, Seniors Data S...
Learning to Rank with Deep Visual Semantic Features - Kamelia, Seniors Data S...
 
Managing and Versioning Machine Learning Models in Python
Managing and Versioning Machine Learning Models in PythonManaging and Versioning Machine Learning Models in Python
Managing and Versioning Machine Learning Models in Python
 
Square's Machine Learning Infrastructure and Applications - Rong Yan
Square's Machine Learning Infrastructure and Applications - Rong YanSquare's Machine Learning Infrastructure and Applications - Rong Yan
Square's Machine Learning Infrastructure and Applications - Rong Yan
 
Reducing the dimensionality of data with neural networks
Reducing the dimensionality of data with neural networksReducing the dimensionality of data with neural networks
Reducing the dimensionality of data with neural networks
 
Real-time Recommendations for Retail: Architecture, Algorithms, and Design
Real-time Recommendations for Retail: Architecture, Algorithms, and DesignReal-time Recommendations for Retail: Architecture, Algorithms, and Design
Real-time Recommendations for Retail: Architecture, Algorithms, and Design
 
Closing The Loop for Evaluating Big Data Analysis
Closing The Loop for Evaluating Big Data AnalysisClosing The Loop for Evaluating Big Data Analysis
Closing The Loop for Evaluating Big Data Analysis
 
Machine Learning at Netflix
Machine Learning at NetflixMachine Learning at Netflix
Machine Learning at Netflix
 
Working Effectively With Legacy Code
Working Effectively With Legacy CodeWorking Effectively With Legacy Code
Working Effectively With Legacy Code
 
Introduction to Hive and HCatalog
Introduction to Hive and HCatalogIntroduction to Hive and HCatalog
Introduction to Hive and HCatalog
 
Deep learning at nmc devin jones
Deep learning at nmc devin jones Deep learning at nmc devin jones
Deep learning at nmc devin jones
 
Data Workflows for Machine Learning - Seattle DAML
Data Workflows for Machine Learning - Seattle DAMLData Workflows for Machine Learning - Seattle DAML
Data Workflows for Machine Learning - Seattle DAML
 
Lessons Learned from Building Machine Learning Software at Netflix
Lessons Learned from Building Machine Learning Software at NetflixLessons Learned from Building Machine Learning Software at Netflix
Lessons Learned from Building Machine Learning Software at Netflix
 
Hadoop World 2011: Advanced HBase Schema Design
Hadoop World 2011: Advanced HBase Schema DesignHadoop World 2011: Advanced HBase Schema Design
Hadoop World 2011: Advanced HBase Schema Design
 
IMCSummit 2015 - Day 2 Developer Track - Implementing a Highly Scalable In-Me...
IMCSummit 2015 - Day 2 Developer Track - Implementing a Highly Scalable In-Me...IMCSummit 2015 - Day 2 Developer Track - Implementing a Highly Scalable In-Me...
IMCSummit 2015 - Day 2 Developer Track - Implementing a Highly Scalable In-Me...
 

Similar to An Architecture for Agile Machine Learning in Real-Time Applications

Machine Learning Models in Production
Machine Learning Models in ProductionMachine Learning Models in Production
Machine Learning Models in Production
DataWorks Summit
 
Monitoring AI with AI
Monitoring AI with AIMonitoring AI with AI
Monitoring AI with AI
Stepan Pushkarev
 
Architecting for change: LinkedIn's new data ecosystem
Architecting for change: LinkedIn's new data ecosystemArchitecting for change: LinkedIn's new data ecosystem
Architecting for change: LinkedIn's new data ecosystem
Yael Garten
 
Software engineering practices for the data science and machine learning life...
Software engineering practices for the data science and machine learning life...Software engineering practices for the data science and machine learning life...
Software engineering practices for the data science and machine learning life...
DataWorks Summit
 

Similar to An Architecture for Agile Machine Learning in Real-Time Applications (20)

Machine Learning Models in Production
Machine Learning Models in ProductionMachine Learning Models in Production
Machine Learning Models in Production
 
PredictionIO – A Machine Learning Server in Scala – SF Scala
PredictionIO – A Machine Learning Server in Scala – SF ScalaPredictionIO – A Machine Learning Server in Scala – SF Scala
PredictionIO – A Machine Learning Server in Scala – SF Scala
 
DevOps for Machine Learning overview en-us
DevOps for Machine Learning overview en-usDevOps for Machine Learning overview en-us
DevOps for Machine Learning overview en-us
 
