The document discusses Vertex AI pipelines for MLOps workflows. It begins with an introduction of the speaker and their background. It then discusses what MLOps is, defining three levels of automation maturity. Vertex AI is introduced as Google Cloud's managed ML platform. Pipelines are described as orchestrating the entire ML workflow through components. Custom components and conditionals allow flexibility. Pipelines improve reproducibility and sharing. Changes can trigger pipelines through services like Cloud Build, Eventarc, and Cloud Scheduler to continuously adapt models to new data.

1. Vertex AI
Pipelines for your MLOps workflows
GDG DevFest, November 2021
Márton Kodok
Google Developer Expert at REEA.net

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Vertex AI: Pipelines for your MLOps workflows @martonkodok
About me

3. 1. What is MLOps?
2. What is Vertex AI?
3. Build, train and deploy ML solutions
4. Using Pipelines throughout your ML workflow
5. Adapting to changes of data
6. Conclusions
Agenda
Vertex AI: Pipelines for your MLOps workflows @martonkodok

4. @martonkodok
What is
MLOps?
Part #1

5. “ DevOpsprinciples to MLsystems
Vertex AI: Pipelines for your MLOps workflows @martonkodok
What is MLOps?

6. Elements for ML systems
Adapted from Hidden Technical Debt in Machine Learning Systems. @martonkodok

7. “Continuousdelivery and automationpipelines
for machinelearning systems.
Vertex AI: Pipelines for your MLOps workflows @martonkodok
What is MLOps?

8. MLOps level 0: Manual process
MLOps level 1: ML pipeline automation
MLOps level 2: CI/CD pipeline automation
Levelsofautomation defines maturity of theMLprocess
@martonkodok

9. MLOps level 0: Manual process - Process for building and deploying ML models is entirely manual.
Infrequent release iterations. No CI, No CD. Disconnection between ML and operations.
MLOps level 1: ML pipeline automation - Continuous training of the model by automating the ML pipeline;
achieve continuous delivery of model prediction service. New pipelines mostly based on new data.
MLOps level 2: CI/CD pipeline automation -iteratively try out new ML algorithms and new modeling where
the experiment steps are orchestrated. The output of this stage is the source code of the ML pipeline steps
that are then pushed to a source repository. Build source. Run test. Output is pipeline.
Levelsofautomation defines maturity of theMLprocess
@martonkodok

10. MLOps level 2: CI/CDpipelineautomation
@martonkodok

11. Levelsofautomation defines maturity of theMLprocess

12. @martonkodok
What is
Vertex AI?
Part #2

13. “VertexAI is a managed ML platform for practitioners
to accelerate experiments and deploy AI models.
Vertex AI: Pipelines for your MLOps workflows @martonkodok

14. What’s included in VertexAI?
Vertex AI: Pipelines for your MLOps workflows @martonkodok
Data Labeling
AutoML
DL Environment (DL VM + DL Container)
Prediction
Feature Store Training
Experiments
Data Readiness
Feature
Engineering
Training/
HP-Tuning
Model
Monitoring
Model serving
Understanding/
Tuning
Edge
Model
Management
Notebooks
Pipelines (Orchestration)
Explainable AI
Hybrid AI
Model
Monitoring
Metadata
Vision Translation Tables
Language
Video
AI Accelerators
Models
Datasets
Custom Models
Containers
Python
Endpoints
Vizier Optimization

15. VertexAI is a unified MLOps platform
Vertex AI: Pipelines for your MLOps workflows @martonkodok
Operational
Model
Programming
Model
No Infra Management Managed Security Pay only for usage
Model-as-a-service
oriented
Streamlined model
development
Open SDKs,
integrates with ML frameworks

16. Using Pipelines
throughout your
ML workflow
Part #3

17. VertexAI: Pipelines - Orchestrate your ML workflow
Vertex AI: Pipelines for your MLOps workflows @martonkodok
Data Labeling
AutoML
DL Environment (DL VM + DL Container)
Prediction
Feature Store Training
Experiments
Data Readiness
Feature
Engineering
Training/
HP-Tuning
Model
Monitoring
Model serving
Understanding/
Tuning
Edge
Model
Management
Notebooks
Pipelines (Orchestration)
Explainable AI
Hybrid AI
Model
Monitoring
Metadata
Vision Translation Tables
Language
Video
AI Accelerators
Models
Datasets
Custom Models
Containers
Python
Endpoints
Vizier Optimization

