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Vertex AI
Unified ML Platform for the entire
AI workflow on Google Cloud
DevFest Season, November 2021
Márton Kodok
Google Developer Expert at REEA.net
● Among the Top3 romanians on Stackoverflow 195k reputation
● Google Developer Expert on Cloud technologies
● Crafting Web/Mobile backends at REEA.net
● BigQuery + Redis database engine expert
Slideshare: martonkodok
Articles: martonkodok.medium.com
Twitter: @martonkodok
StackOverflow: pentium10
GitHub: pentium10
Vertex AI - Unified ML Platform @martonkodok
About me
1. What is Vertex AI
2. Gather, Import & label datasets
3. Build, train and deploy ML solutions
4. Manage your models with confidence
5. Using Pipelines throughout your ML workflow
6. Adapting to changes of data
7. Conclusions
Agenda
Vertex AI - Unified ML Platform @martonkodok
“VertexAI is a managed ML platform for practitioners
to accelerate experiments and deploy AI models.
Vertex AI - Unified ML Platform @martonkodok
Where does VertexAI fit in?
Application Servers
Vertex AI
Desktop client
Mobile client
End-to-end platform for
ML model development
and deployment
Backend
Vertex AI - Unified ML Platform @martonkodok
Application Logic
VertexAI is a unified MLOps platform
Vertex AI - Unified ML Platform @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
What’s included in VertexAI?
Vertex AI - Unified ML Platform @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
VertexAI supports...
Vertex AI - Unified ML Platform @martonkodok
UI based model development
# Define job
job = aiplatform.AutoMLTabularTrainingJob(
display_name='price-predict-training',
optimization_prediction_type='regression'
)
# Run job
model = job.run(
dataset=ds,
target_column='median_house_value',
model_display_name='house-value-prediction',
)
Code-based model development
Using Vertex AI throughout your ML workflow
Vertex AI - Unified ML Platform @martonkodok
Gather data Train model
Scalably
deploy
model
Evaluate,
monitor,
retrain
@martonkodok
Gather,
Import & label
datasets at
scale
Part #1
VertexAI: Gather, Import & label datasets at scale
Vertex AI - Unified ML Platform @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
“ Datasets
Vertex AI - Unified ML Platform @martonkodok
Datasets
Vertex AI - Unified ML Platform @martonkodok
Vertex AI datasets (fully managed)
• Fully serverless
• Region based
• Free to store
• In tandem with AutoML managed models
Custom Datasets
Cloud Storage, BigQuery or on Internet
Accessing managed dataset from your app:
- JSONL (default)
- CSV or BigQuery stream
“ VertexAI Managed Datasets + Objectives
(AutoML*)
Vertex AI - Unified ML Platform @martonkodok
* legacy name, previous generation naming from AI Platform
- Regression/classification
- Forecasting
- Single-label classification
- Multi-label classification
- Text entity extraction
- Text sentiment analysis
- Video action recognition
- Video classifications
for entire video, shots, frames
- Video object tracking
Vertex AI: Managed datasets + objectives
Vertex AI - Unified ML Platform @martonkodok
Image Tabular Text Video
- Single-label classification
- Multi-label classification
- Image object detection
- Image segmentation
Vertex AI: Managed dataset + objectives
Vertex AI - Unified ML Platform @martonkodok
Image
Vertex AI: Managed dataset + objectives
Vertex AI - Unified ML Platform @martonkodok
Tabular
Vertex AI: Managed dataset + objectives
Vertex AI - Unified ML Platform @martonkodok
Text
Vertex AI: Managed dataset + objectives
Vertex AI - Unified ML Platform @martonkodok
Video
Data labeling + Feature Store
Vertex AI - Unified ML Platform @martonkodok
Data labeling (fully managed)
• Create data labeling, annotation tasks
• Use human labelers
• Use Google’s labeler workforce
• Use your own workforce
Feature Store (fully managed)
• Centralized repository for organizing, storing,
and serving ML features
• Organization can efficiently share, discover,
re-use features
• Data Model: Entity Type -> Feature
• Ingest data from BigQuery or Cloud Storage
Pro: point-in-time lookup from time series
@martonkodok
Build,
train & deploy
models at
scale
Part #2
2. Train models
Vertex AI - Unified ML Platform @martonkodok
Gather data Train model
Scalably
deploy
model
Evaluate,
monitor,
retrain
VertexAI: Build, train & deploy models at scale
Vertex AI - Unified ML Platform @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
1. AutoML out-of-the box training integration
No-code solution. You must target one of the AutoML’s predefined objectives.
