Managing Data on DAG Store // Stephen Bailey // MLOps Podcast #141 video clip
MLOps Coffee Sessions #141 with Stephen Bailey, Airflow Sucks for MLOps co-hosted by Joe Reis. This episode is sponsored by Wallaroo.
Catch the full episode here https://youtu.be/C1dxY1tCmFo!
Training and sharing knowledge among different stakeholders on platforms like DAG store can be challenging but can be addressed through education and creating a playbook and template for creating jobs, as well as making processes aware of each other and allowing other teams to know what can be reliable.
Decomposing a main DBT project into sub-marts with different classifications, and using Dag store's assets framework to link jobs to upstream dependencies can also help. It is important to classify and categorize data sources and who owns them, to make it clear to end users which data to use.
// Abstract
Stephen discusses his experience working with data platforms, particularly the challenges of training and sharing knowledge among different stakeholders. This talk highlights the importance of having clear priorities and a sense of practicality and mentions the use of modular job design and data classification to make it easier for end users to understand which data to use.
Stephen also mentions the importance of being able to move quickly and not getting bogged down in the quest for perfection. We recommend Stephen's blog post "Airflow's Problem" for further reading.
// Bio
Stephen has worked as a data scientist, analyst, manager, and engineer, and loves all the domains equally. He currently works at Whatnot, a collectibles marketplace that focuses on live shopping, and has previously worked in privacy tech at Immuta. He has his Ph.D. from Vanderbilt University in educational cognitive neuroscience, but it has yet to help him understand why his three children are so crazy.
// MLOps Jobs board
https://mlops.pallet.xyz/jobs
// MLOps Swag/Merch
https://mlops-community.myshopify.com/
// Related Links
Airflow's Problem blog post: https://stkbailey.substack.com/p/airflows
Watch on YouTube ↗
(saves to browser)
Sign in to unlock AI tutor explanation · ⚡30
Playlist
Uploads from MLOps.community · MLOps.community · 0 of 60
← Previous
Next →
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
Our 1st MLOps Meetup // Luke Marsden // MLOps Meetup #1
MLOps.community
Remote Collaboration as a Data Scientist
MLOps.community
MLOps Manifesto with Luke Marsden from Dotscience
MLOps.community
MLOps lifecycle description
MLOps.community
What Does Best in Class AI/ML Governance Look Like in Fin Services? // Charles Radclyffe // MLOps #2
MLOps.community
Life purpose and too many spreadsheets
MLOps.community
Explainability, Black boxes and EU white paper on reproducibility
MLOps.community
Hierarchy of Machine Learning Needs // Phil Winder // MLOps Meetup #3
MLOps.community
Automatically Retrain Machine Learning Models? Are best practices worth it?
MLOps.community
Building an MLOps Team? Key ideas to keep in mind
MLOps.community
Hierarchy of MLOps Needs
MLOps.community
Bare necessities for getting an ML model into production
MLOps.community
MLOps and Monitoring
MLOps.community
How Phil Winder got into Data Science and Software Engineering
MLOps.community
Provenance and Reproducibility in Machine Learning; what is it and why you need it?
MLOps.community
Friction Between Data Scientists and Software Engineers
MLOps.community
MLOps Problems in different size companies
MLOps.community
ML tooling in large companies
MLOps.community
ML Platforms - The build vs buy question
MLOps.community
ML Services Gateway at SurveyMonkey
MLOps.community
Message buses, Async and sync architecture
MLOps.community
MLOps #4: Shubhi Jain - Building an ML Platform @SurveyMonkey
MLOps.community
Hybrid Data Science Teams @SurveyMonkey
MLOps.community
How do you handle ML version control at SurveyMonkey
MLOps.community
Doing ML with Personal Information
MLOps.community
Evolution of the ML feature store @SurveyMonkey
MLOps.community
Developing a Machine Learning Feature Store
MLOps.community
Auto retrain ML models is not the question
MLOps.community
3 key parts to Machine Learning monitoring
MLOps.community
MLOps Meetup #6: Mid-Scale Production Feature Engineering with Dr. Venkata Pingali
MLOps.community
MLOps meetup #5 High Stakes ML: Active Failures, Latent Factors with Flavio Clesio
MLOps.community
MLOps: Airflow Pros and Cons
MLOps.community
Specific challenges in Machine Learning
MLOps.community
Current State Of Machine Learning
MLOps.community
Humans in the Loop are a defining factor in Machine Learning
MLOps.community
Learning from real life Machine Learning failures
MLOps.community
Survivorship Bias in machine learning tutorials
MLOps.community
Swiss Cheese model in Machine Learning
MLOps.community
Resume driven development in Machine learning & software engineering
MLOps.community
Who has the highest standards in ML?
MLOps.community
Venkata Pingali of Scribble Data Thoughts on the Current State of Machine Learning
MLOps.community
Dependable data and being able to Trust in your Data with Venkata Pengali of Scribble Data
MLOps.community
Speed, Trust, Evolution and Scale in MLOps
MLOps.community
More difficult transition for data scientists to become ML engineers
MLOps.community
How many models in prod til I need a dedicated ML platform?
MLOps.community
Deeper thinking from data scientists around platform blackholes
MLOps.community
Checkpointing, metadata, and confidence in your data
MLOps.community
Adjacent usecases and multistep feature engineering
MLOps.community
Standardization of Machine Learning tools like in Software Engineering with Venkata Pingali
MLOps.community
Reproducability flaws in end to end Machine Learning debugging
MLOps.community
3rd wave of data scientists
MLOps.community
MLOps meetup #7 Alex Spanos // TrueLayer 's MLOps Pipeline
MLOps.community
MLOps Meetup #8 Optimizing Your ML Workflow with Kubeflow 1.0
MLOps.community
Are Kubeflow and Airflow complementary?
MLOps.community
Why Kubeflow gained so much traction=open community
MLOps.community
Who decides the dirrection of Kubeflow
MLOps.community
What do Kubeflow and Arrikto do and how do they work together?
MLOps.community
Versioning your ML steps with Kubeflow
MLOps.community
Machine Learning Lifecycles//Perception vs Reality
MLOps.community
Kubeflow vs SageMaker in Machine Learning
MLOps.community
Related AI Lessons
⚡
⚡
⚡
⚡
The Most Valuable Person in the AI Era Isn’t the Generalist. It’s the Specialist Who Got Curious.
Medium · AI
You’ve Already Been Judged by an Algorithm. You Just Don’t Know Which One.
Medium · Data Science
AI Psychosis & Pixel Exploits: Hacking Security with Python+HTMX in 2026 — What You Need to Know in 2026
Dev.to · TechPulse AI
Why Your AI Models Are Vulnerable to 'Toxic Ex' in 2026: The Shocking Truth
Dev.to · TechPulse AI
🎓
Tutor Explanation
DeepCamp AI