During the event, Michał Bryś helps you understand the basics of LLM models and their significance in the field of AI. He also discusses the privacy and security concerns that come with using large language models in your organization. Watch the webinar where we covered the following topics:

• Introduction to LLM models
• Privacy and information security considerations for using large language models in your organization
• The technology landscape of open source LLM models
• Use case overview: Summarizing financial data using open-source LLM model

Data Scientists can't imagine their work without Jupyter. The notebooks are great for data preparation, experimentation, model building and validating their performance before deploying to production. As ML engineers, we often work on providing the Jupyter environment for Data Science teams, but - if you did it at least once - you know already that providing a platform that is both flexible and cost-effective is a challenge. In GetInData we built a few Jupyter installations (including Jupyter-on-kubernetes) and we understood that the technical excellence of the setup is not enough if it's not reinforced by proper knowledge exchange on how cluster resources management works with all the users and providing KPIs they understand. We're happy to show you different possible Jupyter setups with their pros and cons and share the lessons we learned, covering also topics like culling (stopping inactive notebooks) and running spark-on-kubernetes sessions directly from notebooks

The implementation or migration to cloud is a challenge for many companies, which raises many questions and doubts. In this webinar you will find answers to some non-standard questions that we collected during the implementation of cloud projects.