Coffee Sessions #159

Why is MLOps Hard in an Enterprise?

MLOps is particularly challenging to implement in enterprise organizations due to the complexity of the data ecosystem, the need for collaboration across multiple teams, the lack of standardization in ML tooling and infrastructure. In addition to these challenges, at Ahold Delhaize there is a requirement for reusability of models as our brands seek to have similar data science products, such as personalized offers, demand forecast, cross-sell.

Take-aways

MLOps is more about people and organisation rather than about the tools. - The key to an effective solution design is not in the number of fancy tools you add, but rather in creating a simple and reusable design that can serve as a standard solution and provide the necessary functionality. - MLOps is breaking the wall between DevOps and data scientist. - Start with understanding the organization, where you stand from MLOps perspective. - Motivate why MLOPs is needed, including main stakeholders. - Train your data scientist, they need to understand operational side of their model, and write production ready code.

In this episode

Maria Vechtomova

Maria Vechtomova

Lead ML engineer, Ahold Delhaize

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.

LinkedIn

Basak Eskili

Basak Eskili

Machine Learning Engineer, Ahold Delhaize

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.

LinkedIn

Demetrios Brinkmann

Demetrios Brinkmann

Host

Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.

Abi Aryan

Abi Aryan

Host

Abi is a machine learning engineer and a live streamer. Over the past six years, her focus has been building machine learning models for various industries including short-form video hosting, OTT, e-commerce, insurance tech, etc., for startups across the US, UK, Canada, and India. Prior to that, she was a Data Science Fellow with Insight Toronto and a Visiting Research Scholar at UCLA working in AutoML, MultiAgent Systems, and Emotion Recognition. In her free time, she helps produce the MLOps Community Podcast.