SlideShare a Scribd company logo
1 of 15
Download to read offline
DATA	
  
SCIENTIST	
  
is	
  	
  
NOT	
  
a	
  defined	
  term	
   This	
  is	
  not…	
  a	
  Data	
  Scien9st	
  	
  
www.dataiku.com	
  	
  -­‐	
  @dataiku	
  -­‐	
  @baAymarc	
  
MACHINE	
  
LEARNING	
  
EXPERT	
  
DATA	
  
CLEANER	
  
DATA	
  LEAK	
  FIXER	
  
END	
  OF	
  	
  
(HADOOP)	
  JOB	
  	
  
DATA	
  
WAITER	
  
How	
  can	
  we	
  	
  
HELP	
  	
  
DATA	
  SCIENTISTS	
  
to	
  	
  
FOCUS	
  
on	
  the	
  	
  
REAL	
  PROBLEMS	
  ?	
  	
  
Pain	
  points	
  
•  Data	
  prepara9on	
  is	
  9me-­‐consuming	
  
	
  
•  Machine	
  learning	
  is	
  hard	
  to	
  understand	
  
•  Insights	
  and	
  models	
  (almost)	
  never	
  reach	
  
produc9on	
  
Data	
  Science	
  Studio	
  
•  A	
  democra9c	
  &	
  ready	
  to	
  use	
  Data	
  Science	
  
Studio	
  to	
  start	
  innova9ng	
  with	
  data!	
  
Ready	
  to	
  Use	
  Data	
  
Science	
  PlaYorm	
  
Common	
  playground	
  for	
  
innova9on	
  
Accessible	
  Sta9s9cs	
  &	
  
Machine	
  Learning	
  for	
  
everyone	
  
Handle	
  real-­‐life	
  data	
  
Data	
  Science	
  Studio	
  
Visual	
  and	
  Interac9ve	
  Data	
  
Prepara9on	
  
For	
  Data	
  Cleaners	
  
Guided	
  Machine	
  Learning	
  
For	
  non	
  Machine	
  Learning	
  Experts	
  
Produc9on	
  ready	
  
For	
  Data	
  Leak	
  Fixers	
  
Visual	
  Data	
  
Prepara9on	
  
Visual	
  Data	
  Prepara9on	
  
•  Interac9ve	
  UI	
  with	
  instant	
  feedback	
  and	
  
sugges9ons	
  
•  Reversibility	
  of	
  the	
  script,	
  data	
  integrity	
  
•  Explora9on	
  of	
  data:	
  quick	
  analysis,	
  facets	
  
•  Cleansing:	
  missing	
  values,	
  outliers,	
  parsing	
  
•  Enrichment:	
  GeoIP,	
  Holidays,	
  joins	
  
•  Produc9on-­‐ready:	
  integra9on	
  within	
  a	
  flow	
  
Guided	
  Machine	
  	
  
Learning	
  
Produc9on	
  
&	
  orchestra9on	
  
Data	
  Science	
  Studio:	
  	
  
benefits	
  
•  Real-­‐9me	
  and	
  interac9ve	
  
–  Transforma9on	
  effects	
  can	
  be	
  previsualized	
  in	
  real-­‐9me	
  
	
  
•  Transparent	
  and	
  traceable	
  
–  Keep	
  the	
  full	
  history	
  of	
  your	
  data	
  transforma9on	
  logics	
  and	
  
model	
  designs	
  
•  Easy	
  access	
  to	
  machine	
  learning	
  
–  Get	
  started	
  with	
  our	
  app	
  templates,	
  bootstrap	
  your	
  model	
  
and	
  features	
  selec9ons,	
  then	
  go	
  further!	
  
	
  
•  Scalable	
  and	
  Produc9on	
  Ready	
  
–  Apply	
  your	
  recipes	
  on	
  your	
  cluster	
  on	
  terabytes	
  of	
  data	
  
Dataiku	
  at	
  a	
  glance	
  
•  Founded	
  in	
  2013	
  by	
  Data	
  and	
  Search	
  Engine	
  veterans	
  
•  From	
  “data”	
  and	
  “haïku”	
  
“data	
  can	
  be	
  big	
  	
  
solu;on	
  would	
  be	
  small	
  
feel	
  the	
  hot	
  wind”	
  
•  1	
  goal:	
  make	
  Data	
  Science	
  accessible	
  to	
  anyone!	
  
	
  
	
  
	
  
	
  
	
  
	
  
Contact:	
  marc.baAy@dataiku.com	
  -­‐	
  @baAymarc	
  -­‐	
  github.com/dataiku	
  

More Related Content

What's hot

Splitting the Check on Compliance and Security
Splitting the Check on Compliance and SecuritySplitting the Check on Compliance and Security
Splitting the Check on Compliance and SecurityJason Chan
 
McAfee - MVISION Cloud (MVC) - Cloud Access Security Broker (CASB)
McAfee - MVISION Cloud (MVC) - Cloud Access Security Broker (CASB)McAfee - MVISION Cloud (MVC) - Cloud Access Security Broker (CASB)
McAfee - MVISION Cloud (MVC) - Cloud Access Security Broker (CASB)Iftikhar Ali Iqbal
 
Emeli Dral (Evidently AI) – Analyze it: production monitoring for machine lea...
Emeli Dral (Evidently AI) – Analyze it: production monitoring for machine lea...Emeli Dral (Evidently AI) – Analyze it: production monitoring for machine lea...
Emeli Dral (Evidently AI) – Analyze it: production monitoring for machine lea...Codiax
 
