MLOps Community
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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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MLOps Community


3:00 PM - 5:30 PM, Jun 15 PDT
[San Francisco Workshop] Deploy and Scale LLM-based applications
Build and deploy LLM-based applicationsLLMs have gained immense popularity in recent months. An entirely new ecosystem of pre-trained models and tools has emerged that streamline the process of building LLM-based applications.Join us for this in-person wo
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2:45 PM, Jun 15 - 8:35 PM, Jun 16 GMT
LLMs in Production - Part II


With the open source releasing foundational models at a blistering pace there has never been a better time to develop an AI-powered product. In this talk, we walk you through the challenges and state-of-the-art techniques that can help you fine-tune your own LLMs. Additionally, we will provide guidance on how to determine when a small model would be more appropriate for your use case.
Jun 1st, 2023 | Views 207
MLOps is particularly challenging to implement in enterprise organizations due to the complexity of the data ecosystem, the need for collaboration across multiple teams, and the lack of standardization in ML tooling and infrastructure. In addition to these challenges, at Ahold Delhaize, there is a requirement for the reusability of models as our brands seek to have similar data science products, such as personalized offers, demand forecasts, and cross-sell.
May 30th, 2023 | Views 68
Simba Khadder shares his insights during a talk at the MLOps Meetup in San Francisco, highlighting their experience with 100 million monthly active users. They discussed their failures and learnings, primarily focusing on building a recommended system. By utilizing embeddings, they generated candidate song recommendations and refined the rankings. Simba emphasized the significance of resolving workflow challenges to enhance the effectiveness of data scientists. They introduced the concept of a feature store, which revolutionized their approach to data science.
May 26th, 2023 | Views 43