Serverless ML Workshop with Hopsworks at PyData Seattle

Create a MVPS (minimal viable prediction service) in 90 mins
Jim Dowling
@jim_dowling
CEO, Hopsworks
WORKSHOP
Build a production ML system
with only Python on free
serverless services

Jim Dowling - CEO & Co-Founder of Hopsworks
and an Associate Professor at KTH Royal
Institute of Technology. Co-inventor of the
open-source Hopsworks platform.
Presenter;
https://www.serverless-ml.org - Free Online Course
Serverless ML Community Discord

Register on app.hopsworks.ai
Register on modal.ai
Register on huggingface.co
- create a new “space”
SLIDES:

1
The scope of Data Scientist’s work has
expanded to build Prediction Services
It’s not enough to just train models anymore

Data
Engineer
Data
Scientist
ML
Engineer
Data Dump Model
Enterprise
Data
Model
Serving
models need operational data
If you work in this kind of Enterprise, Conway’s Law implies you will only train models
// What is the scope of a Data Scientist’s work?

Business
Value
Real-Time
Data
Real time prediction services
Personalized search /
recommendations
High Business Value
Batch Prediction Services
Demand forecasting, risk
estimation, ﬁnancial planning
Good Business Value
Batch
Data
Static
Data
One-off Reports
Medium Business Value
// Increase Business Value with more up-to-date Data

1. Train a Model on a static dataset and
produce an Evaluation Report
OR
2. Build a
Minimal Viable Prediction Service
to show your stakeholders how/where
your model will be used
// What is the scope of a Data Scientist’s work?

https://id2223kth.github.io/assignments/project/ServerlessMLProjectsID22232023.html
// Build Minimal Viable Prediction Services

AI Powered
Product & Services
Monitoring
& Compliance
New Data
Historical Data
Output
Source
data data
Models
1. Feature
engineering 3. Inference
2. Model Training
data
data
data
logs
models
data models
Model Registry
ML Pipelines & MLOps
data
// ML System = Feature + Training + Inference Pipelines
Feature Store

2
Write Feature, Training, Inference pipelines
to build an Air Quality Prediction Service
Hopsworks, Modal, Streamlit/Gradio

1. Feature Pipeline => Features/Labels
2. Training Pipeline => Model
3. Inference Pipeline => Predictions
Data =>
Features/Labels =>
Model/Features =>
// ML Systems should have 3 independent Machine Learning Pipelines

Prediction
Problem with
Business KPIs
& Data Sources
Prediction
Consumer
// Minimal Viable Prediction service - Iteratively Develop it
MVPs
(Minimal Viable
Prediction service)
Feature
Pipeline
Inference
Pipeline
Training
Pipeline

feature-pipeline.py
batch-inference-
pipeline.py
training-pipeline.ipynb
Interactive UI
(app.py)
Weather Data
Air Quality Data
// Today’s Serverless ML Air Quality Prediction Service
Dashboard
(Github Pages)

User Interface
with Maps
Predictions
Data
features
model
Model Registry
features,labels models
data
predict
data
Feature View
Feature
Group
Feature
Group
Feature
Group
Hopsworks
Feature View
Streaming Data
event bus
Batch Data
databases &
warehouse
Feature
Pipeline
Online Inference
Pipeline
Training
Pipeline
ML Pipelines & MLOps
Hopsworks: write to Feature Groups, read from Feature Views
features

Data Sources: Open-Meteo and AQPA
https://open-meteo.com/en/docs/air-quality-api
https://aqicn.org/api/

city_name date wind_speed_max wind_direction_dominant wind_gusts_max temp_max
<entity_id> <event_time> <numerical feature> <categorical feature> <numerical feature> <numerical feature>
string datetime double string double double
berlin 2022-01-01 14.3 ne 22.4 22.7
dublin 2022-04-01 9.3 n 18.2 25.4
seattle 2022-07-01 11.1 nw 15.2 20.8
tacoma 2022-10-01 1.3 w 2.5 28.4
Row
Feature value.
Store unencoded to
maximize reuse over
many models.
Feature vector.
Set of feature values with
the same primary key.
Feature Types
Feature Group - weather
entity_id and event_time
uniquely identify each row.
They are not features.

