The document discusses tracking the machine learning (ML) lifecycle using MLflow, an open-source platform that facilitates reproducibility and management of ML models. It highlights the importance of documenting data science processes, organizing experiments, and storing components for effective workflows. The tutorial focuses on a case study involving the prediction of California house prices, showcasing the use of MLflow for managing experiments and tracking performance metrics.

1. DSC DACH 23 ╳
Go with the flow
Track your machine learning lifecycle using

2. Agenda
1. Introduction to Parkside Interactive
2. Why track your ML lifecycle?
3. What is MLflow?
4. Tutorial
5. Recap

3. We Work Globally and
Have Offices in GrazHQ
,
Linz, Vienna, Porto &
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the entire digital product lifecycle.
12
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4

5. DATA SCIENTIST
Philipp Danninger
Based in Graz, Austria
MSc in Economics
MSc in Data & Information Science
danninger@parkside-interactive.com
www.linkedin.com/in/philipp-danninger

6. Why track the lifecycle of ML applications?
What are ingress
channels?
How often do you receive
new data?
Data ingress
Deployment method &
workflow?
What is the final model’s
version?
Dependencies?
Model deployment &
maintenance
Data imputations?
Sample sizes?
Split size?
Data leakage?
Training set creation
Choice of algorithm?
Hyperparameters?
Cross-validation?
Performance metrics?
Model training &
evaluation
What parts of the data
are usable?
How to store the data?
Data cleaning
Can you answer all the
questions after some
time has passed?
Store all components
for reproducibility in
one place → MLflow

7. What is MLflow?
● Open source platform for machine
learning lifecycle
● 4 components
○ MLflow Tracking
○ MLflow Projects
○ MLflow Models
○ MLflow Registry

8. Some MLflow Tracking basics
● MLflow Tracking is organized around the concept of runs, which are executions of
some piece of data science code
● You can optionally organize runs into experiments, which group together runs for a
specific task
● Code version (git commit), start & end time, source, parameters, metrics, artifacts
● Store runs locally, to SQLAlchemy compatible database or remote tracking server
● Once your runs have been recorded, you can query them using the Tracking UI or the
MLflow API

9. Tutorial - What are we going to cover?
ML problem: Prediction of California house prices
Creation of ML pipeline
MLflow:
● Experiments - different algorithms
● MLflow runs for experiments
● Tracking of artifacts, performance metrics &
hyperparameters
● Registration of models
● Prediction using registered models
Repo: https://github.com/pdanninger/dsc_dach.git

10. Recap
● Machine learning use-cases pose individual challenges
● A transparent documentation of your approach saves a lot of time
● MLflow provides simple but powerful capabilities to document and structure
your approach for reproducibility
● MLflow allows you to structure the deployment of your machine learning
models

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