2. Custom Training - run your own training applications in the cloud
Train with one of the Google’s pre-builtcontainers or useyourown.
3. Hyperparameter tuning jobs - searchesforbestcombination of hyperparameter values by optimizing
values across a series of trials. Available for custom training. Your training app must adhere to
accepting Vertex AI parameters. You need to report metrics to Vertex AI.
Training
https://cloud.google.com/vertex-ai/docs/training/using-hyperparameter-tuning @martonkodok
Pre-built containers for custom training
https://cloud.google.com/vertex-ai/docs/training/pre-built-containers @martonkodok
Tensorflow
ML Framework version 1.15, 2.1-2.4
use with Cuda 11.x GPU
scikit-learn
ML Framework version 0.23
No GPUs
PyTorch
ML Framework version 1.4 - 1.7
use with Cuda 11.x GPU
XGBoost
ML Framework version 1.1
No GPUs
3. Deploying models
Vertex AI - Unified ML Platform @martonkodok
Gather data Train model
Scalably
deploy
model
Evaluate,
monitor,
retrain
“ You can deploy models on VertexAI
and get a HTTPs Endpointto serve predictions
rapidly and reliably.
Vertex AI - Unified ML Platform @martonkodok
1. Deploy a model and get aREST endpointto serve predictions realtime or batched
2. You can use models whetherornotthemodelwastrained on Vertex AI.
3. Specify a prediction traffic split in your endpoint.
4. VPC Private Network option for custom-trained models/tabular models
Vertex AI: Endpoints
Vertex AI - Unified ML Platform @martonkodok
Vertex AI Endpoints Backend Prediction
deploy REST
Manage your
models with
confidence
Part #3
VertexAI: Manage your models with confidence
Vertex AI - Unified ML Platform @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
VertexAI: Manage your models with confidence
Vertex AI - Unified ML Platform @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
Explainable AI
Vertex AI - Unified ML Platform @martonkodok
Explainable AI
• Interpret predictions made by ML models
• Receive a score explaining how much each
factor contributed to the model predictions
• What-If Tool lets you investigate model
behavior at a glance
AI Explanations samples
Github: GoogleCloudPlatform/ai-platform-samples
- Training, deploying, and explaining a tabular data model
- Training, deploying, and explaining an image model
Limitations: doesn’t work well on low-contrast, X-rays, one shade,
panoramas, very tall, very wide images.
Explainable AI: What-if Tool (pair-code.github.io/what-if-tool)
Vertex AI - Unified ML Platform @martonkodok
What-if Tool
• Model probing, from within any workflow
• test performance in hypothetical situations
• analyze the importance of different data
features
• visualize model behavior across multiple
models and subsets of data
Tutorials, demos onpair-code.github.io/what-if-tool
• Available on many platforms (TensorBoard, Jupyter,
Colaboratory, Vertex AI)
• Supports what-if Analyses (explore counterfactuals, fairness
measures, partial dependence plots)
• Visualizes Model Performances (threshold simulation, up to 2
model comparison, dataset summary statistics)
Part #4
Using Pipelines
throughout your
ML workflow
VertexAI: Pipelines - Orchestrate your model
Vertex AI - Unified ML Platform @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
“ Why are MLpipelines useful?
Vertex AI: Pipelines for your MLOps workflows @martonkodok
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
“Continuousdelivery and automationpipelines
for machinelearning systems.
Vertex AI: Pipelines for your MLOps workflows @martonkodok
What is MLOps?
MLOps level 0: Manual process
MLOps level 1: ML pipeline automation
MLOps level 2: CI/CD pipeline automation
Levelsofautomation defines maturity of theMLprocess
@martonkodok
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
MLOps level 2: CI/CDpipelineautomation
@martonkodok
Levelsofautomation defines maturity of theMLprocess
Vertex AI: Pipelines
Vertex AI - Unified ML Platform
Source: Piero Esposito
https://github.com/piEsposito/vertex-ai-tutorials
Pipeline SDK: Condition
Vertex AI: Pipelines for your MLOps workflows
automl_tabular_classification_beans.ipynb
github.com/GoogleCloudPlatform/vertex-ai-samples
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
Part #5
Adapting to
changes of data
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
Conclusion
Vertex AI - Unified ML Platform @martonkodok
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
Thank you.
