Machine learning operations model book mlops
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May 12, 2024
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About This Presentation
How to implement machine learning
Slide Content
Prof. Dr. Jan Kirenz
Machine Learning Operations (MLOps)
Usage of Pipelines in the ML Lifecycle with
Tensor Flow Extended (TFX) and Kubeflow
Prof. Dr. Jan Kirenz
HdM Stuttgart
Machine Learning Operations (MLOps)
Usage of Pipelines in the ML Lifecycle with
Tensor Flow Extended (TFX) and Kubeflow
Prof. Dr. Jan Kirenz
HdM Stuttgart
Prof. Dr. Jan Kirenz
80-85% PoC Factory
The Proof of Concept Factory
Most companies...
●… conduct AI experiments and pilots
but achieve a low scaling success
rate
●… have significant under investment,
yielding low returns
Source: Accenture (2019) https://www.accenture.com/us-en/insights/artificial-intelligence/ai-investments
80-85% PoC Factory
The Proof of Concept Factory
Most companies...
●… conduct AI experiments and pilots
but achieve a low scaling success
rate
●… have significant under investment,
yielding low returns
Source: Accenture (2019) https://www.accenture.com/us-en/insights/artificial-intelligence/ai-investments
Prof. Dr. Jan Kirenzhttps://www.gartner.com/smarterwithgartner/gartner-top-10-data-and-analytics-trends-for-2021/
Scalable AI
Scalable AI
Prof. Dr. Jan Kirenz
ML Project Code
The problem with scaling AI: ML code is only a
fraction of a production-ready ML project code
ML
Code
5-10%
ML Project Code
The problem with scaling AI: ML code is only a
fraction of a production-ready ML project code
ML
Code
5-10%
Prof. Dr. Jan Kirenz
Monitoring
Hidden technical debt in machine learning systems
Sculley, D. et al. (2015). Hidden technical debt in machine learning systems. Advances in neural information processing systems, 28, pp. 2503-2511
Data Collection
Configuration
Feature Engineering
Data
Verification
Metadata Management
Model Analysis
Serving
Infra-
structure
Automation
Process Management
Machine Resource
Management
Testing and Debugging
ML
Code
Monitoring
Hidden technical debt in machine learning systems
Sculley, D. et al. (2015). Hidden technical debt in machine learning systems. Advances in neural information processing systems, 28, pp. 2503-2511
Data Collection
Configuration
Feature Engineering
Data
Verification
Metadata Management
Model Analysis
Serving
Infra-
structure
Automation
Process Management
Machine Resource
Management
Testing and Debugging
ML
Code
Prof. Dr. Jan Kirenz
Machine learning operations (MLOps)
●ML Engineering culture and practice that
aims at unifying ML System development
(Dev) and ML system operations (Ops)
●Tools and principles to support workflow
standardization and automation through
the ML system lifecycle (e.g. with pipelines)
Machine learning operations (MLOps)
●ML Engineering culture and practice that
aims at unifying ML System development
(Dev) and ML system operations (Ops)
●Tools and principles to support workflow
standardization and automation through
the ML system lifecycle (e.g. with pipelines)
Prof. Dr. Jan Kirenz Prof. Dr. Jan Kirenz
Machine learning
lifecycle
Machine learning
lifecycle
Prof. Dr. Jan Kirenz
Plan
Model
Deployment Data
Business Analyst
Data
Engineer
Software
Developer
Data Scientist
Lifecycle
of an ML System
Plan | Data | Model | Deployment
Plan
Model
Deployment Data
Business Analyst
Data
Engineer
Software
Developer
Data Scientist
Lifecycle
of an ML System
Plan | Data | Model | Deployment
Prof. Dr. Jan Kirenz
Plan
