MLOps Coffee Sessions #13 How to Choose the Right ML Tool // Jose Navarro and Mariya Davydova

MLOps.community · Beginner ·🏗️ Systems Design & Architecture ·5y ago
This time we talked about one of the most vibrant questions for any MLOps practitioner: how to choose the right tools for your ML team, given the huge amount of open-source and proprietary MLOps tools available on the market today. We discussed several criteria to rely on when choosing a tool, including: - The requirements of the particular team use-cases - The scaling capacity of the tool - The cost of migration from a chosen tool - The cost of teaching the team to use this tool - The company or the community behind the tool Apart from that, we talked about particular use-cases and discussed the trade-offs between waiting for a new release of your tool to get the missing piece of functionality, switching to another tool, and building an in-house solution. We also touched on the topic of organizing MLOps teams and practices across large companies with a lot of ML teams. // Bio: Jose Navarro Jose Navarro is a Machine Learning Infrastructure Engineer making everyday cooking fun at Cookpad, where its recipe platform has more than 40 million monthly users. He holds an MSc in Machine Learning and High-Performance Computing from the University of Bristol. He is interested in Cloud Native technologies, serverless, and event-driven architecture. Mariya Davidova Mariya came to MLOps from a software development background. She started her career as a Java developer in JetBrains in 2011, then gradually moved to developer advocacy for JS-based APIs. In 2019, she joined Neu.ro as a platform developer advocate and then moved to the product management position. Mariya has been obsessed with AI and ML for many years: she finished a bunch of courses, read a lot of books, and even wrote a couple of fiction stories about AI. She believes that proper tooling and decent development and operations practices are essential success components for ML projects, as well as they are for traditional SD. --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https
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1 Our 1st MLOps Meetup // Luke Marsden // MLOps Meetup #1
Our 1st MLOps Meetup // Luke Marsden // MLOps Meetup #1
MLOps.community
 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
MLOps Manifesto with Luke Marsden from Dotscience
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 4 MLOps lifecycle description
MLOps lifecycle description
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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
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
Bare necessities for getting an ML model into production
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 13 MLOps and Monitoring
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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 24 How do you handle ML version control at SurveyMonkey
How do you handle ML version control at 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
Deeper thinking from data scientists around platform blackholes
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 47 Checkpointing, metadata, and confidence in your data
Checkpointing, metadata, and confidence in your data
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 48 Adjacent usecases and multistep feature engineering
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
Reproducability flaws in end to end Machine Learning debugging
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 51 3rd wave of data scientists
3rd wave of data scientists
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 52 MLOps meetup #7 Alex Spanos // TrueLayer 's MLOps Pipeline
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
MLOps Meetup #8 Optimizing Your ML Workflow with Kubeflow 1.0
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 54 Are Kubeflow and Airflow complementary?
Are Kubeflow and Airflow complementary?
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 55 Why Kubeflow gained so much traction=open community
Why Kubeflow gained so much traction=open community
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 56 Who decides the dirrection of Kubeflow
Who decides the dirrection of Kubeflow
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 57 What do Kubeflow and Arrikto do and how do they work together?
What do Kubeflow and Arrikto do and how do they work together?
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 58 Versioning your ML steps with Kubeflow
Versioning your ML steps with Kubeflow
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 59 Machine Learning Lifecycles//Perception vs Reality
Machine Learning Lifecycles//Perception vs Reality
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 60 Kubeflow vs SageMaker in Machine Learning
Kubeflow vs SageMaker in Machine Learning
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