Optimizely Agent: Scaling Resilient Feature Delivery
Optimizely Agent: Scaling Resilient Feature DeliveryOptimizely Agent: Scaling Resilient Feature Delivery
Optimizely Agent: Scaling Resilient Feature Delivery
 
Monitoring AI with AI
Monitoring AI with AIMonitoring AI with AI
Monitoring AI with AI
 
Data Summer Conf 2018, “Monitoring AI with AI (RUS)” — Stepan Pushkarev, CTO ...
Data Summer Conf 2018, “Monitoring AI with AI (RUS)” — Stepan Pushkarev, CTO ...Data Summer Conf 2018, “Monitoring AI with AI (RUS)” — Stepan Pushkarev, CTO ...
Data Summer Conf 2018, “Monitoring AI with AI (RUS)” — Stepan Pushkarev, CTO ...
 
Strata 2016 - Architecting for Change: LinkedIn's new data ecosystem
Strata 2016 - Architecting for Change: LinkedIn's new data ecosystemStrata 2016 - Architecting for Change: LinkedIn's new data ecosystem
Strata 2016 - Architecting for Change: LinkedIn's new data ecosystem
 
Architecting for change: LinkedIn's new data ecosystem
Architecting for change: LinkedIn's new data ecosystemArchitecting for change: LinkedIn's new data ecosystem
Architecting for change: LinkedIn's new data ecosystem
 
Serverless ML Workshop with Hopsworks at PyData Seattle
Serverless ML Workshop with Hopsworks at PyData SeattleServerless ML Workshop with Hopsworks at PyData Seattle
Serverless ML Workshop with Hopsworks at PyData Seattle
 
Models in Minutes using AutoML
Models in Minutes using AutoMLModels in Minutes using AutoML
Models in Minutes using AutoML
 
The Magic Of Application Lifecycle Management In Vs Public
The Magic Of Application Lifecycle Management In Vs PublicThe Magic Of Application Lifecycle Management In Vs Public
The Magic Of Application Lifecycle Management In Vs Public
 
201906 04 Overview of Automated ML June 2019
201906 04 Overview of Automated ML June 2019201906 04 Overview of Automated ML June 2019
201906 04 Overview of Automated ML June 2019
 
Supercharging Self-Service API Integration with AI
Supercharging Self-Service API Integration with AI Supercharging Self-Service API Integration with AI
Supercharging Self-Service API Integration with AI
 
Real-time Machine Learning Analytics Using Structured Streaming and Kinesis F...
Real-time Machine Learning Analytics Using Structured Streaming and Kinesis F...Real-time Machine Learning Analytics Using Structured Streaming and Kinesis F...
Real-time Machine Learning Analytics Using Structured Streaming and Kinesis F...
 
Whats new in_mlflow
Whats new in_mlflowWhats new in_mlflow
Whats new in_mlflow
 
C19013010 the tutorial to build shared ai services session 1
C19013010  the tutorial to build shared ai services session 1C19013010  the tutorial to build shared ai services session 1
C19013010 the tutorial to build shared ai services session 1
 
Automated Production Ready ML at Scale
Automated Production Ready ML at ScaleAutomated Production Ready ML at Scale
Automated Production Ready ML at Scale
 
Ai lifecycle and navigator
Ai lifecycle and navigatorAi lifecycle and navigator
Ai lifecycle and navigator
 
Software engineering practices for the data science and machine learning life...
Software engineering practices for the data science and machine learning life...Software engineering practices for the data science and machine learning life...
Software engineering practices for the data science and machine learning life...
 
Real-time Machine Learning Analytics Using Structured Streaming and Kinesis F...
Real-time Machine Learning Analytics Using Structured Streaming and Kinesis F...Real-time Machine Learning Analytics Using Structured Streaming and Kinesis F...
Real-time Machine Learning Analytics Using Structured Streaming and Kinesis F...
 

Recently uploaded

Structuring Teams and Portfolios for Success
Structuring Teams and Portfolios for SuccessStructuring Teams and Portfolios for Success
Structuring Teams and Portfolios for Success
UXDXConf
 
Future Visions: Predictions to Guide and Time Tech Innovation, Peter Udo Diehl
Future Visions: Predictions to Guide and Time Tech Innovation, Peter Udo DiehlFuture Visions: Predictions to Guide and Time Tech Innovation, Peter Udo Diehl
Future Visions: Predictions to Guide and Time Tech Innovation, Peter Udo Diehl
Peter Udo Diehl
 

Recently uploaded (20)

ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...
ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...
ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...
 
TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...
TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...
TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...
 
A Business-Centric Approach to Design System Strategy
A Business-Centric Approach to Design System StrategyA Business-Centric Approach to Design System Strategy
A Business-Centric Approach to Design System Strategy
 
IESVE for Early Stage Design and Planning
IESVE for Early Stage Design and PlanningIESVE for Early Stage Design and Planning
IESVE for Early Stage Design and Planning
 
AI presentation and introduction - Retrieval Augmented Generation RAG 101
AI presentation and introduction - Retrieval Augmented Generation RAG 101AI presentation and introduction - Retrieval Augmented Generation RAG 101
AI presentation and introduction - Retrieval Augmented Generation RAG 101
 
Top 10 Symfony Development Companies 2024
Top 10 Symfony Development Companies 2024Top 10 Symfony Development Companies 2024
Top 10 Symfony Development Companies 2024
 
ECS 2024 Teams Premium - Pretty Secure
ECS 2024   Teams Premium - Pretty SecureECS 2024   Teams Premium - Pretty Secure
ECS 2024 Teams Premium - Pretty Secure
 
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...
 
WSO2CONMay2024OpenSourceConferenceDebrief.pptx
WSO2CONMay2024OpenSourceConferenceDebrief.pptxWSO2CONMay2024OpenSourceConferenceDebrief.pptx
WSO2CONMay2024OpenSourceConferenceDebrief.pptx
 
How we scaled to 80K users by doing nothing!.pdf
How we scaled to 80K users by doing nothing!.pdfHow we scaled to 80K users by doing nothing!.pdf
How we scaled to 80K users by doing nothing!.pdf
 
Salesforce Adoption – Metrics, Methods, and Motivation, Antone Kom
Salesforce Adoption – Metrics, Methods, and Motivation, Antone KomSalesforce Adoption – Metrics, Methods, and Motivation, Antone Kom
Salesforce Adoption – Metrics, Methods, and Motivation, Antone Kom
 
How Red Hat Uses FDO in Device Lifecycle _ Costin and Vitaliy at Red Hat.pdf
How Red Hat Uses FDO in Device Lifecycle _ Costin and Vitaliy at Red Hat.pdfHow Red Hat Uses FDO in Device Lifecycle _ Costin and Vitaliy at Red Hat.pdf
How Red Hat Uses FDO in Device Lifecycle _ Costin and Vitaliy at Red Hat.pdf
 
Powerful Start- the Key to Project Success, Barbara Laskowska
Powerful Start- the Key to Project Success, Barbara LaskowskaPowerful Start- the Key to Project Success, Barbara Laskowska
Powerful Start- the Key to Project Success, Barbara Laskowska
 
Designing for Hardware Accessibility at Comcast
Designing for Hardware Accessibility at ComcastDesigning for Hardware Accessibility at Comcast
Designing for Hardware Accessibility at Comcast
 
What's New in Teams Calling, Meetings and Devices April 2024
What's New in Teams Calling, Meetings and Devices April 2024What's New in Teams Calling, Meetings and Devices April 2024
What's New in Teams Calling, Meetings and Devices April 2024
 
The Metaverse: Are We There Yet?
The  Metaverse:    Are   We  There  Yet?The  Metaverse:    Are   We  There  Yet?
The Metaverse: Are We There Yet?
 
The UX of Automation by AJ King, Senior UX Researcher, Ocado
The UX of Automation by AJ King, Senior UX Researcher, OcadoThe UX of Automation by AJ King, Senior UX Researcher, Ocado
The UX of Automation by AJ King, Senior UX Researcher, Ocado
 
Structuring Teams and Portfolios for Success
Structuring Teams and Portfolios for SuccessStructuring Teams and Portfolios for Success
Structuring Teams and Portfolios for Success
 
Future Visions: Predictions to Guide and Time Tech Innovation, Peter Udo Diehl
Future Visions: Predictions to Guide and Time Tech Innovation, Peter Udo DiehlFuture Visions: Predictions to Guide and Time Tech Innovation, Peter Udo Diehl
Future Visions: Predictions to Guide and Time Tech Innovation, Peter Udo Diehl
 
10 Differences between Sales Cloud and CPQ, Blanka Doktorová
10 Differences between Sales Cloud and CPQ, Blanka Doktorová10 Differences between Sales Cloud and CPQ, Blanka Doktorová
10 Differences between Sales Cloud and CPQ, Blanka Doktorová
 

An Architecture for Agile Machine Learning in Real-Time Applications