Model
Deployment Data
Identify use
case
Frame
problem
Identify
variables
Define metrics
Business Analyst
Data
Engineer
Software
Developer
Data Scientist
Lifecycle
of an ML System
Plan | Data | Model | Deployment
Plan
Model
Deployment Data
Identify use
case
Frame
problem
Identify
variables
Define metrics
Business Analyst
Data
Engineer
Software
Developer
Data Scientist
Lifecycle
of an ML System
Plan | Data | Model | Deployment
Prof. Dr. Jan Kirenz
Plan
Model
Deployment Data
Identify use
case
Frame
problem
Identify
variables
Define metrics
Business Analyst
Data
Engineer
Software
Developer
Data Scientist
Data ingestion
Analyze &
clean data
Define schema
Feature
engineering
Data splitting
Anomaly
detection
Data preprocessing
Lifecycle
of an ML System
Plan | Data | Model | Deployment
Plan
Model
Deployment Data
Identify use
case
Frame
problem
Identify
variables
Define metrics
Business Analyst
Data
Engineer
Software
Developer
Data Scientist
Data ingestion
Analyze &
clean data
Define schema
Feature
engineering
Data splitting
Anomaly
detection
Data preprocessing
Lifecycle
of an ML System
Plan | Data | Model | Deployment
Prof. Dr. Jan Kirenz
Plan
Model
Deployment Data
Identify use
case
Frame
problem
Identify
variables
Define metrics
Business Analyst
Data
Engineer
Software
Developer
Data Scientist
Data ingestion
Analyze &
clean data
Define schema
Feature
engineering
Evaluate
model
Model Training
& tuning
Select
algorithm
Data splitting
Anomaly
detection
Data preprocessing
Lifecycle
of an ML System
Plan | Data | Model | Deployment
Plan
Model
Deployment Data
Identify use
case
Frame
problem
Identify
variables
Define metrics
Business Analyst
Data
Engineer
Software
Developer
Data Scientist
Data ingestion
Analyze &
clean data
Define schema
Feature
engineering
Evaluate
model
Model Training
& tuning
Select
algorithm
Data splitting
Anomaly
detection
Data preprocessing
Lifecycle
of an ML System
Plan | Data | Model | Deployment
Prof. Dr. Jan Kirenz
Plan
Model
Deployment Data
Identify use
case
Frame
problem
Identify
variables
Define metrics
Business Analyst
Data
Engineer
Software
Developer
Data Scientist
Data ingestion
Analyze &
clean data
Define schema
Feature
engineering
Validate model
Deploy model
Serve model
Retrain
triggers
Evaluate
model
Model Training
& tuning
Monitor model
Select
algorithm
Data splitting
Anomaly
detection
Data preprocessing
Lifecycle
of an ML System
Plan | Data | Model | Deployment
Plan
Model
Deployment Data
Identify use
case
Frame
problem
Identify
variables
Define metrics
Business Analyst
Data
Engineer
Software
Developer
Data Scientist
Data ingestion
Analyze &
clean data
Define schema
Feature
engineering
Validate model
Deploy model
Serve model
Retrain
triggers
Evaluate
model
Model Training
& tuning
Monitor model
Select
algorithm
Data splitting
Anomaly
detection
Data preprocessing
Lifecycle
of an ML System
Plan | Data | Model | Deployment
Prof. Dr. Jan Kirenz
Plan
Model
Deployment Data
Identify use
case
Frame
problem
Identify
variables
Define metrics
Business Analyst
Data
Engineer
Software
Developer
Data Scientist
Data ingestion
Analyze &
clean data
Define schema
Feature
engineering
Validate model
Deploy model
Serve model
Retrain
triggers
Evaluate
model
Model Training
& tuning
Monitor model
Select
algorithm
Data splitting
Anomaly
detection
Data preprocessing
Lifecycle
of an ML System
Plan | Data | Model | Deployment
Common issues which lead to a PoC to production gap
●Lack of reuse and duplication
●Inconsistency (data, code, models)