18. “ Why are MLpipelines useful?
Vertex AI: Pipelines for your MLOps workflows @martonkodok

19. 1. Orchestrate ML workflow steps as a process.
We no longer handle all data gathering, model training, tuning, evaluation, deployment as a monolith.
2. Adopt MLOps for production models. We need a repeatable, verifiable, and automatic process for
making any change to a production model.
3. Develop steps independently -as you scale out, enables you to share your ML workflow with others on
your team, so they can run it, and contribute code. Enablesyoutotracktheinputandoutputfromeach
stepinareproducibleway.
Why are ML pipelines useful?
@martonkodok

20. Vertex AI: Pipelines
Vertex AI: Pipelines for your MLOps workflows
Source: Piero Esposito
https://github.com/piEsposito/vertex-ai-tutorials

21. Using Pipelines throughout your ML workflow
Vertex AI: Pipelines for your MLOps workflows @martonkodok
Gather data Train model
Deploy
model

22. Pipeline Components
Vertex AI: Pipelines for your MLOps workflows
pipeline_components_automl_images.ipynb
github.com/GoogleCloudPlatform/vertex-ai-samples

23. Using Pipelines throughout your ML workflow
Vertex AI: Pipelines for your MLOps workflows @martonkodok
Gather data Train model
Evaluate
model
Scalably
deploy
model

24. Pipeline SDK: Condition
Vertex AI: Pipelines for your MLOps workflows
automl_tabular_classification_beans.ipynb
github.com/GoogleCloudPlatform/vertex-ai-samples

25. 1. Use of the Google Cloud Pipeline Components, which support easy access to Vertex AI services
2. Custom Components - function that compiles to a task ‘factory’ function that can be used by pipelines
3. No more Kubeflow Pipelines that must be deployed on a Kubernetes Cluster.
4. Sharing component specifications - the YAML format allows the component to be put under version
control and shared with others, or be used by other pipelines by calling the load_from_url function.
5. Leveraging Pipeline step caching to develop and debug
6. Vertex AI Metadata service + Artifacts Lineage tracking - inverse of pipeline DAG
Developer friendly components
@martonkodok

26. Recap
Vertex AI: Pipelines for your MLOps workflows @martonkodok
Data Labeling
AutoML
DL Environment (DL VM + DL Container)
Prediction
Feature Store Training
Experiments
Data Readiness
Feature
Engineering
Training/
HP-Tuning
Model
Monitoring
Model serving
Understanding/
Tuning
Edge
Model
Management
Notebooks
Pipelines (Orchestration)
Explainable AI
Hybrid AI
Model
Monitoring
Metadata
Vision Translation Tables
Language
Video
AI Accelerators
Models
Datasets
Custom Models
Containers
Python
Endpoints
Vizier Optimization

27. Part #4
Adapting to
changes of data

28. Automatic CI / CD Perspective with GCP Services
Vertex AI: Pipelines for your MLOps workflows @martonkodok
Eventarc
• Detect changes on data
• React to events from Cloud services
• Handle events on Cloud Workflows,
Cloud Functions, Cloud Run
• Reuse pipeline spec.json from GCS
• Trigger Vertex AI pipeline
• Detect changes in codebase
• Build pipeline
• Pipeline spec.json to Cloud Storage
• Image to Cloud Registry
• Trigger Vertex AI pipeline
Cloud Build
Cloud Scheduler
• Poll for changes of any data
• Launch based on schedule
• In tandem with Cloud Workflows
• Trigger Vertex AI pipeline

29. Conclusion
Vertex AI: Pipelines for your MLOps workflows @martonkodok

30. 1. Build with the groundbreaking ML tools that power Google
2. Approachable from the non-ML developer perspective (AutoML, managed models, training)
3. Ease the life of a data scientist/ML (has feature store, managed datasets, endpoints, notebooks)
4. End-to-end integration for data and AI with build pipelines that outperform and solve complex ML tasks
5. GitOps-style continuous delivery with Cloud Build
6. Explainable AI and TensorBoard to visualize and track ML experiments
Vertex AI: Enhanced developer experience
Vertex AI: Pipelines for your MLOps workflows @martonkodok

31. Thank you. Q&A.
Slides available on:
slideshare.net/martonkodok
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