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FinOps and GreenOps offer a well-defined path to more efficient use of cloud resources and lower environmental impact. Join us in Bristol on 30 October to see how we can get control over what cloud is costing us and how we can limit its climate impact.

Hosted by Just Eat in Bristol, this Meetup will look at practical ways we can control how we use cloud to maximise its benefits and minimise the problems

Here's the agenda: 5:00 pm Reception 5:30 pm Introduction & Welcome 5:40 pm Keynote Address "What shall we do about Cloud Waste?" - Mike Bradbury\, FinOps Certified Professional 6:00 pm 5-minute Lightning talks from industry experts: - Ben Percy\, Just Eat - Andy Foley\, Nationwide BS - Mark Wilson\, Walgreens-Boots Alliance - Greg Holmes\, Apptio - Kevin Barnes\, Version1 6:45 pm Interactive Breakout Sessions 7:15 pm Informal networking

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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