●Manual and slow transition from PoC to production
Plan
Model
Deployment Data
Identify use
case
Frame
problem
Identify
variables
Define metrics
Business Analyst
Data
Engineer
Software
Developer
Data Scientist
Data ingestion
Analyze &
clean data
Define schema
Feature
engineering
Validate model
Deploy model
Serve model
Retrain
triggers
Evaluate
model
Model Training
& tuning
Monitor model
Select
algorithm
Data splitting
Anomaly
detection
Data preprocessing
Lifecycle
of an ML System
Plan | Data | Model | Deployment
Common issues which lead to a PoC to production gap
●Lack of reuse and duplication
●Inconsistency (data, code, models)
●Manual and slow transition from PoC to production
Prof. Dr. Jan Kirenz
Plan
Model
Deployment Data
Identify use
case
Frame
problem
Identify
variables
Define metrics
Business Analyst
Data
Engineer
Software
Developer
Data Scientist
Data ingestion
Analyze &
clean data
Define schema
Feature
engineering
Validate model
Deploy model
Serve model
Retrain
triggers
Evaluate
model
Model Training
& tuning
Monitor model
Select
algorithm
Data splitting
Anomaly
detection
Data preprocessing
Model
management
Model registry
Data and feature
management
Feature store
Pipeline
management
Pipeline orchestration
Metadata
management
Metadata store
Lifecycle
of an ML System
Plan | Data | Model | Deployment
MLOps components
Plan
Model
Deployment Data
Identify use
case
Frame
problem
Identify
variables
Define metrics
Business Analyst
Data
Engineer
Software
Developer
Data Scientist
Data ingestion
Analyze &
clean data
Define schema
Feature
engineering
Validate model
Deploy model
Serve model
Retrain
triggers
Evaluate
model
Model Training
& tuning
Monitor model
Select
algorithm
Data splitting
Anomaly
detection
Data preprocessing
Model
management
Model registry
Data and feature
management
Feature store
Pipeline
management
Pipeline orchestration
Metadata
management
Metadata store
Lifecycle
of an ML System
Plan | Data | Model | Deployment
MLOps components
Prof. Dr. Jan Kirenz
What is a pipeline?
●Description of an ML workflow
●A pipeline component is a self-contained
set of user code that performs one step in
the pipeline
●Includes the definition of the configuration
and inputs required to run the pipeline (e.g.
model hyperparameters)
… do this
… than that
Start
...
… the end
The workflow is
also called directed
acyclic graph (DAG)
This is a component
Complete workflow of the ML
system lifecycle
What is a pipeline?
●Description of an ML workflow
●A pipeline component is a self-contained
set of user code that performs one step in
the pipeline
●Includes the definition of the configuration
and inputs required to run the pipeline (e.g.
model hyperparameters)
… do this
… than that
Start
...
… the end
The workflow is
also called directed
acyclic graph (DAG)
This is a component
Complete workflow of the ML
system lifecycle
Prof. Dr. Jan KirenzSource: Baer & Ngahane (2019)
… do this
… than that
Start
… the end
… do this
… than that
Start
… the end
Prof. Dr. Jan Kirenz
TensorFlow Extended (TFX)
●Google-production-scale machine learning
(ML) platform based on TensorFlow
●Portable to multiple environments (Azure,
AWS, Google Cloud, IBM, ...)
●Python based toolkit; can be used with
notebooks
●Helps you orchestrate your ML process:
Apache Airflow, Apache Beam or Kubeflow
pipelines
Source: TensorFlow (2021)
TensorFlow Extended (TFX)
●Google-production-scale machine learning
(ML) platform based on TensorFlow
●Portable to multiple environments (Azure,
AWS, Google Cloud, IBM, ...)
