Machine Learning Operations & Azure
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The document outlines a presentation from the Erlangen Artificial Intelligence & Machine Learning Meetup by Axel Dittmann, covering concepts in AI, machine learning, and MLOps. It emphasizes the importance of model validation, automation, collaboration between app developers and data scientists, and the use of Azure DevOps for building, deploying, and monitoring machine learning applications. Key benefits of MLOps include reproducibility, verifiability, and the ability to manage and monitor model performance effectively.
![App Developer
Cloud Services
IDE
Data Scientist
[ { “dog": 0.99218,
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IDE
Apps
Edge Devices
Model Store
Consume Model
DevOps
Pipeline
Customize Model
Deploy Model
Predict
Validate&Flight
Model+App
Update
Application
Publish Model
Collect
Feedback
Deploy
Application
Model
Telemetry
Retrain Model
ML + App Dev Process](https://image.slidesharecdn.com/admlopsandazure-191014094357/85/Machine-Learning-Operations-Azure-18-320.jpg)
Machine Learning Operations & Azure
- 1. Erlangen Artificial Intelligence & Machine Learning Meetup presents
- 2. Axel Dittmann Diplom-Betriebswirt (FH) Diplom-Wirtschaftsinformatiker (FH) Global Technical Specialist IOT CISSP Axel.Dittmann@Microsoft.com Twitter: @DittmannAxel
- 3. Robotics Virtual Reality Internet of Things Artificial Intelligence Sensors feeling and observing the environment – providing Data Reinforcement Learning Generate insights Process Data React based on generated insight Human computer interaction
- 4. ML / AI is HARD!
- 6. Building a model Data ingestion Data analysis Data transformation Data validation Data splitting Trainer Model validation Training at scale LoggingRoll-out Serving Monitoring
- 7. GitOps = Git + Dev + Ops
- 8. GitOps == VELOCITY and SECURITY
- 9. MLOps!
- 10. MLOps = ML + DEV + OPS Experiment Data Acquisition Business Understanding Initial Modeling Develop Modeling Operate Continuous Delivery Data Feedback Loop System + Model Monitoring ML + Testing Continuous Integration Continuous Deployment
- 11. MLOps Benefits • Code drives generation and deployments • Pipelines are reproducible and verifiable • All artifacts can be tagged and audited • SWE best practices for quality control • Offline comparisons of model quality • Minimize bias and enable explainability • Controlled rollout capabilities • Live comparison of predicted vs. expected performance • Results fed back to watch for drift and improve model Automation / Observability Validation Reproducibility /Auditability == VELOCITY and SECURITY (For ML)
- 12. App developer using Azure DevOps MLOps Workflow Build appCollaborate Test app Release app Monitor app Model reproducibility Model retrainingModel deploymentModel validation Data scientist using Azure Machine Learning
- 13. Code, dataset, and environment versioning Model reproducibility Model retrainingModel deploymentModel validation Build appCollaborate Test app Release app Monitor app App developer using Azure DevOps Data scientist using Azure Machine Learning MLOps Workflow
- 14. MLOps Workflow Model reproducibility Model retrainingModel deploymentModel validation Automated ML ML Pipelines Hyperparameter tuning Train model Build appCollaborate Test app Release app Monitor app App developer using Azure DevOps Data scientist using Azure Machine Learning
- 15. MLOps Workflow Model validation & certification Model reproducibility Model retrainingModel deploymentModel validation Train model Validate model Build appCollaborate Test app Release app Monitor app App developer using Azure DevOps Data scientist using Azure Machine Learning
- 16. MLOps Workflow Model packaging Simple deployment Model reproducibility Model retrainingModel deploymentModel validation Train model Validate model Deploy model Build appCollaborate Test app Release app Monitor app App developer using Azure DevOps Data scientist using Azure Machine Learning
- 17. MLOps Workflow Model management & monitoring Model performance analysis Model reproducibility Model retrainingModel deploymentModel validation Train model Validate model Deploy model Monitor model Retrain model Build appCollaborate Test app Release app Monitor app App developer using Azure DevOps Data scientist using Azure Machine Learning
- 18. App Developer Cloud Services IDE Data Scientist [ { “dog": 0.99218, “wuffwuff": 0.81242 }] IDE Apps Edge Devices Model Store Consume Model DevOps Pipeline Customize Model Deploy Model Predict Validate&Flight Model+App Update Application Publish Model Collect Feedback Deploy Application Model Telemetry Retrain Model ML + App Dev Process
- 19. Azure DevOps Pipelines Cloud-hosted pipelines for Linux, Windows and macOS. Any language, any platform, any cloud Build, test, and deploy Node.js, Python, Java, PHP, Ruby, C/C++, .NET, Android, and iOS apps. Run in parallel on Linux, macOS, and Windows. Deploy to Azure, AWS, GCP or on-premises Extensible Explore and implement a wide range of community- built build, test, and deployment tasks, along with hundreds of extensions from Slack to SonarCloud. Support for YAML, reporting and more Containers and Kubernetes Easily build and push images to container registries like Docker Hub and Azure Container Registry. Deploy containers to individual hosts or Kubernetes.
- 20. Deploy your ML Model
- 21. Data Localitypolicies Store data in the local Data Centre Process data locally Challenge! Worldwide distributed Machine Learning training Idea! Move the Model leave the data in its location Azure DevOps Azure IoT Hub Azure Machine Learning Service Azure services for support Dr. Lydia Nemec https://www.linkedin.com/in/lydianemec/ @LydiaNemec https://github.com/DittmannAxel/AI_IOT_Summit_Sept19
- 22. Thank You
- 23. To learn more about the meetup, click the Link https://www.meetup.com/Erlangen-Artificial-Intelligence-Machine-Learning-Meetup Erlangen Artificial Intelligence & Machine Learning Meetup