Smarter Supply Chain – IBM Case Study in Supply Chain Transformation and Inno...
Smarter Supply Chain – IBM Case Study in Supply Chain Transformation and Inno...Smarter Supply Chain – IBM Case Study in Supply Chain Transformation and Inno...
Smarter Supply Chain – IBM Case Study in Supply Chain Transformation and Inno...NUS-ISS
 
Datascience and python
Datascience and pythonDatascience and python
Datascience and pythonUmmeSalmaM1
 
Building an ML Platform with Ray and MLflow
Building an ML Platform with Ray and MLflowBuilding an ML Platform with Ray and MLflow
Building an ML Platform with Ray and MLflowDatabricks
 
Demystifying observability
Demystifying observability Demystifying observability
Demystifying observability Abigail Bangser
 
M365 e3 and identity and threat protection and compliance new skus
M365 e3 and identity and threat protection and compliance new skusM365 e3 and identity and threat protection and compliance new skus
M365 e3 and identity and threat protection and compliance new skusSpencerLuke2
 
Pivotal Cloud Foundry: A Technical Overview
Pivotal Cloud Foundry: A Technical OverviewPivotal Cloud Foundry: A Technical Overview
Pivotal Cloud Foundry: A Technical OverviewVMware Tanzu
 
Introduction To Confluence
Introduction To ConfluenceIntroduction To Confluence
Introduction To ConfluenceHua Soon Sim
 
Confluence vs sharepoint compared
Confluence vs sharepoint comparedConfluence vs sharepoint compared
Confluence vs sharepoint comparedNagaraj Yerram
 
A Modern Data Architecture for Microservices
A Modern Data Architecture for MicroservicesA Modern Data Architecture for Microservices
A Modern Data Architecture for MicroservicesAmazon Web Services
 
How to justify the economic value of your data investment
How to justify the economic value of your data investmentHow to justify the economic value of your data investment
How to justify the economic value of your data investmentSplunk
 
Orchestrator - Practical Approach to host UiPath Orchestrator
Orchestrator - Practical Approach to host UiPath OrchestratorOrchestrator - Practical Approach to host UiPath Orchestrator
Orchestrator - Practical Approach to host UiPath OrchestratorVibhor Shrivastava
 

What's hot (20)

Splitting the Check on Compliance and Security
Splitting the Check on Compliance and SecuritySplitting the Check on Compliance and Security
Splitting the Check on Compliance and Security
 
Software Performance Engineering Services
Software Performance Engineering ServicesSoftware Performance Engineering Services
Software Performance Engineering Services
 
McAfee - MVISION Cloud (MVC) - Cloud Access Security Broker (CASB)
McAfee - MVISION Cloud (MVC) - Cloud Access Security Broker (CASB)McAfee - MVISION Cloud (MVC) - Cloud Access Security Broker (CASB)
McAfee - MVISION Cloud (MVC) - Cloud Access Security Broker (CASB)
 
Emeli Dral (Evidently AI) – Analyze it: production monitoring for machine lea...
Emeli Dral (Evidently AI) – Analyze it: production monitoring for machine lea...Emeli Dral (Evidently AI) – Analyze it: production monitoring for machine lea...
Emeli Dral (Evidently AI) – Analyze it: production monitoring for machine lea...
 
Smarter Supply Chain – IBM Case Study in Supply Chain Transformation and Inno...
Smarter Supply Chain – IBM Case Study in Supply Chain Transformation and Inno...Smarter Supply Chain – IBM Case Study in Supply Chain Transformation and Inno...
Smarter Supply Chain – IBM Case Study in Supply Chain Transformation and Inno...
 
Overview of Low-code
Overview of Low-code Overview of Low-code
Overview of Low-code
 
Datascience and python
Datascience and pythonDatascience and python
Datascience and python
 
Confluence
ConfluenceConfluence
Confluence
 
Building an ML Platform with Ray and MLflow
Building an ML Platform with Ray and MLflowBuilding an ML Platform with Ray and MLflow
Building an ML Platform with Ray and MLflow
 
Demystifying observability
Demystifying observability Demystifying observability
Demystifying observability
 
M365 e3 and identity and threat protection and compliance new skus
M365 e3 and identity and threat protection and compliance new skusM365 e3 and identity and threat protection and compliance new skus
M365 e3 and identity and threat protection and compliance new skus
 
Pivotal Cloud Foundry: A Technical Overview
Pivotal Cloud Foundry: A Technical OverviewPivotal Cloud Foundry: A Technical Overview
Pivotal Cloud Foundry: A Technical Overview
 
Dynatrace
DynatraceDynatrace
Dynatrace
 
Power Automate
Power AutomatePower Automate
Power Automate
 
Introduction To Confluence
Introduction To ConfluenceIntroduction To Confluence
Introduction To Confluence
 
Confluence vs sharepoint compared
Confluence vs sharepoint comparedConfluence vs sharepoint compared
Confluence vs sharepoint compared
 
Getting your enterprise ready for Microsoft 365 Copilot
Getting your enterprise ready for Microsoft 365 CopilotGetting your enterprise ready for Microsoft 365 Copilot
Getting your enterprise ready for Microsoft 365 Copilot
 
A Modern Data Architecture for Microservices
A Modern Data Architecture for MicroservicesA Modern Data Architecture for Microservices
A Modern Data Architecture for Microservices
 