●Python based toolkit; can be used with
notebooks
●Helps you orchestrate your ML process:
Apache Airflow, Apache Beam or Kubeflow
pipelines
Source: TensorFlow (2021)
Prof. Dr. Jan Kirenz
TFX 1.0 (19.05.21)
●Enterprise-grade support
●Security patches and select bug fixes for
up to three years
●Guaranteed API & Artifact backward
compatibility
Source: Google (2021)
TFX 1.0 (19.05.21)
●Enterprise-grade support
●Security patches and select bug fixes for
up to three years
●Guaranteed API & Artifact backward
compatibility
Source: Google (2021)
Prof. Dr. Jan Kirenz
Plan
Model
Deployment Data
Identify use
case
Frame
problem
Identify
variables
Define metrics
Business Analyst
Data
Engineer
Software
Developer
Data Scientist
Data ingestion
Analyze &
clean data
Define schema
Feature
engineering
Validate model
Deploy model
Serve model
Evaluate
model
Model Training
& tuning
Monitor model
ExampleGen
Select
algorithm
StatisticsGen
SchemaGen
Example
Validator
Transform
Data splitting
Trainer
TunerEvaluator
InfraValidator
Anomaly
detection
Pusher
HUB / JS / LITE / SERVING
Model Server
TF Data Validation (TFDV)
TFT
TF
KerasTuner
TensorFlow
Model Analysis
(TFMA)
BulkInferrer
Data preprocessing
Metadata Store: ML Metadata (MLMD)
TFX Options for Pipeline
Orchestration
Plan
Model
Deployment Data
Identify use
case
Frame
problem
Identify
variables
Define metrics
Business Analyst
Data
Engineer
Software
Developer
Data Scientist
Data ingestion
Analyze &
clean data
Define schema
Feature
engineering
Validate model
Deploy model
Serve model
Evaluate
model
Model Training
& tuning
Monitor model
ExampleGen
Select
algorithm
StatisticsGen
SchemaGen
Example
Validator
Transform
Data splitting
Trainer
TunerEvaluator
InfraValidator
Anomaly
detection
Pusher
HUB / JS / LITE / SERVING
Model Server
TF Data Validation (TFDV)
TFT
TF
KerasTuner
TensorFlow
Model Analysis
(TFMA)
BulkInferrer
Data preprocessing
Metadata Store: ML Metadata (MLMD)
TFX Options for Pipeline
Orchestration
Prof. Dr. Jan Kirenz
Plan
Model
Deployment Data
Identify use
case
Frame
problem
Identify
variables
Define metrics
Business Analyst
Data
Engineer
Software
Developer
Data Scientist
Data ingestion
Analyze &
clean data
Define schema
Feature
engineering
Validate model
Deploy model
Serve model
Evaluate
model
Model Training
& tuning
Monitor model
ExampleGen
Select
algorithm
StatisticsGen
SchemaGen
Example
Validator
Transform
Data splitting
Trainer
TunerEvaluator
InfraValidator
Anomaly
detection
Pusher
HUB / JS / LITE / SERVING
Model Server
TF Data Validation (TFDV)
TFT
TF
KerasTuner
TensorFlow
Model Analysis
(TFMA)
BulkInferrer
Metadata Store (ML Metadata)
TFX Options for Pipeline
Orchestration
Data preprocessing
Plan
Model
Deployment Data
Identify use
case
Frame
problem
Identify
variables
Define metrics
Business Analyst
Data
Engineer
Software
Developer
Data Scientist
Data ingestion
Analyze &
clean data
Define schema
Feature
engineering
Validate model
Deploy model
Serve model
Evaluate
model
Model Training
& tuning
Monitor model
ExampleGen
Select
algorithm
StatisticsGen
SchemaGen
Example
Validator
Transform
Data splitting
Trainer
TunerEvaluator
InfraValidator
Anomaly
detection
Pusher
HUB / JS / LITE / SERVING
Model Server
TF Data Validation (TFDV)
TFT
TF
KerasTuner
TensorFlow
Model Analysis
(TFMA)
BulkInferrer