city_name date pm2_5
<entity_id> <event_time> <numerical feature>
string datetime double
berlin 2022-01-01 5.3
dublin 2022-04-01 2.3
seattle 2022-07-01 3.1
tacoma 2022-10-01 4.3
Feature Group - air_quality
Possible Label
Column could be a target
for a prediction problem

3
Get started
Create an account on Hopsworks

● Use Conda or virtual environments to manage your python dependencies on
your laptop. See more info on how to manage your Python environment here.
● git clone https://github.com/jimdowling/air_quality.git
cd air_quality
conda create -n serverlessml python=3.9
conda activate serverlessml
pip install -r requirements.txt
// Getting Started…

1. First, create an account on
https://app.hopsworks.ai
2. Click on “User Settings”
3. Create and Save an “API Key”
Register on app.hopsworks.ai

Add HOPSWORKS_API_KEY as a Environment
variable secret
Create an account on Modal
(might need some time to be approved)
// modal.ai

1. Create an account
on Hugging Face
2. Create a “Space”
3. Create a Streamlit App
// huggingface.ai

1. Add your HOPSWORKS_API_KEY as a Repo Secret

Streaming or On-Demand
Data Pipelines
Backfill data
csv file
Events
msgs in a Kafka Topic
user request input
Incremental Writes
write Pandas DataFrames
to the Feature Store
Batch Data
Pipelines
Static
Data
// Batch and Backfill Feature Pipelines

// What features are computed in a feature pipeline?
● Grouped Aggregations
● Binning
● Filtering
● Rolling/lag features
● Combining data from multiple tables using a common join key.
● Mapping: Transforming values using a user-deﬁned function (UDF).
● Stream mapping: transforming values from using a UDF and
accumulated state.
● Time series analysis: Analyzing or aggregating data over time, such as
identifying trends, patterns, or anomalies.
● NLP - classifying text and outputting features (e.g., sentiment)
● Clustering, dimensionality reduction, etc
Generally, features are not encoded in Feature Pipelines as it prevents
reuse of features across models and slows down writes.

Pandas 1.0
10 GBs 100 GBs TBs PBs
Data Size
Pandas 2.x+, Polars
Spark or SQL (BQ/Snowﬂake/etc)
Spark Streaming
Batch
Streaming
Flink
// Choose the best framework for your feature pipeline
Difficulty

// Write Weather Pandas DataFrame to Hopsworks
weather_df = # 1. read today’s data in as a Pandas DataFrame
# 2. create features for in Pandas DataFrame
weather_fg = fs.get_or_create_feature_group(name="weather",
version=1,
description="Weather Daily Updates",
primary_key=['city'],
event_time=‘date’
)
weather_fg.insert(weather_df) # 3. write Pandas DataFrame to Feature Group
# …

air_quality_df = # 1. read the most recent air quality observations
# 2. create features for in Pandas DataFrame
air_quality_fg = fs.get_or_create_feature_group(name="air_quality",
version=1,
description="City Air Quality Data",
primary_key=['city'],
expectation_suite=expectation_suite,
event_time='date'
)
air_quality_fg.insert(air_quality_df) # 3. write DataFrame to Feature Group
# …
// Write Air Quality Pandas DataFrame to Hopsworks

// Feature Functions in a Python module
def moving_average(df, window=7):
df[f'mean_{window}_days'] = df.groupby('city_name')['pm2_5']
.rolling(window=window).mean().reset_index(0,drop=True).shift(1)
features/air_quality.py
One Python module per Feature Group

stub = modal.Stub("air_quality_daily")
image = modal.Image.debian_slim().pip_install(["hopsworks"])
@stub.function(image=image, schedule=modal.Period(days=1),
secret=modal.Secret.from_name("jim-hopsworks-ai"))
def g():
…
if __name__ == "__main__":
stub.deploy("air_quality_daily")
with stub.run():
g()
// Schedule your Feature Pipeline with Modal
Define program
dependencies
and program
schedule, env
variables
Deploy main()
function as a
scheduled
program on
modal