How to justify the economic value of your data investment
How to justify the economic value of your data investmentHow to justify the economic value of your data investment
How to justify the economic value of your data investment
 
Orchestrator - Practical Approach to host UiPath Orchestrator
Orchestrator - Practical Approach to host UiPath OrchestratorOrchestrator - Practical Approach to host UiPath Orchestrator
Orchestrator - Practical Approach to host UiPath Orchestrator
 

Viewers also liked

The Rise of the DataOps - Dataiku - J On the Beach 2016
The Rise of the DataOps - Dataiku - J On the Beach 2016 The Rise of the DataOps - Dataiku - J On the Beach 2016
The Rise of the DataOps - Dataiku - J On the Beach 2016 Dataiku
 
Dataiku, Pitch Data Innovation Night, Boston, Septembre 16th
Dataiku, Pitch Data Innovation Night, Boston, Septembre 16thDataiku, Pitch Data Innovation Night, Boston, Septembre 16th
Dataiku, Pitch Data Innovation Night, Boston, Septembre 16thDataiku
 
Dataiku - data driven nyc - april 2016 - the solitude of the data team m...
Dataiku  -  data driven nyc  - april  2016 - the  solitude of the data team m...Dataiku  -  data driven nyc  - april  2016 - the  solitude of the data team m...
Dataiku - data driven nyc - april 2016 - the solitude of the data team m...Dataiku
 
Comment Coyote Systems utilse le Data Science Studio de Dataiku pour optimise...
Comment Coyote Systems utilse le Data Science Studio de Dataiku pour optimise...Comment Coyote Systems utilse le Data Science Studio de Dataiku pour optimise...
Comment Coyote Systems utilse le Data Science Studio de Dataiku pour optimise...Le_GFII
 
Dataiku - google cloud platform roadshow - october 2013
Dataiku  - google cloud platform roadshow - october 2013Dataiku  - google cloud platform roadshow - october 2013
Dataiku - google cloud platform roadshow - october 2013Dataiku
 
Online Games Analytics - Data Science for Fun
Online Games Analytics - Data Science for FunOnline Games Analytics - Data Science for Fun
Online Games Analytics - Data Science for FunDataiku
 
"Machine Learning and Internet of Things, the future of medical prevention", ...
"Machine Learning and Internet of Things, the future of medical prevention", ..."Machine Learning and Internet of Things, the future of medical prevention", ...
"Machine Learning and Internet of Things, the future of medical prevention", ...Dataconomy Media
 
Dataiku - hadoop ecosystem - @Epitech Paris - janvier 2014
Dataiku  - hadoop ecosystem - @Epitech Paris - janvier 2014Dataiku  - hadoop ecosystem - @Epitech Paris - janvier 2014
Dataiku - hadoop ecosystem - @Epitech Paris - janvier 2014Dataiku
 
Eat whatever you can with PyBabe
Eat whatever you can with PyBabeEat whatever you can with PyBabe
Eat whatever you can with PyBabeDataiku
 
From Labelling Open data images to building a private recommender system
From Labelling Open data images to building a private recommender systemFrom Labelling Open data images to building a private recommender system
From Labelling Open data images to building a private recommender systemPierre Gutierrez
 
Dataiku pig - hive - cascading
Dataiku   pig - hive - cascadingDataiku   pig - hive - cascading
Dataiku pig - hive - cascadingDataiku
 
BreizhJUG - Janvier 2014 - Big Data - Dataiku - Pages Jaunes
BreizhJUG - Janvier 2014 - Big Data -  Dataiku - Pages JaunesBreizhJUG - Janvier 2014 - Big Data -  Dataiku - Pages Jaunes
BreizhJUG - Janvier 2014 - Big Data - Dataiku - Pages JaunesDataiku
 
Dataiku productive application to production - pap is may 2015
Dataiku    productive application to production - pap is may 2015 Dataiku    productive application to production - pap is may 2015
Dataiku productive application to production - pap is may 2015 Dataiku
 
How to Build a Successful Data Team - Florian Douetteau (@Dataiku)
How to Build a Successful Data Team - Florian Douetteau (@Dataiku) How to Build a Successful Data Team - Florian Douetteau (@Dataiku)
How to Build a Successful Data Team - Florian Douetteau (@Dataiku) Dataiku
 
Churn prediction data modeling
Churn prediction data modelingChurn prediction data modeling
Churn prediction data modelingPierre Gutierrez
 
How to Build Successful Data Team - Dataiku ?
How to Build Successful Data Team -  Dataiku ? How to Build Successful Data Team -  Dataiku ?
How to Build Successful Data Team - Dataiku ? Dataiku
 
The 3 Key Barriers Keeping Companies from Deploying Data Products
The 3 Key Barriers Keeping Companies from Deploying Data Products The 3 Key Barriers Keeping Companies from Deploying Data Products
The 3 Key Barriers Keeping Companies from Deploying Data Products Dataiku
 
10 R Packages to Win Kaggle Competitions
10 R Packages to Win Kaggle Competitions10 R Packages to Win Kaggle Competitions
10 R Packages to Win Kaggle CompetitionsDataRobot
 

Viewers also liked (20)

The Rise of the DataOps - Dataiku - J On the Beach 2016
The Rise of the DataOps - Dataiku - J On the Beach 2016 The Rise of the DataOps - Dataiku - J On the Beach 2016
The Rise of the DataOps - Dataiku - J On the Beach 2016
 
Dataiku, Pitch Data Innovation Night, Boston, Septembre 16th
Dataiku, Pitch Data Innovation Night, Boston, Septembre 16thDataiku, Pitch Data Innovation Night, Boston, Septembre 16th
Dataiku, Pitch Data Innovation Night, Boston, Septembre 16th
 
Dataiku - data driven nyc - april 2016 - the solitude of the data team m...
Dataiku  -  data driven nyc  - april  2016 - the  solitude of the data team m...Dataiku  -  data driven nyc  - april  2016 - the  solitude of the data team m...
Dataiku - data driven nyc - april 2016 - the solitude of the data team m...
 