Metadata Store (ML Metadata)
TFX Options for Pipeline
Orchestration
Data preprocessing
Prof. Dr. Jan Kirenz
Plan
Model
Deployment Data
Identify use
case
Frame
problem
Identify
variables
Define metrics
Business Analyst
Data
Engineer
Software
Developer
Data Scientist
Data ingestion
Analyze &
clean data
Define schema
Feature
engineering
Validate model
Deploy model
Serve model
Evaluate
model
Model Training
& tuning
Monitor model
ExampleGen
Select
algorithm
StatisticsGen
SchemaGen
Example
Validator
Transform
Data splitting
Trainer
TunerEvaluator
InfraValidator
Anomaly
detection
Pusher
HUB / JS / LITE / SERVING
Model Server
TF Data Validation (TFDV)
TFT
TF
KerasTuner
TensorFlow
Model Analysis
(TFMA)
BulkInferrer
Data preprocessing
Metadata Store (ML Metadata)
TFX Options for Pipeline
Orchestration
Plan
Model
Deployment Data
Identify use
case
Frame
problem
Identify
variables
Define metrics
Business Analyst
Data
Engineer
Software
Developer
Data Scientist
Data ingestion
Analyze &
clean data
Define schema
Feature
engineering
Validate model
Deploy model
Serve model
Evaluate
model
Model Training
& tuning
Monitor model
ExampleGen
Select
algorithm
StatisticsGen
SchemaGen
Example
Validator
Transform
Data splitting
Trainer
TunerEvaluator
InfraValidator
Anomaly
detection
Pusher
HUB / JS / LITE / SERVING
Model Server
TF Data Validation (TFDV)
TFT
TF
KerasTuner
TensorFlow
Model Analysis
(TFMA)
BulkInferrer
Data preprocessing
Metadata Store (ML Metadata)
TFX Options for Pipeline
Orchestration
Prof. Dr. Jan Kirenz
Plan
Model
Deployment Data
Identify use
case
Frame
problem
Identify
variables
Define metrics
Business Analyst
Data
Engineer
Software
Developer
Data Scientist
Data ingestion
Analyze &
clean data
Define schema
Feature
engineering
Validate model
Deploy model
Serve model
Evaluate
model
Model Training
& tuning
Monitor model
ExampleGen
Select
algorithm
StatisticsGen
SchemaGen
Example
Validator
Transform
Data splitting
Trainer
TunerEvaluator
InfraValidator
Anomaly
detection
Pusher
HUB / JS / LITE / SERVING
Model Server
TF Data Validation (TFDV)
TFT
TF
TensorFlow
Model Analysis
(TFMA)
BulkInferrer
Data preprocessing
Metadata Store (ML Metadata)
TFX Options for Pipeline
Orchestration
Plan
Model
Deployment Data
Identify use
case
Frame
problem
Identify
variables
Define metrics
Business Analyst
Data
Engineer
Software
Developer
Data Scientist
Data ingestion
Analyze &
clean data
Define schema
Feature
engineering
Validate model
Deploy model
Serve model
Evaluate
model
Model Training
& tuning
Monitor model
ExampleGen
Select
algorithm
StatisticsGen
SchemaGen
Example
Validator
Transform
Data splitting
Trainer
TunerEvaluator
InfraValidator
Anomaly
detection
Pusher
HUB / JS / LITE / SERVING
Model Server
TF Data Validation (TFDV)
TFT
TF
TensorFlow
Model Analysis
(TFMA)
BulkInferrer
Data preprocessing
Metadata Store (ML Metadata)
TFX Options for Pipeline
Orchestration
Prof. Dr. Jan Kirenz
Plan
Model
Deployment Data
Identify use
case
Frame
problem
Identify
variables
Define metrics
Business Analyst
Data
Engineer
Software
Developer
Data Scientist
Data ingestion
Analyze &
clean data
Define schema
Feature
engineering
Validate model
Deploy model
Serve model
Evaluate
model
Model Training
& tuning
Monitor model
ExampleGen
Select
algorithm
StatisticsGen
SchemaGen
Example
Validator
Transform
Data splitting