Slides available on:
slideshare.net/martonkodok
Reea.net - Integrated web solutions driven by creativity
to deliver projects.

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Vertex AI - Unified ML Platform for the entire AI workflow on Google Cloud

  • 1. Vertex AI Unified ML Platform for the entire AI workflow on Google Cloud DevFest Season, November 2021 Márton Kodok Google Developer Expert at REEA.net
  • 2. ● Among the Top3 romanians on Stackoverflow 195k reputation ● Google Developer Expert on Cloud technologies ● Crafting Web/Mobile backends at REEA.net ● BigQuery + Redis database engine expert Slideshare: martonkodok Articles: martonkodok.medium.com Twitter: @martonkodok StackOverflow: pentium10 GitHub: pentium10 Vertex AI - Unified ML Platform @martonkodok About me
  • 3. 1. What is Vertex AI 2. Gather, Import & label datasets 3. Build, train and deploy ML solutions 4. Manage your models with confidence 5. Using Pipelines throughout your ML workflow 6. Adapting to changes of data 7. Conclusions Agenda Vertex AI - Unified ML Platform @martonkodok
  • 4. “VertexAI is a managed ML platform for practitioners to accelerate experiments and deploy AI models. Vertex AI - Unified ML Platform @martonkodok
  • 5. Where does VertexAI fit in? Application Servers Vertex AI Desktop client Mobile client End-to-end platform for ML model development and deployment Backend Vertex AI - Unified ML Platform @martonkodok Application Logic
  • 6. VertexAI is a unified MLOps platform Vertex AI - Unified ML Platform @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
  • 7. What’s included in VertexAI? Vertex AI - Unified ML Platform @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
  • 8. VertexAI supports... Vertex AI - Unified ML Platform @martonkodok UI based model development # Define job job = aiplatform.AutoMLTabularTrainingJob( display_name='price-predict-training', optimization_prediction_type='regression' ) # Run job model = job.run( dataset=ds, target_column='median_house_value', model_display_name='house-value-prediction', ) Code-based model development
  • 9. Using Vertex AI throughout your ML workflow Vertex AI - Unified ML Platform @martonkodok Gather data Train model Scalably deploy model Evaluate, monitor, retrain
  • 11. VertexAI: Gather, Import & label datasets at scale Vertex AI - Unified ML Platform @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
  • 12. “ Datasets Vertex AI - Unified ML Platform @martonkodok
  • 13. Datasets Vertex AI - Unified ML Platform @martonkodok Vertex AI datasets (fully managed) • Fully serverless • Region based • Free to store • In tandem with AutoML managed models Custom Datasets Cloud Storage, BigQuery or on Internet Accessing managed dataset from your app: - JSONL (default) - CSV or BigQuery stream
  • 14. “ VertexAI Managed Datasets + Objectives (AutoML*) Vertex AI - Unified ML Platform @martonkodok * legacy name, previous generation naming from AI Platform
  • 15. - Regression/classification - Forecasting - Single-label classification - Multi-label classification - Text entity extraction - Text sentiment analysis - Video action recognition - Video classifications for entire video, shots, frames - Video object tracking Vertex AI: Managed datasets + objectives Vertex AI - Unified ML Platform @martonkodok Image Tabular Text Video - Single-label classification - Multi-label classification - Image object detection - Image segmentation
  • 16. Vertex AI: Managed dataset + objectives Vertex AI - Unified ML Platform @martonkodok Image
  • 17. Vertex AI: Managed dataset + objectives Vertex AI - Unified ML Platform @martonkodok Tabular
  • 18. Vertex AI: Managed dataset + objectives Vertex AI - Unified ML Platform @martonkodok Text
  • 19. Vertex AI: Managed dataset + objectives Vertex AI - Unified ML Platform @martonkodok Video
  • 20. Data labeling + Feature Store Vertex AI - Unified ML Platform @martonkodok Data labeling (fully managed) • Create data labeling, annotation tasks • Use human labelers • Use Google’s labeler workforce • Use your own workforce Feature Store (fully managed) • Centralized repository for organizing, storing, and serving ML features • Organization can efficiently share, discover, re-use features • Data Model: Entity Type -> Feature • Ingest data from BigQuery or Cloud Storage Pro: point-in-time lookup from time series
  • 22. 2. Train models Vertex AI - Unified ML Platform @martonkodok Gather data Train model Scalably deploy model Evaluate, monitor, retrain
  • 23. VertexAI: Build, train & deploy models at scale Vertex AI - Unified ML Platform @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
