How to use UC OSS, what features are available, and intro to the ecosystem. We'll dive into the latest release and get hands-on with demos for working with your UC data and AI assets — including tables, volumes, models and AI functions.
talk-data.com
Speaker
Ben Wilson
3
talks
Sr Software Engineer
Databricks
Ben works on the team supporting MLflow and other notable Open Source ML products at Databricks.
Bio from: Data + AI Summit 2025
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MLflow is one of the most used open source machine learning frameworks with over 13 million monthly downloads. With the recent advancements in generative AI, MLflow has been rapidly integrating support for a lot of the popular AI tools being used such as Hugging Face, LangChain, and OpenAI. This means that it’s becoming easier than ever to build AI pipelines with your data as the foundation, yet expanding your capabilities with the incredible advancements of the AI community.
Come to this session to learn how MLflow can help you:
- Easily grab open source models from Hugging Face and use Transformers pipelines in MLflow
- Integrate LangChain for more advanced services and to add context into your model pipelines
- Bring in OpenAI APIs as part of your pipelines
- Quickly track and deploy models on the lakehouse using MLflow
Talk by: Corey Zumar and Ben Wilson
Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc
We talked about:
Ben’s Background Building solutions for customers Why projects don’t make it to production Why do people choose overcomplicated solutions? The dangers of isolating data science from the business unit The importance of being able to explain things Maximizing chances of making into production The IKEA effect Risks of implementing novel algorithms If it can be done simply – do that first Don’t become the guinea pig for someone’s white paper The importance of stat skills and coding skills Structuring an agile team for ML work Timeboxing research Mentoring Ben’s book ‘Uncool techniques’ at AI-First companies Should managers learn data science? Do data scientists need to specialize to be successful?
Links:
Ben's book: https://www.manning.com/books/machine-learning-engineering-in-action (get 35% off with code "ctwsummer21")
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