Trainer
TunerEvaluator
InfraValidator
Anomaly
detection
Pusher
HUB / JS / LITE / SERVING
Model Server
TF Data Validation (TFDV)
TFT
TF
TensorFlow
Model Analysis
(TFMA)
BulkInferrer
Data preprocessing
Metadata Store (ML Metadata)
TFX Options for Pipeline
Orchestration
Plan
Model
Deployment Data
Identify use
case
Frame
problem
Identify
variables
Define metrics
Business Analyst
Data
Engineer
Software
Developer
Data Scientist
Data ingestion
Analyze &
clean data
Define schema
Feature
engineering
Validate model
Deploy model
Serve model
Evaluate
model
Model Training
& tuning
Monitor model
ExampleGen
Select
algorithm
StatisticsGen
SchemaGen
Example
Validator
Transform
Data splitting
Trainer
TunerEvaluator
InfraValidator
Anomaly
detection
Pusher
HUB / JS / LITE / SERVING
Model Server
TF Data Validation (TFDV)
TFT
TF
TensorFlow
Model Analysis
(TFMA)
BulkInferrer
Data preprocessing
Metadata Store (ML Metadata)
TFX Options for Pipeline
Orchestration
Prof. Dr. Jan Kirenz
Plan
Model
Deployment Data
Identify use
case
Frame
problem
Identify
variables
Define metrics
Business Analyst
Data
Engineer
Software
Developer
Data Scientist
Data ingestion
Analyze &
clean data
Define schema
Feature
engineering
Validate model
Deploy model
Serve model
Evaluate
model
Model Training
& tuning
Monitor model
ExampleGen
Select
algorithm
StatisticsGen
SchemaGen
Example
Validator
Transform
Data splitting
Trainer
TunerEvaluator
InfraValidator
Anomaly
detection
Pusher
HUB / JS / LITE / SERVING
Model Server
TF Data Validation (TFDV)
TFT
TF
KerasTuner
TensorFlow
Model Analysis
(TFMA)
BulkInferrer
Data preprocessing
Metadata Store (ML Metadata)
TFX Options for Pipeline
Orchestration
Plan
Model
Deployment Data
Identify use
case
Frame
problem
Identify
variables
Define metrics
Business Analyst
Data
Engineer
Software
Developer
Data Scientist
Data ingestion
Analyze &
clean data
Define schema
Feature
engineering
Validate model
Deploy model
Serve model
Evaluate
model
Model Training
& tuning
Monitor model
ExampleGen
Select
algorithm
StatisticsGen
SchemaGen
Example
Validator
Transform
Data splitting
Trainer
TunerEvaluator
InfraValidator
Anomaly
detection
Pusher
HUB / JS / LITE / SERVING
Model Server
TF Data Validation (TFDV)
TFT
TF
KerasTuner
TensorFlow
Model Analysis
(TFMA)
BulkInferrer
Data preprocessing
Metadata Store (ML Metadata)
TFX Options for Pipeline
Orchestration
Prof. Dr. Jan Kirenz
TunerEvaluator
InfraValidator
ExampleGen
StatisticsGen
SchemaGen
Example
Validator
Transform
Trainer
Pusher
HUB / JS / LITE / SERVING
Model Server
KerasTuner
BulkInferrer
Metadata Store (ML Metadata)
TF Data Validation (TFDV)
TFT
TF
TFX Options for Pipeline
Orchestration
TensorFlow
Model Analysis
(TFMA)
TensorFlow Lite is a set of tools that enables on-device
machine learning by helping developers run their models
on mobile, embedded, and IoT devices.
TunerEvaluator
InfraValidator
ExampleGen
StatisticsGen
SchemaGen
Example
Validator
Transform
Trainer
Pusher
HUB / JS / LITE / SERVING
Model Server
KerasTuner
BulkInferrer
Metadata Store (ML Metadata)
TF Data Validation (TFDV)
TFT
TF
TFX Options for Pipeline
Orchestration
TensorFlow
Model Analysis
(TFMA)
TensorFlow Lite is a set of tools that enables on-device
machine learning by helping developers run their models
on mobile, embedded, and IoT devices.
