MLOps Community
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.
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Events
5:00 PM - 6:00 PM, Jan 25 GMT
The 7 Lines of Code You Need to Run Faster Real-time Inference
5:00 PM - 6:00 PM, Jan 18 GMT
How Aurora Accelerates Autonomous Vehicle ML Model Development Using Kubeflow
5:00 PM - 6:00 PM, Dec 7 GMT
The Motivation for MLOps
Content
video
In this talk, team Aurora discusses how they accelerated ML model development for autonomous vehicles by integrating with Kubeflow.
Team Aurora covers how the Kubeflow infrastructure evolved and how it is currently deployed. Then they discuss how they build pipelines, the developer experience, and the benefits of using pipelines. Finally, they walk through how they adopted Kubeflow org-wide.
Jan 27th, 2023 | Views 79
video
Investors are currently focusing on developer tooling and the foundational AI model movement, as they have seen explosive growth in this area. This podcast explores the impact of foundational models on investment thesis and the future of machine learning operations.
The discussion also touches on the idea of generative AI and large language models, and their potential impact on MLOps in the next 10 years. Jill and Manmeet from Capital G share their insights on this topic.
Jan 27th, 2023 | Views 62
video
The field of Explainable Artificial Intelligence (XAI) is continuously evolving, with an increasing focus on providing model-centric explanations in a human-centric manner. However, better frameworks and training for users are needed to fully utilize the potential of XAI tools.
Additionally, there is a discrepancy in the approach to fairness in XAI, with the industry approaching it from a regulatory standpoint, while academia is engaging in more discussion and research on the topic.
Jan 17th, 2023 | Views 88