Evaluation Fit-for-Purpose
Evaluation Fit-for-PurposeEvaluation Fit-for-Purpose
Evaluation Fit-for-Purpose
 
Comment Coyote Systems utilse le Data Science Studio de Dataiku pour optimise...
Comment Coyote Systems utilse le Data Science Studio de Dataiku pour optimise...Comment Coyote Systems utilse le Data Science Studio de Dataiku pour optimise...
Comment Coyote Systems utilse le Data Science Studio de Dataiku pour optimise...
 
Dataiku - google cloud platform roadshow - october 2013
Dataiku  - google cloud platform roadshow - october 2013Dataiku  - google cloud platform roadshow - october 2013
Dataiku - google cloud platform roadshow - october 2013
 
Online Games Analytics - Data Science for Fun
Online Games Analytics - Data Science for FunOnline Games Analytics - Data Science for Fun
Online Games Analytics - Data Science for Fun
 
"Machine Learning and Internet of Things, the future of medical prevention", ...
"Machine Learning and Internet of Things, the future of medical prevention", ..."Machine Learning and Internet of Things, the future of medical prevention", ...
"Machine Learning and Internet of Things, the future of medical prevention", ...
 
Before Kaggle
Before KaggleBefore Kaggle
Before Kaggle
 
Dataiku - hadoop ecosystem - @Epitech Paris - janvier 2014
Dataiku  - hadoop ecosystem - @Epitech Paris - janvier 2014Dataiku  - hadoop ecosystem - @Epitech Paris - janvier 2014
Dataiku - hadoop ecosystem - @Epitech Paris - janvier 2014
 
Eat whatever you can with PyBabe
Eat whatever you can with PyBabeEat whatever you can with PyBabe
Eat whatever you can with PyBabe
 
From Labelling Open data images to building a private recommender system
From Labelling Open data images to building a private recommender systemFrom Labelling Open data images to building a private recommender system
From Labelling Open data images to building a private recommender system
 
Dataiku pig - hive - cascading
Dataiku   pig - hive - cascadingDataiku   pig - hive - cascading
Dataiku pig - hive - cascading
 
BreizhJUG - Janvier 2014 - Big Data - Dataiku - Pages Jaunes
BreizhJUG - Janvier 2014 - Big Data -  Dataiku - Pages JaunesBreizhJUG - Janvier 2014 - Big Data -  Dataiku - Pages Jaunes
BreizhJUG - Janvier 2014 - Big Data - Dataiku - Pages Jaunes
 
Dataiku productive application to production - pap is may 2015
Dataiku    productive application to production - pap is may 2015 Dataiku    productive application to production - pap is may 2015
Dataiku productive application to production - pap is may 2015
 
How to Build a Successful Data Team - Florian Douetteau (@Dataiku)
How to Build a Successful Data Team - Florian Douetteau (@Dataiku) How to Build a Successful Data Team - Florian Douetteau (@Dataiku)
How to Build a Successful Data Team - Florian Douetteau (@Dataiku)
 
Churn prediction data modeling
Churn prediction data modelingChurn prediction data modeling
Churn prediction data modeling
 
How to Build Successful Data Team - Dataiku ?
How to Build Successful Data Team -  Dataiku ? How to Build Successful Data Team -  Dataiku ?
How to Build Successful Data Team - Dataiku ?
 
The 3 Key Barriers Keeping Companies from Deploying Data Products
The 3 Key Barriers Keeping Companies from Deploying Data Products The 3 Key Barriers Keeping Companies from Deploying Data Products
The 3 Key Barriers Keeping Companies from Deploying Data Products
 
10 R Packages to Win Kaggle Competitions
10 R Packages to Win Kaggle Competitions10 R Packages to Win Kaggle Competitions
10 R Packages to Win Kaggle Competitions
 

Similar to Dataiku, Pitch at Data-Driven NYC, New York City, September 17th 2013

Part 1: Introducing the Cloudera Data Science Workbench
Part 1: Introducing the Cloudera Data Science WorkbenchPart 1: Introducing the Cloudera Data Science Workbench
Part 1: Introducing the Cloudera Data Science WorkbenchCloudera, Inc.
 