schedule:
- cron: '0 14 * * *'
..
steps:
- name: checkout repo content
uses: actions/checkout@v3
- name: setup python
uses: actions/setup-python@v3
with:
python-version: '3.9'
working-directory: .
- name: install python packages
run:
pip install -r requirements.txt
- name: execute Feature Pipeline
env:
HOPSWORKS_API_KEY: ${{ secrets.HOPSWORKS_API_KEY }}
CONTINENT: "Seattle"
run: jupyter nbconvert --to notebook --execute 2_feature_pipeline.ipynb
// Schedule Notebooks with Github Actions

// Create Data Validation Rules in Great Expectations
from great_expectations.core import ExpectationSuite, ExpectationConfiguration
expectation_suite = ExpectationSuite(
expectation_suite_name="transaction_suite")
expectation_suite.add_expectation(
ExpectationConfiguration(
expectation_type="expect_column_values_to_be_between",
kwargs={
"column":"pm2_5",
"min_value":"0.0",
"max_value":"1000.0",
}
)
)

Feature
Pipeline
Data Sources
Data
warehouse
Applications
-
Services
Validate
Data
Unstructured
Data
Great Expectations
Expectation Suite
Feature 1
Expectation Suite
Feature 2
Validation Reports
Feature 1
Validation Reports
Feature 2
Feature Group 1
Data
Feature Group 2
Data
Monitor
Search
Alert
// Data Validation with Great Expectations
https://www.hopsworks.ai/post/data-validation-for-enterprise-ai-using-great-expectations-with-hopsworks

https://docs.hopsworks.ai/3.1/user_guides/fs/feature_group/data_validation/
// Great Expectations in Hopsworks

Redundant
Feature
Irrelevant
Feature
A similar feature
is already
selected
The feature has
no predictive
power
Prohibited
Feature
Feature cannot be
used. May be
context dependent
Useful
Feature
It has predictive
power for my
prediction problem
Infeasible
Feature
Useful feature that
can’t be computed
for some reason
// Feature Selection

FEATURE VIEW
Select and Join Features
city_name date wind_speed_max wind_direction_dominant wind_gusts_max temp_max
<entity_id> <event_time> <numerical feature> <categorical feature> <numerical feature> <numerical feature>
string datetime double string double double
berlin 2022-01-01 14.3 ne 22.4 22.7
dublin 2022-04-01 9.3 n 18.2 25.4
seattle 2022-07-01 11.1 nw 15.2 20.8
tacoma 2022-10-01 1.3 w 2.5 28.4
city_name date pm2_5
<entity_id> <event_time> <numerical feature>
string datetime double
berlin 2022-01-01 5.3
dublin 2022-04-01 2.3
seattle 2022-07-01 3.1
tacoma 2022-10-01 4.3
// Select Features and Create a Feature View

Label ts
Feature ts
// Data Modelling: Training Data is a Fact/Dimension Model
Feature ts
Feature ts
Feature ts Feature ts
ts=TimeStamp
Join Features to
Labels with Entity IDs

select features
(wind_speed_max,wind_speed_dir, …)
air_quality_fv
FeatureView
ﬁlter (city_name == “Seattle”)
transform = { “wind_speed_max” : min_max_scaler }
label = [“pm2_5”]
weather
Feature Group
air_quality
Feature Group
Training Data Inference Data
Optional Steps
join on
city, date
// Building a Feature View from existing Features

// Select Features and create a Feature View
select
features
from
feature
groups
create
feature
view from
selected
features
fg_air_quality = fs.get_feature_group(name="air_quality", version=1)
fg_weather = fs.get_feature_group(name="weather", version=1)
selected = fg_air_quality.select(['pm2_5').join(fg_weather.select_all())
fv = fs.create_feature_view(name="air_quality_fv",
version=1,
description="Weather and Air Quality",
labels=['pm2_5'],
query=selected
)