  • 24. 1. AutoML out-of-the box training integration No-code solution. You must target one of the AutoML’s predefined objectives. 2. Custom Training - run your own training applications in the cloud Train with one of the Google’s pre-builtcontainers or useyourown. 3. Hyperparameter tuning jobs - searchesforbestcombination of hyperparameter values by optimizing values across a series of trials. Available for custom training. Your training app must adhere to accepting Vertex AI parameters. You need to report metrics to Vertex AI. Training https://cloud.google.com/vertex-ai/docs/training/using-hyperparameter-tuning @martonkodok
  • 25. Pre-built containers for custom training https://cloud.google.com/vertex-ai/docs/training/pre-built-containers @martonkodok Tensorflow ML Framework version 1.15, 2.1-2.4 use with Cuda 11.x GPU scikit-learn ML Framework version 0.23 No GPUs PyTorch ML Framework version 1.4 - 1.7 use with Cuda 11.x GPU XGBoost ML Framework version 1.1 No GPUs
  • 26. 3. Deploying models Vertex AI - Unified ML Platform @martonkodok Gather data Train model Scalably deploy model Evaluate, monitor, retrain
  • 27. “ You can deploy models on VertexAI and get a HTTPs Endpointto serve predictions rapidly and reliably. Vertex AI - Unified ML Platform @martonkodok
  • 28. 1. Deploy a model and get aREST endpointto serve predictions realtime or batched 2. You can use models whetherornotthemodelwastrained on Vertex AI. 3. Specify a prediction traffic split in your endpoint. 4. VPC Private Network option for custom-trained models/tabular models Vertex AI: Endpoints Vertex AI - Unified ML Platform @martonkodok Vertex AI Endpoints Backend Prediction deploy REST
  • 30. VertexAI: Manage your models with confidence Vertex AI - Unified ML Platform @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
  • 31. VertexAI: Manage your models with confidence Vertex AI - Unified ML Platform @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
  • 32. Explainable AI Vertex AI - Unified ML Platform @martonkodok Explainable AI • Interpret predictions made by ML models • Receive a score explaining how much each factor contributed to the model predictions • What-If Tool lets you investigate model behavior at a glance AI Explanations samples Github: GoogleCloudPlatform/ai-platform-samples - Training, deploying, and explaining a tabular data model - Training, deploying, and explaining an image model Limitations: doesn’t work well on low-contrast, X-rays, one shade, panoramas, very tall, very wide images.
  • 33. Explainable AI: What-if Tool (pair-code.github.io/what-if-tool) Vertex AI - Unified ML Platform @martonkodok What-if Tool • Model probing, from within any workflow • test performance in hypothetical situations • analyze the importance of different data features • visualize model behavior across multiple models and subsets of data Tutorials, demos onpair-code.github.io/what-if-tool • Available on many platforms (TensorBoard, Jupyter, Colaboratory, Vertex AI) • Supports what-if Analyses (explore counterfactuals, fairness measures, partial dependence plots) • Visualizes Model Performances (threshold simulation, up to 2 model comparison, dataset summary statistics)
  • 35. VertexAI: Pipelines - Orchestrate your model Vertex AI - Unified ML Platform @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
  • 36. “ Why are MLpipelines useful? Vertex AI: Pipelines for your MLOps workflows @martonkodok
  • 37. 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
  • 38. “Continuousdelivery and automationpipelines for machinelearning systems. Vertex AI: Pipelines for your MLOps workflows @martonkodok What is MLOps?
  • 39. MLOps level 0: Manual process MLOps level 1: ML pipeline automation MLOps level 2: CI/CD pipeline automation Levelsofautomation defines maturity of theMLprocess @martonkodok
  • 40. 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
  • 41. MLOps level 2: CI/CDpipelineautomation @martonkodok
  • 43. Vertex AI: Pipelines Vertex AI - Unified ML Platform Source: Piero Esposito https://github.com/piEsposito/vertex-ai-tutorials
  • 44. Pipeline SDK: Condition Vertex AI: Pipelines for your MLOps workflows automl_tabular_classification_beans.ipynb github.com/GoogleCloudPlatform/vertex-ai-samples
  • 45. 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
  • 47. 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
  • 48. Conclusion Vertex AI - Unified ML Platform @martonkodok
  • 49. 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
  • 50. Thank you. Slides available on: slideshare.net/martonkodok Reea.net - Integrated web solutions driven by creativity to deliver projects.