MLOps: Airflow Pros and Cons

MLOps.community · Intermediate ·📐 ML Fundamentals ·5y ago
Airflow in Machine Learning as an orchestration tool? In our 5th meetup, we spoke with the Brasilian ML Engineer Flavio Clesio. In this video he talks to us about his feelings about Airflow and what he sees are some of the pros and cons. This is taken from a longer conversation that can be found here: https://youtu.be/9g4deV1uNZo Machine Learning Systems play a huge role in several businesses from the Banking industry to recommender systems in entertainment applications until health domains. The era of "A Data Scientist with a Script in a single machine" is officially over in high stakes ML…
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1 Our 1st MLOps Meetup // Luke Marsden // MLOps Meetup #1
Our 1st MLOps Meetup // Luke Marsden // MLOps Meetup #1
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 2 Remote Collaboration as a Data Scientist
Remote Collaboration as a Data Scientist
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 3 MLOps Manifesto with Luke Marsden from Dotscience
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 5 What Does Best in Class AI/ML Governance Look Like in Fin Services? // Charles Radclyffe // MLOps #2
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 6 Automation, UBI, and taxes with Charles Radclyffe
Automation, UBI, and taxes with Charles Radclyffe
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 7 Common sense AI/ML governance with Charles Radclyffe
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 8 Life purpose and too many spreadsheets
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 9 Explainability, Black boxes and EU white paper on reproducibility
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 10 The problem with too many smart ML engineers
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 11 What should we be optimizing for?
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 12 Hierarchy of Machine Learning Needs // Phil Winder // MLOps Meetup #3
Hierarchy of Machine Learning Needs // Phil Winder // MLOps Meetup #3
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 13 Automatically Retrain Machine Learning Models? Are best practices worth it?
Automatically Retrain Machine Learning Models? Are best practices worth it?
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 14 Building an MLOps Team? Key ideas to keep in mind
Building an MLOps Team? Key ideas to keep in mind
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 15 Hierarchy of MLOps Needs
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 16 Bare necessities for getting an ML model into production
Bare necessities for getting an ML model into production
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 17 MLOps and Monitoring
MLOps and Monitoring
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 18 How Phil Winder got into Data Science and Software Engineering
How Phil Winder got into Data Science and Software Engineering
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 19 Provenance and Reproducibility in Machine Learning; what is it and why you need it?
Provenance and Reproducibility in Machine Learning; what is it and why you need it?
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 20 Friction Between Data Scientists and Software Engineers
Friction Between Data Scientists and Software Engineers
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 21 MLOps Problems in different size companies
MLOps Problems in different size companies
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 22 ML tooling in large companies
ML tooling in large companies
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 23 ML Platforms - The build vs buy question
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 24 ML Services Gateway at SurveyMonkey
ML Services Gateway at SurveyMonkey
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 25 Message buses, Async and sync architecture
Message buses, Async and sync architecture
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 26 MLOps #4: Shubhi Jain - Building an ML Platform @SurveyMonkey
MLOps #4: Shubhi Jain - Building an ML Platform @SurveyMonkey
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 27 Hybrid Data Science Teams @SurveyMonkey
Hybrid Data Science Teams @SurveyMonkey
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 28 How do you handle ML version control at SurveyMonkey
How do you handle ML version control at SurveyMonkey
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 29 Doing ML with Personal Information
Doing ML with Personal Information
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 30 Evolution of the ML feature store @SurveyMonkey
Evolution of the ML feature store @SurveyMonkey
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 31 Developing a Machine Learning Feature Store
Developing a Machine Learning Feature Store
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 32 Auto retrain ML models is not the question
Auto retrain ML models is not the question
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 33 3 key parts to Machine Learning monitoring
3 key parts to Machine Learning monitoring
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 34 MLOps Meetup #6: Mid-Scale Production Feature Engineering with Dr. Venkata Pingali
MLOps Meetup #6: Mid-Scale Production Feature Engineering with Dr. Venkata Pingali
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 35 MLOps meetup #5 High Stakes ML: Active Failures, Latent Factors with Flavio Clesio
MLOps meetup #5 High Stakes ML: Active Failures, Latent Factors with Flavio Clesio
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 ▶ MLOps: Airflow Pros and Cons
MLOps: Airflow Pros and Cons
MLOps.community
 37 Specific challenges in Machine Learning
Specific challenges in Machine Learning
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 38 Current State Of Machine Learning
Current State Of Machine Learning
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 39 Humans in the Loop are a defining factor in Machine Learning
Humans in the Loop are a defining factor in Machine Learning
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 40 Learning from real life Machine Learning failures
Learning from real life Machine Learning failures
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 41 Survivorship Bias in machine learning tutorials
Survivorship Bias in machine learning tutorials
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 42 Swiss Cheese model in Machine Learning
Swiss Cheese model in Machine Learning
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 43 Resume driven development in Machine learning & software engineering
Resume driven development in Machine learning & software engineering
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 44 Who has the highest standards in ML?
Who has the highest standards in ML?
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 45 Venkata Pingali of Scribble Data Thoughts on the Current State of Machine Learning
Venkata Pingali of Scribble Data Thoughts on the Current State of Machine Learning
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 46 Dependable data and being able to Trust in your Data with Venkata Pengali of Scribble Data
Dependable data and being able to Trust in your Data with Venkata Pengali of Scribble Data
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 47 Speed, Trust, Evolution and Scale in MLOps
Speed, Trust, Evolution and Scale in MLOps
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 48 More difficult transition for data scientists to become ML engineers
More difficult transition for data scientists to become ML engineers
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 49 How many models in prod til I need a dedicated ML platform?
How many models in prod til I need a dedicated ML platform?
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 50 Deeper thinking from data scientists around platform blackholes
Deeper thinking from data scientists around platform blackholes
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 51 Checkpointing, metadata, and confidence in your data
Checkpointing, metadata, and confidence in your data
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 52 Adjacent usecases and multistep feature engineering
Adjacent usecases and multistep feature engineering
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 53 Standardization of Machine Learning tools like in Software Engineering with Venkata Pingali
Standardization of Machine Learning tools like in Software Engineering with Venkata Pingali
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 54 Reproducability flaws in end to end Machine Learning debugging
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 55 3rd wave of data scientists
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 56 MLOps meetup #7 Alex Spanos // TrueLayer 's MLOps Pipeline
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 57 MLOps Meetup #8 Optimizing Your ML Workflow with Kubeflow 1.0
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 58 Are Kubeflow and Airflow complementary?
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 59 Why Kubeflow gained so much traction=open community
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 60 Who decides the dirrection of Kubeflow
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