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.
Events
4:00 PM - 5:00 PM, Dec 5 GMT
Agent Hour
3:00 PM - 8:00 PM, Nov 13 GMT
Agents in Production
5:00 PM - 6:00 PM, Oct 30 GMT
Productionalizing AI: Driving Innovation with Cost-Effective Strategies
Content
video
PyTorch is widely adopted across the machine learning community for its flexibility and ease of use in applications such as computer vision and natural language processing. However, supporting reinforcement learning, decision-making, and control communities is equally crucial, as these fields drive innovation in areas like robotics, autonomous systems, and game-playing. This podcast explores the intersection of PyTorch and these fields, covering practical tips and tricks for working with PyTorch, an in-depth look at TorchRL, and discussions on debugging techniques, optimization strategies, and testing frameworks. By examining these topics, listeners will understand how to effectively use PyTorch for control systems and decision-making applications.
Dec 3rd, 2024 | Views 99
video
This November Reading Group conversation covers advanced retrieval techniques, strategies like iter-drag and hyper-drag for complex queries, and the impact of larger context windows on model performance. The Reading Group also examines challenges in generalizing these methods.
Dec 2nd, 2024 | Views 126
video
Matt Van Itallie, founder and CEO of Sema, discusses how comprehensive codebase evaluations play a crucial role in MLOps and technical due diligence. He highlights the impact of Generative AI on code transparency and explains the Generative AI Bill of Materials (GBOM), which helps identify and manage risks in AI-generated code. This talk offers practical insights for technical and non-technical audiences, showing how proper diligence can enhance value and mitigate risks in machine learning operations.
Nov 29th, 2024 | Views 327