Deployment Design Patterns - Deploying Machine Learning and Deep Learning Models into Production

Confidential & Proprietary
Deployment Design Patterns
Deploying Machine Learning Models into Production
Dan Zaratsian, Cloud Solutions Engineer @ Google
https://github.com/zaratsian
October 2018

1. Train ML Model (using sklearn)
2. Deploy as Batch
3. Deploy as Web App (also using Spark)
4. Deploy as Web Service (Serverless)
5. Automated Model Build & Deploy (NLP, News Sources)

Why is this Important?
● Too much time is lost before deploying.
● Deployments are not scalable.
● Deployments are not easily maintained & updated.
● Deployed models are not monitored.
● Too many models live (and die) on laptops.
● Deployment process is not understood.

Dan Zaratsian
Cloud Solutions Engineer @ Google
University of Akron
B.S. Electrical Engineering
North Carolina State University
M.S. Advanced Analytics

Data Scientist Production
Environment
End Users
(Customers)
Value
ML Model ML Model
Ideal Scenario (Simplified)...

Environment
End Users
(Customers)
No Value
ML Model
A Typical Scenario (What is commonly done)...
Lots of really great code in a Notebook,
but may not easily deployed.

Why is this a challenge?
A good model may look something like this...
Model Score
Code
Date Quarter Down YardsToGo PlayType
2018-09-16 1 4 6 Run
2018-09-23 4 1 10 Pass
2018-10-07 4 3 13 Pass
2018-10-14 3 1 10 Run
Predicted Yards Gained
6.15 yards
-0.90 yards
3.95 yards
1.50 yards
Com h iv M
Pip e
(mo b e t / s o c e)

Pre c in & Fe t e E g e n
It’s common to use dummy variables, standardize values, transform, etc...
Model Score
Code
2018-09-16 1 4 6 Run
2018-09-23 4 1 10 Pass
2018-10-07 4 3 13 Pass
2018-10-14 3 1 10 Run
6.15 yards
-0.90 yards
3.95 yards
1.50 yards
Month Quarter Down
YardsToG
o
PlayType_
Run
PlayType_
Run
9 1 4 6 1 0
9 4 1 10 0 1
10 4 3 13 0 1
10 3 1 10 1 0
In o p (or )
M ip e

Or in many cases, the input is a sparse matrix, heavily feature engineered...
2018-09-16 1 4 6 Run
2018-09-23 4 1 10 Pass
2018-10-07 4 3 13 Pass
2018-10-14 3 1 10 Run
6.15 yards
-0.90 yards
3.95 yards
1.50 yards
Pre c in & Fe t e E g e n
Model Score
Code
In o p (or )
M ip e

A few thoughts...
● Model deployment can mean different things across organizations.
● It’s complex and there’s a need for cross domain knowledge.
● Requirements vary by use case, industry, organization.
● There’s typically no one right way to deploy models.

Environment
End Users
(Customers)
Value
ML Model ML Model
Deployment Considerations
● What tool was used?
● What programming language?
● What are the dependencies?
● Is the model pipelined generalized?
● Can it easily be deployed across OS,
clouds, on-prem, etc?
● Is there a process for
promoting new models
into production?
● How often are models
updated or retrained?
● Where is this server(s) hosted?
● How do we scale?
● How do we secure our apps?
● How do we govern the pipeline?
How do end
users consume
our model?

● How will your models be developed (code, drop-and-drag, hybrid)?
● How will your models be deployed?
○ As a batch process
○ Available via API
○ Real-time / Online Stream
○ Recoded (with model coefficients, logic, etc.)
● What scheduler is being used?
● Is your scheduler able to load and deploy your model pipeline?
● How often will the model be retrained?
● Do you have a process in place to monitor model performance?
● Using docker & kubernetes, serverless, ...?
Deployment Considerations (Continued)

Train ML Model
2. Deploy as Batch
3. Deploy as Web App (also available using Spark)

ML Model Training Process (Simplified)
Data Prep &
Feature Eng.
Model
(model.joblib)
Model
Training
Model
Evaluation
ML Client Environment
Data
Scientist
Database
End Users
(Customers)
Training
Data

Batch Deployment
2. Deploy as Batch
3. Deploy as Web App (also available using Spark)

Reference Architecture: Batch Scoring
Data Prep &
Feature Eng.
Model
(model.joblib)
Data
Scientist
Model
Training
Model
Evaluation
End Users
(Customers)
Training
DataDatabase

Reference Architecture: Batch Scoring
Data Prep &
Feature Eng.
Model
(model.joblib)
Data
Scientist
Model
Training
Model
Evaluation
End Users
(Customers)
Model
(model.joblib)
Training
DataDatabase
Scored Records
Database Records
Deployed on Server, executed with Scheduler

Deploy as Web App
2. Deploy as Batch
3. Deploy as Web App (also in Spark)

Reference Architecture: Deploy as Web App
Data Prep &
Feature Eng.
Model
(model.joblib)
Data
Scientist
Model
Training
Model
Evaluation
End Users
(Customers)
Training
DataDatabase

Accept and
Process Request
POST
Reference Architecture: Deploy as Web App
Data Prep &
Feature Eng.
Model
(model.joblib)
Data
Scientist
Model
Training
Model
Evaluation
End Users
(Customers)
Model
(model.joblib)
Training
DataDatabase
Response
Database Records
Web Framework
(Simplified Architecture)

Deploy as Web Service (Serverless)
2. Deploy as Batch

Reference Architecture: Deploy as Web Service (API)
Data Prep &
Feature Eng.
Model
(model.joblib)
Data
Scientist
Model
Training
Model
Evaluation
End Users
(Customers)
Training
DataDatabase

Accept and
Process Request
API Call
Reference Architecture: Deploy as Web Service (API)
Data Prep &
Feature Eng.
Model
(model.joblib)
Data
Scientist
Model
Training
Model
Evaluation
End Users
(Customers)
Model
(model.joblib)
Training
DataDatabase
Response
Web Service (API)
(Simplified Architecture)

Automated Model Build & Deploy
2. Deploy as Batch
5. Automated Model Build & Deploy (NLP, News)

Confidential & ProprietaryConfidential & Proprietary
UPDATEDEPLOYEVALUATETUNE ML MODEL
PARAMETERS
ML MODEL DESIGN
DATA
PREPROCESSING
Cloud AutoML
A technology that can automatically creates a Machine Learning Model
UPDATEDEPLOYEVALUATETUNE ML MODEL
PARAMETERS
ML MODEL
DESIGN
DATA
PREPROCESSING

Why Cloud AutoML?
Your own
custom models
Simple
Limited ML
expertise needed
High quality

Tha s!
Dan Zaratsian
http://github.com/zaratsian

Deployment Design Patterns - Deploying Machine Learning and Deep Learning Models into Production

Empfohlen

Empfohlen

Weitere ähnliche Inhalte

Was ist angesagt?

Was ist angesagt? (20)

Ähnlich wie Deployment Design Patterns - Deploying Machine Learning and Deep Learning Models into Production

Ähnlich wie Deployment Design Patterns - Deploying Machine Learning and Deep Learning Models into Production (20)

Mehr von All Things Open

Mehr von All Things Open (20)

Kürzlich hochgeladen

Kürzlich hochgeladen (20)

Deployment Design Patterns - Deploying Machine Learning and Deep Learning Models into Production