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DevOps for Software Development
DevOps for Data Science
Ingest Transform Explore Model DeployÚ Ú Ú Ú Ú Score Visualize MeasureÚ Ú ÚÚ Model Score ƒ(x) Preparation Modeling Operati...
DevOps MLOps Code testing Code reproducibility App deployment Model retraining Model validation Model reproducibility Mode...
ML SERVER AZURE ML STUDIO SQL Server (In-database ML) DATA SCIENCE VM BATCH AIHDINSIGHTAZURE DATABRICKSDATA LAKE ANALYTICS...
DEMO
KubeFlow on Azure
KubeFlow on Azure
KubeFlow on Azure
KubeFlow on Azure
KubeFlow on Azure
KubeFlow on Azure
KubeFlow on Azure
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4 jan. 2021

KubeFlow on Azure

Cloud Native Night, December 2020, talk by Sascha Dittmann (Cloud Solution Architect, Microsoft)

== Please download slides if blurred! ==

Abstract:
Data scientists and software developers are increasingly working together on projects. It is therefore not surprising that more and more best practices from the world of software development are being adopted in the data science field. Kubeflow is an example of this.

The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Its goal is not to recreate other services, but to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures. Anywhere you are running Kubernetes, you should be able to run Kubeflow.

About Sascha:
Sascha Dittmann is working as a Cloud Solution Architect at Microsoft. In his role he supports customers and partners to implement successful cloud solutions. His focus is on software development for Microsoft Azure, SQL Server Business Intelligence, Big Data as well as Data Science.
Before he joined Microsoft in 2015, he was working for Ernst & Young as a Software Developer (13 years) and Solution Architect (3 years).
He's an author of several technical articles and a regular speaker at user groups and conferences. Between 2012 and 2015 he received 4 Microsoft MVP awards for Microsoft Azure.

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KubeFlow on Azure

  1. 1. DevOps for Software Development
  2. 2. DevOps for Data Science
  3. 3. Ingest Transform Explore Model DeployÚ Ú Ú Ú Ú Score Visualize MeasureÚ Ú ÚÚ Model Score ƒ(x) Preparation Modeling Operationalization https://inseaddataanalytics.github.io/INSEADAnalytics/CRISP_DM.pdf
  4. 4. DevOps MLOps Code testing Code reproducibility App deployment Model retraining Model validation Model reproducibility Model deployment
  5. 5. ML SERVER AZURE ML STUDIO SQL Server (In-database ML) DATA SCIENCE VM BATCH AIHDINSIGHTAZURE DATABRICKSDATA LAKE ANALYTICSAZURE SQL DB (In-database ML) COGNITIVE SERVICES
  6. 6. DEMO

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