Challenging Big Data Assumptions // Hannes Mühleisen & Jordan Tigani // MLOps Podcast #202 clip

MLOps.community · Beginner ·📰 AI News & Updates ·2y ago
MLOps podcast #202 with Hannes Mühleisen, Co-Founder & CEO of DuckDB Labs and Jordan Tigani, Chief Duck-Herder at MotherDuck, Small Data, Big Impact: The Story Behind DuckDB. In this exclusive interview, we heard from the co-creators of DuckDB and MotherDuck, Hannes Mühleisen and Jordan Tigani. They shared insights on the evolution of big data, challenging the assumption that everyone is dealing with massive data sets. They emphasized the importance of recognizing the validity of experiences with data of all sizes. Their perspective disrupts the conventional understanding of big data and encourages a more inclusive approach to data engineering. // Abstract Navigate the intricacies of data management with Jordan Tagani and Hannes Mühleisen, the creative geniuses behind DuckDB and MotherDuck. This deep dive unravels the game-changing principles behind DuckDB's creation, tackling the prevailing wisdom to passionately fill the gap for smaller data set management. Let's also discover MotherDuck's unique focus on providing an unprecedented developer experience and its innovative edge in visualization and data delivery. This episode is teeming with enlightening discussions about managing community feedback, funding, and future possibilities that should not be missed for any tech enthusiasts and data management practitioners. // Bio Hannes Mühleisen Prof. Dr. Hannes Mühleisen is a creator of the DuckDB database management system and Co-founder and CEO of DuckDB Labs, a consulting company providing services around DuckDB. Hannes is also Professor of Data Engineering at Radboud Universiteit Nijmegen. His' main interest is analytical data management systems. Jordan Tigani Jordan is co-founder and chief duck-herder at MotherDuck, a startup building a serverless analytics platform based on DuckDB. He spent a decade working on Google BigQuery, as a founding engineer, book author, engineering leader, and product leader. More recently, as SingleStore’s Chief Product Officer, J
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Our 1st MLOps Meetup // Luke Marsden // MLOps Meetup #1
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 5 What Does Best in Class AI/ML Governance Look Like in Fin Services? // Charles Radclyffe // MLOps #2
What Does Best in Class AI/ML Governance Look Like in Fin Services? // Charles Radclyffe // MLOps #2
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 6 Life purpose and too many spreadsheets
Life purpose and too many spreadsheets
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 7 Explainability, Black boxes and EU white paper on reproducibility
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 8 Hierarchy of Machine Learning Needs // Phil Winder // MLOps Meetup #3
Hierarchy of Machine Learning Needs // Phil Winder // MLOps Meetup #3
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 9 Automatically Retrain Machine Learning Models? Are best practices worth it?
Automatically Retrain Machine Learning Models? Are best practices worth it?
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 10 Building an MLOps Team? Key ideas to keep in mind
Building an MLOps Team? Key ideas to keep in mind
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 11 Hierarchy of MLOps Needs
Hierarchy of MLOps Needs
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 12 Bare necessities for getting an ML model into production
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 13 MLOps and Monitoring
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 14 How Phil Winder got into Data Science and Software Engineering
How Phil Winder got into Data Science and Software Engineering
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 15 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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 16 Friction Between Data Scientists and Software Engineers
Friction Between Data Scientists and Software Engineers
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 17 MLOps Problems in different size companies
MLOps Problems in different size companies
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 18 ML tooling in large companies
ML tooling in large companies
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 19 ML Platforms - The build vs buy question
ML Platforms - The build vs buy question
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 20 ML Services Gateway at SurveyMonkey
ML Services Gateway at SurveyMonkey
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 21 Message buses, Async and sync architecture
Message buses, Async and sync architecture
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 22 MLOps #4: Shubhi Jain - Building an ML Platform @SurveyMonkey
MLOps #4: Shubhi Jain - Building an ML Platform @SurveyMonkey
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 23 Hybrid Data Science Teams @SurveyMonkey
Hybrid Data Science Teams @SurveyMonkey
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 25 Doing ML with Personal Information
Doing ML with Personal Information
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 26 Evolution of the ML feature store @SurveyMonkey
Evolution of the ML feature store @SurveyMonkey
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 27 Developing a Machine Learning Feature Store
Developing a Machine Learning Feature Store
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 28 Auto retrain ML models is not the question
Auto retrain ML models is not the question
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 29 3 key parts to Machine Learning monitoring
3 key parts to Machine Learning monitoring
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 30 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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 31 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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 32 MLOps: Airflow Pros and Cons
MLOps: Airflow Pros and Cons
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 33 Specific challenges in Machine Learning
Specific challenges in Machine Learning
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 34 Current State Of Machine Learning
Current State Of Machine Learning
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 35 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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 36 Learning from real life Machine Learning failures
Learning from real life Machine Learning failures
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 37 Survivorship Bias in machine learning tutorials
Survivorship Bias in machine learning tutorials
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 38 Swiss Cheese model in Machine Learning
Swiss Cheese model in Machine Learning
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 39 Resume driven development in Machine learning & software engineering
Resume driven development in Machine learning & software engineering
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 40 Who has the highest standards in ML?
Who has the highest standards in ML?
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 41 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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 42 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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 43 Speed, Trust, Evolution and Scale in MLOps
Speed, Trust, Evolution and Scale in MLOps
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 44 More difficult transition for data scientists to become ML engineers
More difficult transition for data scientists to become ML engineers
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 45 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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 46 Deeper thinking from data scientists around platform blackholes
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 47 Checkpointing, metadata, and confidence in your data
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 48 Adjacent usecases and multistep feature engineering
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 49 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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 50 Reproducability flaws in end to end Machine Learning debugging
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 51 3rd wave of data scientists
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 52 MLOps meetup #7 Alex Spanos // TrueLayer 's MLOps Pipeline
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 53 MLOps Meetup #8 Optimizing Your ML Workflow with Kubeflow 1.0
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 54 Are Kubeflow and Airflow complementary?
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 55 Why Kubeflow gained so much traction=open community
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 56 Who decides the dirrection of Kubeflow
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 58 Versioning your ML steps with Kubeflow
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