The Three Layers of Flexibility in Meeting User Needs

MLOps.community · Beginner ·📐 ML Fundamentals ·3y ago
Skills: ML Pipelines70%
Sahil discusses the three layers of flexibility that they introduced to meet the needs of their users. The first layer used Docker reusable components for local development. The second layer was a unified interface for the user's entry point, allowing easy changes around it. Lastly, the team used open-source tools to keep costs low and built a flexible abstraction layer to plug in any technology with a consistent API and replace it with different technology as requirements changed over time. These three layers helped provide the necessary flexibility to meet the needs of their users. MLOps Coffee Sessions #145 with Sahil Khanna, Griffin, ML platform at Instacart co-hosted by Mike Del Balso. Link to the full episode: https://youtu.be/iMizuHVPX0M // Abstract The conversation revolves around the journey of Instacart in implementing machine learning, starting from batch processing to real-time processing. The speaker highlights the importance of real-time processing for businesses and the relevance of Instacart's journey to other machine learning teams. Sahil emphasizes the soft factors, such as staying customer-focused and the right approach, that contributed to the success of Instacart's machine learning implementation. We also recommend two blog posts by Sahil about Instacart's journey. // Bio Sahil is currently a machine learning engineer at Instacart, where they are building a centralized platform for the training, deployment, and management of diverse ML applications. Before Instacart, Sahil developed ML training and inference platforms at Etsy. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/
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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
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 3 MLOps Manifesto with Luke Marsden from Dotscience
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 4 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
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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
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
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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
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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?
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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 57 What do Kubeflow and Arrikto do and how do they work together?
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 58 Versioning your ML steps with Kubeflow
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 59 Machine Learning Lifecycles//Perception vs Reality
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 60 Kubeflow vs SageMaker in Machine Learning
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