Engineering Machine Learning Data Pipelines Series: Big Data Quality - Cleans...
Engineering Machine Learning Data Pipelines Series: Big Data Quality - Cleans...Engineering Machine Learning Data Pipelines Series: Big Data Quality - Cleans...
Engineering Machine Learning Data Pipelines Series: Big Data Quality - Cleans...Precisely
 
Data science tools of the trade
Data science tools of the tradeData science tools of the trade
Data science tools of the tradeFangda Wang
 
Enabling Data centric Teams
Enabling Data centric TeamsEnabling Data centric Teams
Enabling Data centric TeamsData Con LA
 
Maciej Marek (Philip Morris International) - The Tools of The Trade
Maciej Marek (Philip Morris International) - The Tools of The TradeMaciej Marek (Philip Morris International) - The Tools of The Trade
Maciej Marek (Philip Morris International) - The Tools of The TradeCodiax
 
Data science presentation
Data science presentationData science presentation
Data science presentationMSDEVMTL
 
Machine Learning on dirty data - Dataiku - Forum du GFII 2014
Machine Learning on dirty data - Dataiku - Forum du GFII 2014Machine Learning on dirty data - Dataiku - Forum du GFII 2014
Machine Learning on dirty data - Dataiku - Forum du GFII 2014Le_GFII
 
Challenges of Operationalising Data Science in Production
Challenges of Operationalising Data Science in ProductionChallenges of Operationalising Data Science in Production
Challenges of Operationalising Data Science in Productioniguazio
 
Think Big | Enterprise Artificial Intelligence
Think Big | Enterprise Artificial IntelligenceThink Big | Enterprise Artificial Intelligence
Think Big | Enterprise Artificial IntelligenceData Science Milan
 
From open data to API-driven business
From open data to API-driven businessFrom open data to API-driven business
From open data to API-driven businessOpenDataSoft
 
Bridging the Gap: Analyzing Data in and Below the Cloud
Bridging the Gap: Analyzing Data in and Below the CloudBridging the Gap: Analyzing Data in and Below the Cloud
Bridging the Gap: Analyzing Data in and Below the CloudInside Analysis
 
Big Data for Data Scientists - Info Session
Big Data for Data Scientists - Info SessionBig Data for Data Scientists - Info Session
Big Data for Data Scientists - Info SessionWeCloudData
 
ITCamp 2019 - Andy Cross - Machine Learning with ML.NET and Azure Data Lake
ITCamp 2019 - Andy Cross - Machine Learning with ML.NET and Azure Data LakeITCamp 2019 - Andy Cross - Machine Learning with ML.NET and Azure Data Lake
ITCamp 2019 - Andy Cross - Machine Learning with ML.NET and Azure Data LakeITCamp
 
How I became ML Engineer
How I became ML Engineer How I became ML Engineer
How I became ML Engineer Kevin Lee
 
Bbbt presentation 210415_final_2
Bbbt presentation 210415_final_2Bbbt presentation 210415_final_2
Bbbt presentation 210415_final_2Roland Bullivant
 
Democratizing AI with Apache Spark
Democratizing AI with Apache SparkDemocratizing AI with Apache Spark
Democratizing AI with Apache SparkSpark Summit
 
Machine learning model to production
Machine learning model to productionMachine learning model to production
Machine learning model to productionGeorg Heiler
 
PXL Data Engineering Workshop By Selligent
PXL Data Engineering Workshop By Selligent PXL Data Engineering Workshop By Selligent
PXL Data Engineering Workshop By Selligent Jonny Daenen
 
Machine Learning Infrastructure
Machine Learning InfrastructureMachine Learning Infrastructure
Machine Learning InfrastructureSigOpt
 
Introduction to Python Syntax and Semantics
Introduction to Python Syntax and SemanticsIntroduction to Python Syntax and Semantics
Introduction to Python Syntax and SemanticsAdam Cook
 

Similar to Dataiku, Pitch at Data-Driven NYC, New York City, September 17th 2013 (20)

Part 1: Introducing the Cloudera Data Science Workbench
Part 1: Introducing the Cloudera Data Science WorkbenchPart 1: Introducing the Cloudera Data Science Workbench
Part 1: Introducing the Cloudera Data Science Workbench
 
Engineering Machine Learning Data Pipelines Series: Big Data Quality - Cleans...
Engineering Machine Learning Data Pipelines Series: Big Data Quality - Cleans...Engineering Machine Learning Data Pipelines Series: Big Data Quality - Cleans...
Engineering Machine Learning Data Pipelines Series: Big Data Quality - Cleans...
 
Data science tools of the trade
Data science tools of the tradeData science tools of the trade
Data science tools of the trade
 
Enabling Data centric Teams
Enabling Data centric TeamsEnabling Data centric Teams
Enabling Data centric Teams
 
Maciej Marek (Philip Morris International) - The Tools of The Trade
Maciej Marek (Philip Morris International) - The Tools of The TradeMaciej Marek (Philip Morris International) - The Tools of The Trade
Maciej Marek (Philip Morris International) - The Tools of The Trade
 
Data science presentation
Data science presentationData science presentation
Data science presentation
 
Machine Learning on dirty data - Dataiku - Forum du GFII 2014
Machine Learning on dirty data - Dataiku - Forum du GFII 2014Machine Learning on dirty data - Dataiku - Forum du GFII 2014
Machine Learning on dirty data - Dataiku - Forum du GFII 2014
 
Challenges of Operationalising Data Science in Production
Challenges of Operationalising Data Science in ProductionChallenges of Operationalising Data Science in Production
Challenges of Operationalising Data Science in Production
 
Think Big | Enterprise Artificial Intelligence
Think Big | Enterprise Artificial IntelligenceThink Big | Enterprise Artificial Intelligence
Think Big | Enterprise Artificial Intelligence
 
From open data to API-driven business
From open data to API-driven businessFrom open data to API-driven business
From open data to API-driven business
 
