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How to move your ML system from local to production - 2024-03-15

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Alessandra Bilardi
Alessandra Bilardi

Incontro DevOps Italia 2024 When a Cloud Engineer has to do a review the code of its colleague Data Scientist for production environment, it is always important to understand where it is best to put the focus. Often, the best approach is to promote the resources awareness to be used and to find a framework to split the work together. https://2024.incontrodevops.it/talks_speakers/

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DevOps & MLOps - IDI 2024 Come portare in produzione un sistema di ML che ora gira solo nel locale del nostro data scientist ?
DevOps & MLOps - IDI 2024 Alessandra Bilardi Data & Automation Specialist @ Corley Cloud alessandra.bilardi@corley.it corley.it
DevOps & MLOps - IDI 2024 Alessandra Bilardi Data & Automation Specialist @ Corley Cloud alessandra.bilardi@corley.it corley.it
DevOps & MLOps - IDI 2024 Alessandra Bilardi Data & Automation Specialist @ Corley Cloud alessandra.bilardi@corley.it corley.it
DevOps & MLOps - IDI 2024 SUMMARY DevOps methods & actors MLOps methods & actors Use case AWS services What we take home
DevOps & MLOps - IDI 2024 reference
DevOps & MLOps - IDI 2024 DevOps methods & actors
DevOps & MLOps - IDI 2024 DevOps methods & actors
DevOps & MLOps - IDI 2024 DevOps methods & actors
DevOps & MLOps - IDI 2024 MLOps methods & actors
DevOps & MLOps - IDI 2024 ML system & actors
DevOps & MLOps - IDI 2024 reference
DevOps & MLOps - IDI 2024 reference
DevOps & MLOps - IDI 2024 ML system & actors
DevOps & MLOps - IDI 2024 MLOps methods & actors
DevOps & MLOps - IDI 2024 MLOps methods & actors
DevOps & MLOps - IDI 2024 reference
DevOps & MLOps - IDI 2024 Use case
DevOps & MLOps - IDI 2024 Use case
DevOps & MLOps - IDI 2024 Use case
DevOps & MLOps - IDI 2024 reference
DevOps & MLOps - IDI 2024
DevOps & MLOps - IDI 2024 AWS services
DevOps & MLOps - IDI 2024 AWS services
DevOps & MLOps - IDI 2024 AWS Step Functions
DevOps & MLOps - IDI 2024 AWS Step Functions https://github.com/aws/aws-step-functions-data-science-sdk-python
DevOps & MLOps - IDI 2024 AWS services
DevOps & MLOps - IDI 2024 AWS services
DevOps & MLOps - IDI 2024 Amazon SageMaker Pipeline
DevOps & MLOps - IDI 2024 Amazon SageMaker Pipeline https://github.com/aws/amazon-sagemaker-examples/tree/main/sagemaker-pipeline-parameterization
DevOps & MLOps - IDI 2024 reference
DevOps & MLOps - IDI 2024 What we take home
DevOps & MLOps - IDI 2024 Data Scientist & Cloud Engineer 1 How to survive with colleagues with different skills, without hindering the table football game during the lunch break. 2 Versioning of configuration, training and inference data statistics, models, .. 3 Define tools and services: libraries, images, storages, .. 4 Define guidelines for who does what: review, support, deploying the model, .. Promoting resources awareness to be used
DevOps & MLOps - IDI 2024 AWS services
DevOps & MLOps - IDI 2024 Step Functions vs SageMaker Pipeline
DevOps & MLOps - IDI 2024 AWS services Which AWS services for which role, when we want to automate a solution from local to production: depends on the life cycle of the model but above all by the actors involved. Data Scientists (DS) Cloud Engineer (CE) DS AWS Services CE AWS Services many one AWS Step Functions AWS CloudFormation one many Amazon SageMaker Pipeline AWS CloudFormation & Step Functions
DevOps & MLOps - IDI 2024 AWS services Which AWS services for which model, when we have a small model, we can also use something like AWS Lambda, but when we need GPU, we must to use an Endpoint. AWS Service Provisioning type Resources Cost per hour Endpoint Provisioned 2 CPU + 4GB RAM $0.056 Endpoint Serverless 4096MB $0.288 AWS Lambda Serverless 4096MB $0.00024012 +GBsec
DevOps & MLOps - IDI 2024 Thanks for listening!

