What is MLOps, and how can it help me work from home? MLOps is the intersection of three disciplines: software engineering, DevOps and machine learning. MLOps refers to the entire end-to-end lifecycle of getting models from lab to live where they can start delivering value. What do software engineers and DevOps need to learn about machine learning to ensure that it can be integrated into their dev & deployment pipelines? What do data scientists and ML engineers need to learn about DevOps, model deployment and monitoring to ensure they can effectively deploy their work without racking up tonnes of technical debt? And now that working from home is fast becoming the new normal, how can MLOps help my team stay efficient when asynchronous collaboration is needed, something our software engineering and DevOps friends have already mastered? MLOps is a complex discipline due to the many more moving parts involved than regular software DevOps, in this inaugural MLOps.community meetup we'll explore and navigate this new space together and give you a guide on how to avoid the most common pitfalls and challenges getting AI into production and collaborating effectively with your team – even when you're distributed.
In this episode
CEO and Founder, Dotscience
Passionate technology leader. Experienced in CEO, CTO, tech lead, product, sales and engineering roles. Proven ability to conceive and execute a product vision from strategy to implementation, while iterating on product-market fit. Deep understanding of AI/ML, infrastructure software and systems programming, containers, microservices, storage, networking, distributed systems, devops, MLOps and CI/CD workflows.
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.