Bridging the Gap: Analyzing Data in and Below the Cloud
Bridging the Gap: Analyzing Data in and Below the CloudBridging the Gap: Analyzing Data in and Below the Cloud
Bridging the Gap: Analyzing Data in and Below the Cloud
 
Big Data for Data Scientists - Info Session
Big Data for Data Scientists - Info SessionBig Data for Data Scientists - Info Session
Big Data for Data Scientists - Info Session
 
ITCamp 2019 - Andy Cross - Machine Learning with ML.NET and Azure Data Lake
ITCamp 2019 - Andy Cross - Machine Learning with ML.NET and Azure Data LakeITCamp 2019 - Andy Cross - Machine Learning with ML.NET and Azure Data Lake
ITCamp 2019 - Andy Cross - Machine Learning with ML.NET and Azure Data Lake
 
How I became ML Engineer
How I became ML Engineer How I became ML Engineer
How I became ML Engineer
 
Bbbt presentation 210415_final_2
Bbbt presentation 210415_final_2Bbbt presentation 210415_final_2
Bbbt presentation 210415_final_2
 
Democratizing AI with Apache Spark
Democratizing AI with Apache SparkDemocratizing AI with Apache Spark
Democratizing AI with Apache Spark
 
Machine learning model to production
Machine learning model to productionMachine learning model to production
Machine learning model to production
 
PXL Data Engineering Workshop By Selligent
PXL Data Engineering Workshop By Selligent PXL Data Engineering Workshop By Selligent
PXL Data Engineering Workshop By Selligent
 
Machine Learning Infrastructure
Machine Learning InfrastructureMachine Learning Infrastructure
Machine Learning Infrastructure
 
Introduction to Python Syntax and Semantics
Introduction to Python Syntax and SemanticsIntroduction to Python Syntax and Semantics
Introduction to Python Syntax and Semantics
 

More from Dataiku

Applied Data Science Part 3: Getting dirty; data preparation and feature crea...
Applied Data Science Part 3: Getting dirty; data preparation and feature crea...Applied Data Science Part 3: Getting dirty; data preparation and feature crea...
Applied Data Science Part 3: Getting dirty; data preparation and feature crea...Dataiku
 
Applied Data Science Course Part 2: the data science workflow and basic model...
Applied Data Science Course Part 2: the data science workflow and basic model...Applied Data Science Course Part 2: the data science workflow and basic model...
Applied Data Science Course Part 2: the data science workflow and basic model...Dataiku
 
Applied Data Science Course Part 1: Concepts & your first ML model
Applied Data Science Course Part 1: Concepts & your first ML modelApplied Data Science Course Part 1: Concepts & your first ML model
Applied Data Science Course Part 1: Concepts & your first ML modelDataiku
 
The US Healthcare Industry
The US Healthcare IndustryThe US Healthcare Industry
The US Healthcare IndustryDataiku
 
Before Kaggle : from a business goal to a Machine Learning problem
Before Kaggle : from a business goal to a Machine Learning problem Before Kaggle : from a business goal to a Machine Learning problem
Before Kaggle : from a business goal to a Machine Learning problem Dataiku
 
04Juin2015_Symposium_Présentation_Coyote_Dataiku
04Juin2015_Symposium_Présentation_Coyote_Dataiku 04Juin2015_Symposium_Présentation_Coyote_Dataiku
04Juin2015_Symposium_Présentation_Coyote_Dataiku Dataiku
 
Coyote & Dataiku - Séminaire Dixit GFII du 13 04-2015
Coyote & Dataiku - Séminaire Dixit GFII du 13 04-2015Coyote & Dataiku - Séminaire Dixit GFII du 13 04-2015
Coyote & Dataiku - Séminaire Dixit GFII du 13 04-2015Dataiku
 
Dataiku - Big data paris 2015 - A Hybrid Platform, a Hybrid Team
Dataiku -  Big data paris 2015 - A Hybrid Platform, a Hybrid Team Dataiku -  Big data paris 2015 - A Hybrid Platform, a Hybrid Team
Dataiku - Big data paris 2015 - A Hybrid Platform, a Hybrid Team Dataiku
 
The paradox of big data - dataiku / oxalide APEROTECH
The paradox of big data - dataiku / oxalide APEROTECHThe paradox of big data - dataiku / oxalide APEROTECH
The paradox of big data - dataiku / oxalide APEROTECHDataiku
 
OWF 2014 - Take back control of your Web tracking - Dataiku
OWF 2014 - Take back control of your Web tracking - DataikuOWF 2014 - Take back control of your Web tracking - Dataiku
OWF 2014 - Take back control of your Web tracking - DataikuDataiku
 
Dataiku at SF DataMining Meetup - Kaggle Yandex Challenge
Dataiku at SF DataMining Meetup - Kaggle Yandex ChallengeDataiku at SF DataMining Meetup - Kaggle Yandex Challenge
Dataiku at SF DataMining Meetup - Kaggle Yandex ChallengeDataiku
 
Lambda Architecture - Storm, Trident, SummingBird ... - Architecture and Over...
Lambda Architecture - Storm, Trident, SummingBird ... - Architecture and Over...Lambda Architecture - Storm, Trident, SummingBird ... - Architecture and Over...
Lambda Architecture - Storm, Trident, SummingBird ... - Architecture and Over...Dataiku
 
Dataiku hadoop summit - semi-supervised learning with hadoop for understand...
Dataiku   hadoop summit - semi-supervised learning with hadoop for understand...Dataiku   hadoop summit - semi-supervised learning with hadoop for understand...
Dataiku hadoop summit - semi-supervised learning with hadoop for understand...Dataiku
 