Prof. Dr. Jan Kirenz
Plan
Model
Deployment Data
Identify use
case
Frame
problem
Identify
variables
Define metrics
Business Analyst
Data
Engineer
Software
Developer
Data Scientist
Data ingestion
Analyze &
clean data
Define schema
Feature
engineering
Validate model
Deploy model
Serve model
Retrain model
Evaluate
model
Model Training
& tuning
Monitor model
ExampleGen
Select
algorithm
StatisticsGen
SchemaGen
Example
Validator
Transform
Data splitting
Trainer
TunerEvaluator
InfraValidator
Anomaly
detection
Pusher
HUB / JS / LITE / SERVING
Model Server
TF Data Validation (TFDV)
TFT
TF
KerasTuner
TensorFlow
Model Analysis
(TFMA)
BulkInferrer
Data preprocessing
Metadata Store (ML Metadata)
TFX Options for Pipeline
Orchestration
Production phase:
automate the execution
of the ML pipeline based
on a schedule or certain
triggering conditions.
Development phase: run the ML experiment, instead of
manually executing each step.
Data preparation
phase:
automatically
ingest, validate
and transform
data and provide
features to models
Plan
Model
Deployment Data
Identify use
case
Frame
problem
Identify
variables
Define metrics
Business Analyst
Data
Engineer
Software
Developer
Data Scientist
Data ingestion
Analyze &
clean data
Define schema
Feature
engineering
Validate model
Deploy model
Serve model
Retrain model
Evaluate
model
Model Training
& tuning
Monitor model
ExampleGen
Select
algorithm
StatisticsGen
SchemaGen
Example
Validator
Transform
Data splitting
Trainer
TunerEvaluator
InfraValidator
Anomaly
detection
Pusher
HUB / JS / LITE / SERVING
Model Server
TF Data Validation (TFDV)
TFT
TF
KerasTuner
TensorFlow
Model Analysis
(TFMA)
BulkInferrer
Data preprocessing
Metadata Store (ML Metadata)
TFX Options for Pipeline
Orchestration
Production phase:
automate the execution
of the ML pipeline based
on a schedule or certain
triggering conditions.
Development phase: run the ML experiment, instead of
manually executing each step.
Data preparation
phase:
automatically
ingest, validate
and transform
data and provide
features to models
Prof. Dr. Jan Kirenz Prof. Dr. Jan Kirenz
Pipeline orchestration
Pipeline orchestration
Prof. Dr. Jan Kirenz
TFX & Apache Airflow
●Programmatically author, schedule and
monitor workflows with Python code.
●User interface to visualize pipelines
running in production, monitor progress,
and troubleshoot issues.
TFX & Apache Airflow
●Programmatically author, schedule and
monitor workflows with Python code.
●User interface to visualize pipelines
running in production, monitor progress,
and troubleshoot issues.
Prof. Dr. Jan Kirenz
TFX & Apache Beam
●Provides a framework for running batch
and streaming data processing jobs that
run on a variety of runners (Spark, Flink, ...).
●Beam provides an abstraction layer which
enables TFX to run on any supported
runner without code modifications
●TFX only uses the Beam Python API
TFX & Apache Beam
●Provides a framework for running batch
and streaming data processing jobs that
run on a variety of runners (Spark, Flink, ...).
●Beam provides an abstraction layer which
enables TFX to run on any supported
runner without code modifications
●TFX only uses the Beam Python API
Prof. Dr. Jan Kirenz
TFX & Kubeflow pipelines
The Kubeflow Pipelines platform consists of:
●An engine for scheduling multi-step ML
workflows (using Kubernetes).
●User interface (UI) for managing and
tracking experiments, jobs, and runs.
●Python SDK for defining and manipulating
pipelines and components.
●Notebooks for interacting with the system
using the SDK
Kubeflow Pipelines is available as a core component of Kubeflow or as
a standalone installation.
