The MLOps Community is where machine learning practitioners come together to define and implement MLOps.
Our global community is the default hub for MLOps practitioners to meet other MLOps industry professionals, share their real-world experience and challenges, learn skills and best practices, and collaborate on projects and employment opportunities. We are the world's largest community dedicated to addressing the unique technical and operational challenges of production machine learning systems.
Synthetic data is a hot topic in the current Data/Ml space, being proposed as an essential feature in any Data Science toolkit. In a very hands-on approach, the objective is to showcase and depict how to generate synthetic data, deal with the challenges o
You can't just get something done by using tools. You need to go much deeper than that and it is very clear that Data Mesh is the same thing. You have to educate the organization about the movement.
In this session, Shawn broke down the cultural piece of data mesh and how many parallels there are with the MLOps Movement when it comes to the cultural side of MLOps.
Kenny Chen discusses robust production QA Practices for multimodel systems in shifting ground truth and UX environments. This talk covers, QA practices, object metadata tagging, regenerative testing framework, and production model rollbacking.
We spent a lot of time talking about data tooling but we maybe spent not as much time talking about data organizations and efficiently running and organizing data teams.
What about starting with limitations instead of aspirations? Right constraints instead of the north star? In this session, let's learn more about a realistic take on the state of data organizations of today.