Time
city date max_temp wind_direction_dominant … pm2_5
<entity_id> <event_time> min_max_scaler ordinal_encoder … min_max_scaler
string datetime double string … double
Amsterdam 2022-01-01 21.4 w … 11.0
Berlin 2022-04-01 25.3 nw … 10.2
Seattle 2022-07-01 12.1 n … 1.4
Dublin 2022-10-01 15.3 ne … 2.2
Dehli 2022-11-01 33.9 n … 38.2
New York 2022-12-01 5.1 e … 19.2
Portland 2023-01-01 -2.4 ssw … 3.2
Training
Data-v2
Batch
Inference
Data
Feature View - air_quality_fv
Feature
vector
Training
Data-v1
// Feature Views - Create Data Snapshots for Training or Inference

// Feature Views - Create Data Snapshots for Training or Inference

FeatureView for Model-FeedForwardNet
<entity_id> <event_time> label_encoder …
FeatureView for Model-XGBoost
FeatureView for Model-LogisticRegression
Model-Dependent
Transformations
// Feature Views - Model-Dependent Transformations
Model-Dependent
Transformations
Model-Dependent
Transformations

X_train, X_test, y_train, y_test = fv.train_test_split(test_size=0.2)
categorical_transformer=Pipeline(steps=[("encoder",
OneHotEncoder(handle_unknown="ignore"))])
preprocessor = ColumnTransformer(transformers=[
("cat", categorical_transformer, categorical_feature_ids)])
clf = Pipeline(steps=[("preprocessor", preprocessor), ("regressor", XGBRegressor())])
clf.fit(X_train, y_train)
// Model Training with Feature Views
get train &
test set
model-dependent
feature encoding
The pipeline
encodes features,
then train model

joblib.dump(clf, 'air_quality_model/xgboost_pipeline.pkl')
input_schema = Schema(X_test)
output_schema = Schema(y_test)
aq_model = mr.sklearn.create_model("air_quality_model",
metrics={'accuracy': accuracy},
input_example=X_test.sample().to_numpy(),
model_schema=ModelSchema(input_schema=input_schema, output_schema=output_schema))
fraud_model.save('air_quality_model')
// Store the trained Model in a Model Registry
serialize
model
package
up model
and upload
to model
registry
get model
schema
from DFs

4 Inference Pipelines
WORKSHOP

fv = fs.get_feature_view(name="air_quality_fv", version=1)
df = feature_view.get_batch_data(start_time=today)
mr = project.get_model_registry()
model = mr.get_model("lending_model", version=1)
model_dir = model.download()
model = joblib.load(model_dir + "/air_quality_model.pkl")
predictions_df = model.predict(df)
// Batch Inference Pipeline - predict air quality on latest features
download
inference
data
download
model
make
predictions

model = mr.get_model("air_quality_model", version=1)
def air_quality(city_name):
arr = fv.get_feature_vector({"city_name": city_name}, passed_features={})
y_pred = model.predict(np.asarray(arr).reshape(1, -1))
get feature
view &
download
model
make a
prediction with
precomputed
and
user-supplied
features
// Online Inference Pipeline - interactive prediction service

model = mr.get_model("air_quality", version=1)
# FLINK CALLS THE METHOD BELOW FOR EVERY EVENT
def air_quality(city_name):
arr = fv.get_feature_vector({"city_name": city_name}, passed_features={})
y_pred = model.predict(np.asarray(arr).reshape(1, -1))
get feature
view &
download
model
make a
prediction with
precomputed
and
user-supplied
features
// Streaming Inference Pipeline

5 Principles of MLOps
WORKSHOP

● ML-enabled products evolve over time
○ The available input data (features) change over time
○ The target you are trying to predict changes over time
● Automate the testing and deployment of ML-enabled Products
○ Safe incremental updates and a tighter iteration loop
● To this end, features and models must be tested
○ Tests should run automatically as part of a CI/CD workﬂow
// MLOps Principles