Dataiku big data paris - the rise of the hadoop ecosystem
Dataiku   big data paris - the rise of the hadoop ecosystemDataiku   big data paris - the rise of the hadoop ecosystem
Dataiku big data paris - the rise of the hadoop ecosystemDataiku
 
Dataiku - for Data Geek Paris@Criteo - Close the Data Circle
Dataiku  - for Data Geek Paris@Criteo - Close the Data CircleDataiku  - for Data Geek Paris@Criteo - Close the Data Circle
Dataiku - for Data Geek Paris@Criteo - Close the Data CircleDataiku
 
Data Disruption for Insurance - Perspective from th
Data Disruption for Insurance - Perspective from thData Disruption for Insurance - Perspective from th
Data Disruption for Insurance - Perspective from thDataiku
 
Dataiku - From Big Data To Machine Learning
Dataiku - From Big Data To Machine LearningDataiku - From Big Data To Machine Learning
Dataiku - From Big Data To Machine LearningDataiku
 
Dataiku Flow and dctc - Berlin Buzzwords
Dataiku Flow and dctc - Berlin BuzzwordsDataiku Flow and dctc - Berlin Buzzwords
Dataiku Flow and dctc - Berlin BuzzwordsDataiku
 
Dataiku - Paris JUG 2013 - Hadoop is a batch
Dataiku - Paris JUG 2013 - Hadoop is a batch Dataiku - Paris JUG 2013 - Hadoop is a batch
Dataiku - Paris JUG 2013 - Hadoop is a batch Dataiku
 

More from Dataiku (19)

Applied Data Science Part 3: Getting dirty; data preparation and feature crea...
Applied Data Science Part 3: Getting dirty; data preparation and feature crea...Applied Data Science Part 3: Getting dirty; data preparation and feature crea...
Applied Data Science Part 3: Getting dirty; data preparation and feature crea...
 
Applied Data Science Course Part 2: the data science workflow and basic model...
Applied Data Science Course Part 2: the data science workflow and basic model...Applied Data Science Course Part 2: the data science workflow and basic model...
Applied Data Science Course Part 2: the data science workflow and basic model...
 
Applied Data Science Course Part 1: Concepts & your first ML model
Applied Data Science Course Part 1: Concepts & your first ML modelApplied Data Science Course Part 1: Concepts & your first ML model
Applied Data Science Course Part 1: Concepts & your first ML model
 
The US Healthcare Industry
The US Healthcare IndustryThe US Healthcare Industry
The US Healthcare Industry
 
Before Kaggle : from a business goal to a Machine Learning problem
Before Kaggle : from a business goal to a Machine Learning problem Before Kaggle : from a business goal to a Machine Learning problem
Before Kaggle : from a business goal to a Machine Learning problem
 
04Juin2015_Symposium_Présentation_Coyote_Dataiku
04Juin2015_Symposium_Présentation_Coyote_Dataiku 04Juin2015_Symposium_Présentation_Coyote_Dataiku
04Juin2015_Symposium_Présentation_Coyote_Dataiku
 
Coyote & Dataiku - Séminaire Dixit GFII du 13 04-2015
Coyote & Dataiku - Séminaire Dixit GFII du 13 04-2015Coyote & Dataiku - Séminaire Dixit GFII du 13 04-2015
Coyote & Dataiku - Séminaire Dixit GFII du 13 04-2015
 
Dataiku - Big data paris 2015 - A Hybrid Platform, a Hybrid Team
Dataiku -  Big data paris 2015 - A Hybrid Platform, a Hybrid Team Dataiku -  Big data paris 2015 - A Hybrid Platform, a Hybrid Team
Dataiku - Big data paris 2015 - A Hybrid Platform, a Hybrid Team
 
The paradox of big data - dataiku / oxalide APEROTECH
The paradox of big data - dataiku / oxalide APEROTECHThe paradox of big data - dataiku / oxalide APEROTECH
The paradox of big data - dataiku / oxalide APEROTECH
 
OWF 2014 - Take back control of your Web tracking - Dataiku
OWF 2014 - Take back control of your Web tracking - DataikuOWF 2014 - Take back control of your Web tracking - Dataiku
OWF 2014 - Take back control of your Web tracking - Dataiku
 
Dataiku at SF DataMining Meetup - Kaggle Yandex Challenge
Dataiku at SF DataMining Meetup - Kaggle Yandex ChallengeDataiku at SF DataMining Meetup - Kaggle Yandex Challenge
Dataiku at SF DataMining Meetup - Kaggle Yandex Challenge
 
Lambda Architecture - Storm, Trident, SummingBird ... - Architecture and Over...
Lambda Architecture - Storm, Trident, SummingBird ... - Architecture and Over...Lambda Architecture - Storm, Trident, SummingBird ... - Architecture and Over...
Lambda Architecture - Storm, Trident, SummingBird ... - Architecture and Over...
 
Dataiku hadoop summit - semi-supervised learning with hadoop for understand...
Dataiku   hadoop summit - semi-supervised learning with hadoop for understand...Dataiku   hadoop summit - semi-supervised learning with hadoop for understand...
Dataiku hadoop summit - semi-supervised learning with hadoop for understand...
 
