The Differences Between MLOps and DevOps

Data scientists typically come from a kind of metrics background, statistic background. And you see DevOps engineers and all the best practices how to build software. Data scientists don't always know about these practices. So when the other engineers see data scientists doing something that they would necessarily do, there is distrust that appears.

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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.

// 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 has been 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 new tools and infrastructure that enable data scientists to quickly operationalize algorithms. She is bridging the space between data scientists and platform engineers while improving the way of working in accordance with MLOps principles. In her previous role, she was responsible for bringing models to production. She focused on NLP projects and building data processing pipelines. Basak also implemented new solutions by using cloud services for existing applications and databases to improve time and efficiency.

// MLOps Jobs board

jobs.mlops.community

// MLOps Swag/Merch

https://mlops-community.myshopify.com/

// Related LinksMLOps Maturity Assessment Blog: https://mlops.community/mlops-maturity-assessment/

The Minimum Set of Must-Haves for MLOps Blog: https://mlops.community/the-minimum-set-of-must-haves-for-mlops/

Traceability & Reproducibility Blog: https://mlops.community/traceability-reproducibility/

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