In this episode, the MLOps community talks about the importance of bringing DevOps principles and discipline into Machine Learning. Alfredo explains insights around creating the MLOps role, automation, constant feedback loops, and the number one objective - to ship Machine Learning models into production. Additionally, we covered some aspects of getting started with Machine Learning that is critical, in particular how democratization ML knowledge is critical to a better environment, from libraries to courses, to production results. Spreading the knowledge is key!
In this episode
Alfredo Deza is a passionate software engineer, speaker, author, and former Olympic athlete. With almost two decades of DevOps and software engineering experience, he teaches Machine Learning Engineering and gives lectures around the world about software development, personal development, and professional sports. Alfredo has written several books about DevOps and Python including Python For DevOps and Practical MLOps. He continues to share his knowledge about resilient infrastructure, testing, and robust development practices in courses, books, and presentations. Alfredo Deza is the author of Python for DevOps and Practical MLOps.
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.