Munich MLOps Community Meetup #3

Hello fellow MLOps Engineers and ML Enthusiasts

We're announcing the date of our next meetup event - we're back on Tuesday, September 19th at 18:30, at the Munich JetBrains Events Space.

Event agenda:

18:30 - 18:50 - Beers & networking

18:50 - 19:00 - Opening talk from the host of the event

19:00 - 19:45 - Eric JoAchim Liese: E2E MLOps platform at BSH

19:45 - 20:20 - Saahil Ognawala: Fine-tuning in the Era of Large Models

20:20 - Onwards - Beer, pizza & networking

More about the talks:

1. Talk #1: E2E MLOps platform at BSH
   While most companies are now familiar with the requirements of a good DevOps process, the situation is still not on the same level of maturity for end-to-end MLOps processes that are required to train, test, deploy, run, and monitor ML models in production.
   Generally, MLOps can be considered as an extension of DevOps. The same principles that are valid for professional software development, also apply in the context of ML development, but the complexity is much higher.
   For complete reproducibility of the status of an ML product, at least two artifacts are needed in MLOps (versioned code & model), and ideally, versioned data as a third artifact, while in DevOps only one is required (versioned code).
   Additionally, model training and optimization need special tools for experiment tracking. And finally, the behavior of productive models needs to be monitored with new concepts and tools, in contrast to traditional application monitoring.
   To bring our ML use cases faster into production, we designed and implemented a concept for a generic, modular end-to-end platform that provides components for all the necessary steps from initial data preparation, over-training, experiment tracking, testing, deployment, running, and monitoring in production.
   In my talk I want to answer the questions of why we decided to build our own platform as well as how we built it, i.e. I'll give a short overview of the current state of our platform.
2. Talk #2: Fine-tuning in the Era of Large Models

About our speakers:

1. Eric JoAchim Liese (LinkedIn)
   After receiving his Degree In Mathematics and Computer Science with focus on Machine Learning and AI, Eric worked as a Senior Data Scientist, ML Engineer and Consultant for several years. He designed concepts to automate the development of AI products by creating End-to-End MLOps Pipelines, as well as strategies to help companies to become data- and AI-driven. Before that, he worked as a Software Engineer for over a decade and later as a Lead Developer on many projects.
   Eric is currently a Lead Architect & Advisor for AI & Data, helping BSH to shape a strategy to become a data and AI driven company. His previous responsibilities also encompassed designing Data Lakes of BSH, with his main focus on concepts for automating data ingestion, ETL, data quality, and productionization of ML/AI processes (MLOps) on AWS.
2. Saahil Ognawala (LinkedIn)
   Saahil Ognawala is Senior Product Manager at Jina AI, a cutting-edge Multimodal AI startup founded in 2020 that has already appeared in Forbes AI30 and CBInsights AI100. Saahil has previously worked as a data scientist and product manager for Munich Re. Prior to that, he finished his M.Sc. and PhD Computer Science at the Technical University of Munich, specializing in deep learning and software engineering.

Looking forward to your RSVPs and to meeting you there!

Keep on hacking!🤩🤩🤩