Bridging the Gap: MLOps Transformations in Streamlining Model Deployment

MLOps.community · Intermediate ·📊 Data Analytics & Business Intelligence ·3y ago

Key Takeaways

MLOps transformations for streamlining model deployment by breaking down barriers between data scientists and DevOps engineers

Original Description

Maria recounts the outdated approach of zipping code and emailing it to a separate DevOps team, emphasizing the inefficiencies of the previous method. Basak explains the major change implemented, which involved breaking down barriers between data scientists and DevOps engineers. By taking ownership of deploying models within the same team, they eliminated the need for error-prone handovers between teams. This shift to MLOps facilitated streamlined and collaborative model deployment processes. MLOps Coffee Sessions #159 with Maria Vechtomova, Lead ML engineer, and Basak Eskili Machine Learning Engineer, at Ahold Delhaize, Why is MLOps Hard in an Enterprise? co-hosted by Abi Aryan. Link to the full episode: https://youtu.be/RKbMww5kxHE // Abstract MLOps is particularly challenging to implement in enterprise organizations due to the complexity of the data ecosystem, the need for collaboration across multiple teams, and the lack of standardization in ML tooling and infrastructure. In addition to these challenges, at Ahold Delhaize, there is a requirement for the reusability of models as our brands seek to have similar data science products, such as personalized offers, demand forecasts, and cross-sell. // Bio Maria Vechtomova Maria is a Machine Learning Engineer at Ahold Delhaize. Maria is bridging the gap between data scientists infra and IT teams at different brands and focuses on standardization of machine learning operations across all the brands within Ahold Delhaize. During nine years in Data&Analytics, Maria tried herself in different roles, from data scientist to machine learning engineer, was part of teams in various domains, and has built broad knowledge. Maria believes that a model only starts living when it is in production. For this reason, last six years, her focus was on the automation and standardization of processes related to machine learning. Basak Eskili Basak Eskili is a Machine Learning Engineer at Ahold Delhaize. She is working on creating n
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Playlist