TFX & Kubeflow pipelines
The Kubeflow Pipelines platform consists of:
●An engine for scheduling multi-step ML
workflows (using Kubernetes).
●User interface (UI) for managing and
tracking experiments, jobs, and runs.
●Python SDK for defining and manipulating
pipelines and components.
●Notebooks for interacting with the system
using the SDK
Kubeflow Pipelines is available as a core component of Kubeflow or as
a standalone installation.
Prof. Dr. Jan Kirenz
Prof. Dr. Jan Kirenz
Prof. Dr. Jan Kirenz
Prof. Dr. Jan Kirenz
KubeFlow
KubeFlow
Prof. Dr. Jan Kirenz
ML toolkit for Kubernetes
ML toolkit for Kubernetes
Prof. Dr. Jan Kirenz
Google’s Vertex AI
Launched in May 2021
Google’s Vertex AI
Launched in May 2021
Prof. Dr. Jan Kirenz
ML Pipelines | wrap-up
Source: TensorFlow (2021)
By using a ML pipeline, you can:
●Automate your ML process, which lets you
regularly retrain, evaluate, and deploy your
model.
●Utilize distributed compute resources for
processing large datasets and workloads.
●Increase the velocity of experimentation by
running a pipeline with different sets of
hyperparameters.
To learn more visit the following tutorials @:
https://kirenz.github.io/
MLOps tutorials on how to:
●Install TF and TFX
●Build your first TFX pipeline
●Install Kubeflow
●Build your first Kubeflow pipeline
Jan Kirenz
www.kirenz.com
ML Pipelines | wrap-up
Source: TensorFlow (2021)
By using a ML pipeline, you can:
●Automate your ML process, which lets you
regularly retrain, evaluate, and deploy your
model.
●Utilize distributed compute resources for
processing large datasets and workloads.
●Increase the velocity of experimentation by
running a pipeline with different sets of
hyperparameters.
To learn more visit the following tutorials @:
https://kirenz.github.io/
MLOps tutorials on how to:
●Install TF and TFX
●Build your first TFX pipeline
●Install Kubeflow
●Build your first Kubeflow pipeline
Jan Kirenz
www.kirenz.com
Prof. Dr. Jan Kirenz
Backup
Continuous Integration
and Continuous Delivery
pipeline for an ML/AI
project in Microsoft Azure
Backup
Continuous Integration
and Continuous Delivery
pipeline for an ML/AI
project in Microsoft Azure
Prof. Dr. Jan Kirenz
Continuous Integration
and Continuous Delivery
pipeline for an ML/AI
project in Microsoft Azure
Continuous Integration
and Continuous Delivery
pipeline for an ML/AI
project in Microsoft Azure
Prof. Dr. Jan Kirenz
CI/CD and source code management
CI/CD and source code management
Prof. Dr. Jan Kirenz
Cloud based machine learning service
Cloud based machine learning service
Prof. Dr. Jan KirenzSource: Microsoft (2021) https://github.com/Microsoft/MLOpsPython
Prof. Dr. Jan KirenzSource: Microsoft (2021) https://docs.microsoft.com/en-us/azure/architecture/reference-architectures/ai/mlops-python
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2
3
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Prof. Dr. Jan Kirenzhttps://github.com/Microsoft/MLOpsPython
Prof. Dr. Jan Kirenz
Prof. Dr. Jan Kirenz
Prof. Dr. Jan Kirenz
Prof. Dr. Jan Kirenz
Prof. Dr. Jan Kirenz
Prof. Dr. Jan Kirenz
Prof. Dr. Jan Kirenz
Prof. Dr. Jan Kirenz
Prof. Dr. Jan Kirenz
End of Demo
Continuous Integration
and Continuous Delivery
pipeline for an ML/AI
project in Microsoft Azure
End of Demo
Continuous Integration
and Continuous Delivery
pipeline for an ML/AI
project in Microsoft Azure
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