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How to move your ML system from local to production - 2024-03-15

  • 1. DevOps & MLOps - IDI 2024 Come portare in produzione un sistema di ML che ora gira solo nel locale del nostro data scientist ?
  • 2. DevOps & MLOps - IDI 2024 Alessandra Bilardi Data & Automation Specialist @ Corley Cloud alessandra.bilardi@corley.it corley.it
  • 3. DevOps & MLOps - IDI 2024 Alessandra Bilardi Data & Automation Specialist @ Corley Cloud alessandra.bilardi@corley.it corley.it
  • 4. DevOps & MLOps - IDI 2024 Alessandra Bilardi Data & Automation Specialist @ Corley Cloud alessandra.bilardi@corley.it corley.it
  • 5. DevOps & MLOps - IDI 2024 SUMMARY DevOps methods & actors MLOps methods & actors Use case AWS services What we take home
  • 6. DevOps & MLOps - IDI 2024 reference
  • 7. DevOps & MLOps - IDI 2024 DevOps methods & actors
  • 8. DevOps & MLOps - IDI 2024 DevOps methods & actors
  • 9. DevOps & MLOps - IDI 2024 DevOps methods & actors
  • 10. DevOps & MLOps - IDI 2024 MLOps methods & actors
  • 11. DevOps & MLOps - IDI 2024 ML system & actors
  • 12. DevOps & MLOps - IDI 2024 reference
  • 13. DevOps & MLOps - IDI 2024 reference
  • 14. DevOps & MLOps - IDI 2024 ML system & actors
  • 15. DevOps & MLOps - IDI 2024 MLOps methods & actors
  • 16. DevOps & MLOps - IDI 2024 MLOps methods & actors
  • 17. DevOps & MLOps - IDI 2024 reference
  • 18. DevOps & MLOps - IDI 2024 Use case
  • 19. DevOps & MLOps - IDI 2024 Use case
  • 20. DevOps & MLOps - IDI 2024 Use case
  • 21. DevOps & MLOps - IDI 2024 reference
  • 22. DevOps & MLOps - IDI 2024
  • 23. DevOps & MLOps - IDI 2024 AWS services
  • 24. DevOps & MLOps - IDI 2024 AWS services
  • 25. DevOps & MLOps - IDI 2024 AWS Step Functions
  • 26. DevOps & MLOps - IDI 2024 AWS Step Functions https://github.com/aws/aws-step-functions-data-science-sdk-python
  • 27. DevOps & MLOps - IDI 2024 AWS services
  • 28. DevOps & MLOps - IDI 2024 AWS services
  • 29. DevOps & MLOps - IDI 2024 Amazon SageMaker Pipeline
  • 30. DevOps & MLOps - IDI 2024 Amazon SageMaker Pipeline https://github.com/aws/amazon-sagemaker-examples/tree/main/sagemaker-pipeline-parameterization
  • 31. DevOps & MLOps - IDI 2024 reference
  • 32. DevOps & MLOps - IDI 2024 What we take home
  • 33. DevOps & MLOps - IDI 2024 Data Scientist & Cloud Engineer 1 How to survive with colleagues with different skills, without hindering the table football game during the lunch break. 2 Versioning of configuration, training and inference data statistics, models, .. 3 Define tools and services: libraries, images, storages, .. 4 Define guidelines for who does what: review, support, deploying the model, .. Promoting resources awareness to be used
  • 34. DevOps & MLOps - IDI 2024 AWS services
  • 35. DevOps & MLOps - IDI 2024 Step Functions vs SageMaker Pipeline
  • 36. DevOps & MLOps - IDI 2024 AWS services Which AWS services for which role, when we want to automate a solution from local to production: depends on the life cycle of the model but above all by the actors involved. Data Scientists (DS) Cloud Engineer (CE) DS AWS Services CE AWS Services many one AWS Step Functions AWS CloudFormation one many Amazon SageMaker Pipeline AWS CloudFormation & Step Functions
  • 37. DevOps & MLOps - IDI 2024 AWS services Which AWS services for which model, when we have a small model, we can also use something like AWS Lambda, but when we need GPU, we must to use an Endpoint. AWS Service Provisioning type Resources Cost per hour Endpoint Provisioned 2 CPU + 4GB RAM $0.056 Endpoint Serverless 4096MB $0.288 AWS Lambda Serverless 4096MB $0.00024012 +GBsec
  • 38. DevOps & MLOps - IDI 2024 Thanks for listening!