Uploads from MLOps.community · MLOps.community · 0 of 60

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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
MLOps.community
 3 MLOps Manifesto with Luke Marsden from Dotscience
MLOps Manifesto with Luke Marsden from Dotscience
MLOps.community
 4 MLOps lifecycle description
MLOps lifecycle description
MLOps.community
 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
MLOps.community
 6 Life purpose and too many spreadsheets
Life purpose and too many spreadsheets
MLOps.community
 7 Explainability, Black boxes and EU white paper on reproducibility
Explainability, Black boxes and EU white paper on reproducibility
MLOps.community
 8 Hierarchy of Machine Learning Needs // Phil Winder // MLOps Meetup #3
Hierarchy of Machine Learning Needs // Phil Winder // MLOps Meetup #3
MLOps.community
 9 Automatically Retrain Machine Learning Models? Are best practices worth it?
Automatically Retrain Machine Learning Models? Are best practices worth it?
MLOps.community
 10 Building an MLOps Team? Key ideas to keep in mind
Building an MLOps Team? Key ideas to keep in mind
MLOps.community
 11 Hierarchy of MLOps Needs
Hierarchy of MLOps Needs
MLOps.community
 12 Bare necessities for getting an ML model into production
Bare necessities for getting an ML model into production
MLOps.community
 13 MLOps and Monitoring
MLOps and Monitoring
MLOps.community
 14 How Phil Winder got into Data Science and Software Engineering
How Phil Winder got into Data Science and Software Engineering
MLOps.community
 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?
MLOps.community
 16 Friction Between Data Scientists and Software Engineers
Friction Between Data Scientists and Software Engineers
MLOps.community
 17 MLOps Problems in different size companies
MLOps Problems in different size companies
MLOps.community
 18 ML tooling in large companies
ML tooling in large companies
MLOps.community
 19 ML Platforms - The build vs buy question
ML Platforms - The build vs buy question
MLOps.community
 20 ML Services Gateway at SurveyMonkey
ML Services Gateway at SurveyMonkey
MLOps.community
 21 Message buses, Async and sync architecture
Message buses, Async and sync architecture
MLOps.community
 22 MLOps #4: Shubhi Jain - Building an ML Platform @SurveyMonkey
MLOps #4: Shubhi Jain - Building an ML Platform @SurveyMonkey
MLOps.community
 23 Hybrid Data Science Teams @SurveyMonkey
Hybrid Data Science Teams @SurveyMonkey
MLOps.community
 24 How do you handle ML version control at SurveyMonkey
How do you handle ML version control at SurveyMonkey
MLOps.community
 25 Doing ML with Personal Information
Doing ML with Personal Information
MLOps.community
 26 Evolution of the ML feature store @SurveyMonkey
Evolution of the ML feature store @SurveyMonkey
MLOps.community
 27 Developing a Machine Learning Feature Store
Developing a Machine Learning Feature Store
MLOps.community
 28 Auto retrain ML models is not the question
Auto retrain ML models is not the question
MLOps.community
 29 3 key parts to Machine Learning monitoring
3 key parts to Machine Learning monitoring
MLOps.community
 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
MLOps.community
 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
MLOps.community
 32 MLOps: Airflow Pros and Cons
MLOps: Airflow Pros and Cons
MLOps.community
 33 Specific challenges in Machine Learning
Specific challenges in Machine Learning
MLOps.community
 34 Current State Of Machine Learning
Current State Of Machine Learning
MLOps.community
 35 Humans in the Loop are a defining factor in Machine Learning
Humans in the Loop are a defining factor in Machine Learning
MLOps.community
 36 Learning from real life Machine Learning failures
Learning from real life Machine Learning failures
MLOps.community
 37 Survivorship Bias in machine learning tutorials
Survivorship Bias in machine learning tutorials
MLOps.community
 38 Swiss Cheese model in Machine Learning
Swiss Cheese model in Machine Learning
MLOps.community
 39 Resume driven development in Machine learning & software engineering
Resume driven development in Machine learning & software engineering
MLOps.community
 40 Who has the highest standards in ML?
Who has the highest standards in ML?
MLOps.community
 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
MLOps.community
 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
MLOps.community
 43 Speed, Trust, Evolution and Scale in MLOps
Speed, Trust, Evolution and Scale in MLOps
MLOps.community
 44 More difficult transition for data scientists to become ML engineers
More difficult transition for data scientists to become ML engineers
MLOps.community
 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?
MLOps.community
 46 Deeper thinking from data scientists around platform blackholes
Deeper thinking from data scientists around platform blackholes
MLOps.community
 47 Checkpointing, metadata, and confidence in your data
Checkpointing, metadata, and confidence in your data
MLOps.community
 48 Adjacent usecases and multistep feature engineering
Adjacent usecases and multistep feature engineering
MLOps.community
 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
MLOps.community
 50 Reproducability flaws in end to end Machine Learning debugging
Reproducability flaws in end to end Machine Learning debugging
MLOps.community
 51 3rd wave of data scientists
3rd wave of data scientists
MLOps.community
 52 MLOps meetup #7 Alex Spanos // TrueLayer 's MLOps Pipeline
MLOps meetup #7 Alex Spanos // TrueLayer 's MLOps Pipeline
MLOps.community
 53 MLOps Meetup #8 Optimizing Your ML Workflow with Kubeflow 1.0
MLOps Meetup #8 Optimizing Your ML Workflow with Kubeflow 1.0
MLOps.community
 54 Are Kubeflow and Airflow complementary?
Are Kubeflow and Airflow complementary?
MLOps.community
 55 Why Kubeflow gained so much traction=open community
Why Kubeflow gained so much traction=open community
MLOps.community
 56 Who decides the dirrection of Kubeflow
Who decides the dirrection of Kubeflow
MLOps.community
 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?
MLOps.community
 58 Versioning your ML steps with Kubeflow
Versioning your ML steps with Kubeflow
MLOps.community
 59 Machine Learning Lifecycles//Perception vs Reality
Machine Learning Lifecycles//Perception vs Reality
MLOps.community
 60 Kubeflow vs SageMaker in Machine Learning
Kubeflow vs SageMaker in Machine Learning
MLOps.community

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