// MLOps according to Hopsworks (1) Automated Testing
Untrusted Raw Data
● ML-Apps
build-on models tested with A/B tests
● Models
tested with model validation tests
● Features
tested with data validation and unit tests
and corrected with imputation/encoding
Tested Features
Tested Models
Tested
ML-Apps

air_quality_v1
air_quality_v1
air_quality_v2
air_quality_v2
// MLOps according to Hopsworks (2) Versioning of Features, Models
Models
Rollback
Upgrade
weather_v1
air_quality_v1
Features
weather_v1
air_quality_v2
air_quality_v1
Models
air_quality_v2
Features

// MLOps according to Hopsworks (2) Versioning of Features, Models
Models
air_quality_v2
Rollback
Upgrade
weather_v1
air_quality_v1
air_quality_v2
Features
air_quality_v1
weather_v1
air_quality_v2
air_quality_v1
Models
air_quality_v1
air_quality_v2
Features

DEVELOPMENT
Feature Store
PRODUCTION
Feature Store
Test Run
Unit
testing
Feature
development
Entity
Creation STAGING
Feature Store
Scheduled
Pull Request
Manual
Execution
CI/CD Execution
On-demand
Orchestrated
& Scheduled
Merge
backﬁlled
// CI/CD for Feature Pipelines
https://www.hopsworks.ai/post/optimize-your-mlops-workflow-with-a-feature-store-ci-cd-and-github-actions

Evaluate and
Validate Model
Training Data
Evaluation
Sets (for Bias)
Main Branch
Jenkins
PyTest
Pull Request Trigger
bias,behaviour,
performance tests
deployment-test
Model Training
// CI/CD for Model Training
STAGING
Model Registry
PyTest
Manual Trigger Development Branch
Evaluate and
Validate Model
Training Data
Evaluation
Sets (for Bias)
bias,behaviour,
performance tests
deployment-test
Model Training
DEVELOPMENT
Model Registry

HTTPS
client
KServe
Blue: model-A1
Green: model-A2
Network
Endpoint
(Istio)
Hopsworks
deploy model-A2
with 10% of traffic
read pre-computed features
10% traﬃc
(mirrored)
100% traﬃc
// CI/CD for Deployed Models
PRODUCTION
Feature Store

weather_v1
air_quality_v1
air_quality_v1
air_quality_v1
training_data_v1
Feature Groups
Training Data
Models
Connector BQ
Connector
Snowflake
Connector S3
Connector
Redshift
air_quality
Model Deployments
Feature View
Source Data
// Lineage for Features and Models

● In Hopsworks, you can make non-breaking schema changes that do not require
updating the schema version.
● Appending features with a default value is a non-breaking schema change
● Breaking schema changes require updating the schema version for a Feature Group.
fg1 = fs.create_feature_group(name=”example”, version=1)
df = fg1.read()
fg2 = fs.create_feature_group(name=”example”, version=2, features=new_features, …)
fg2.insert(df) #backfill the new feature group with data from the prev version
from hsfs.feature import Feature
features = [
Feature(name="id",type="int",online_type="int"),
Feature(name="name",type="string",online_type="varchar(20)")]
fg = fs.get_feature_group(name="example", version=1)
fg.append_features(features)
// Schema Versioning

6 Ideas for Serverless ML Systems?
WORKSHOP

ChatGPT / GPT-4
(model of world)
1. History,
Context
Personalized
Prompt
Feature Store
Prompt
2.Hand-crafted
Prompts
Vector DB
(model of user)
embedding
Personalized ChatGPT
You have 10,000 customers and 8.7% churn annually. This
is a relative high amount churn, so you should estimate
churn at a fine granularity using this specific churn model:
….
ChatGPT
A churn model helps predict the probability of a customer
leaving you within a given time period. Here is a code
snippet in Python for estimating customer churn:
…..
Help me design a churn model for my customers!
// IDEA FOR SERVERLESS ML SYSTEM - Personalized GPT-4

Show love with a star!
SERVERLESS MACHINE LEARNING
www.serverless-ml.org
https://github.com/featurestoreorg/serverless-ml-course ⭐

Serverless ML Workshop with Hopsworks at PyData Seattle

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