Dataiku big data paris - the rise of the hadoop ecosystem
Dataiku   big data paris - the rise of the hadoop ecosystemDataiku   big data paris - the rise of the hadoop ecosystem
Dataiku big data paris - the rise of the hadoop ecosystem
 
Dataiku - for Data Geek Paris@Criteo - Close the Data Circle
Dataiku  - for Data Geek Paris@Criteo - Close the Data CircleDataiku  - for Data Geek Paris@Criteo - Close the Data Circle
Dataiku - for Data Geek Paris@Criteo - Close the Data Circle
 
Data Disruption for Insurance - Perspective from th
Data Disruption for Insurance - Perspective from thData Disruption for Insurance - Perspective from th
Data Disruption for Insurance - Perspective from th
 
Dataiku - From Big Data To Machine Learning
Dataiku - From Big Data To Machine LearningDataiku - From Big Data To Machine Learning
Dataiku - From Big Data To Machine Learning
 
Dataiku Flow and dctc - Berlin Buzzwords
Dataiku Flow and dctc - Berlin BuzzwordsDataiku Flow and dctc - Berlin Buzzwords
Dataiku Flow and dctc - Berlin Buzzwords
 
Dataiku - Paris JUG 2013 - Hadoop is a batch
Dataiku - Paris JUG 2013 - Hadoop is a batch Dataiku - Paris JUG 2013 - Hadoop is a batch
Dataiku - Paris JUG 2013 - Hadoop is a batch
 

Recently uploaded

DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 AmsterdamDEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 AmsterdamUiPathCommunity
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FMESafe Software
 
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...apidays
 
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...Angeliki Cooney
 
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Victor Rentea
 
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdfRising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdfOrbitshub
 
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAndrey Devyatkin
 
Six Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal OntologySix Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal Ontologyjohnbeverley2021
 
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...Orbitshub
 
WSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering DevelopersWSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering DevelopersWSO2
 
Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...apidays
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerThousandEyes
 
Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)Zilliz
 
DBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDropbox
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FMESafe Software
 
Mcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot Model
Mcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot ModelMcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot Model
Mcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot ModelDeepika Singh
 
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, AdobeApidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobeapidays
 
Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...
Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...
Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...Zilliz
 
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ..."I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...Zilliz
 
presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century educationjfdjdjcjdnsjd
 

Recently uploaded (20)

DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 AmsterdamDEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
 
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
 
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
 
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdfRising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
 
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of Terraform
 
Six Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal OntologySix Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal Ontology
 
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
 
WSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering DevelopersWSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering Developers
 
Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)
 
DBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor Presentation
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 
Mcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot Model
Mcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot ModelMcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot Model
Mcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot Model
 
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, AdobeApidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
 
Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...
Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...
Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...
 
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ..."I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
 
presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century education
 

Dataiku, Pitch at Data-Driven NYC, New York City, September 17th 2013

  • 1. DATA   SCIENTIST   is     NOT   a  defined  term   This  is  not…  a  Data  Scien9st     www.dataiku.com    -­‐  @dataiku  -­‐  @baAymarc  
  • 5. END  OF     (HADOOP)  JOB     DATA   WAITER  
  • 6. How  can  we     HELP     DATA  SCIENTISTS   to     FOCUS   on  the     REAL  PROBLEMS  ?    
  • 7. Pain  points   •  Data  prepara9on  is  9me-­‐consuming     •  Machine  learning  is  hard  to  understand   •  Insights  and  models  (almost)  never  reach   produc9on  
  • 8. Data  Science  Studio   •  A  democra9c  &  ready  to  use  Data  Science   Studio  to  start  innova9ng  with  data!   Ready  to  Use  Data   Science  PlaYorm   Common  playground  for   innova9on   Accessible  Sta9s9cs  &   Machine  Learning  for   everyone   Handle  real-­‐life  data  
  • 9. Data  Science  Studio   Visual  and  Interac9ve  Data   Prepara9on   For  Data  Cleaners   Guided  Machine  Learning   For  non  Machine  Learning  Experts   Produc9on  ready   For  Data  Leak  Fixers  
  • 11. Visual  Data  Prepara9on   •  Interac9ve  UI  with  instant  feedback  and   sugges9ons   •  Reversibility  of  the  script,  data  integrity   •  Explora9on  of  data:  quick  analysis,  facets   •  Cleansing:  missing  values,  outliers,  parsing   •  Enrichment:  GeoIP,  Holidays,  joins   •  Produc9on-­‐ready:  integra9on  within  a  flow  
  • 12. Guided  Machine     Learning  
  • 14. Data  Science  Studio:     benefits   •  Real-­‐9me  and  interac9ve   –  Transforma9on  effects  can  be  previsualized  in  real-­‐9me     •  Transparent  and  traceable   –  Keep  the  full  history  of  your  data  transforma9on  logics  and   model  designs   •  Easy  access  to  machine  learning   –  Get  started  with  our  app  templates,  bootstrap  your  model   and  features  selec9ons,  then  go  further!     •  Scalable  and  Produc9on  Ready   –  Apply  your  recipes  on  your  cluster  on  terabytes  of  data  
  • 15. Dataiku  at  a  glance   •  Founded  in  2013  by  Data  and  Search  Engine  veterans   •  From  “data”  and  “haïku”   “data  can  be  big     solu;on  would  be  small   feel  the  hot  wind”   •  1  goal:  make  Data  Science  accessible  to  anyone!               Contact:  marc.baAy@dataiku.com  -­‐  @baAymarc  -­‐  github.com/dataiku