Eric currently works as a Strategic Advisor & Lead Architect for AI & Data. He is giving guidance about successful AI usage, best practices for architecture and implementation, as well as helping BSH to shape a strategy for a successful transformation to become Data- and AI-driven. He trained his first artificial neural network in 1989, studied Mathematics & Computer Science with focus on AI / ML & Data and since then worked in the field of AI / ML, Big Data, Software Engineering, Architecture, Consulting. He designed concepts to automate the development of AI products by creating End-to-End MLOps Platforms, as well as strategies to help companies to become data- and AI-driven. His previous responsibilities also encompassed designing Data Lakes of BSH, with his main focus on concepts for automating data ingestion, ETL, data quality and productionization of ML/AI processes (MLOps) on AWS. Before that, he worked as a Software Engineer for over a decade and later as a Lead Developer, ML Engineer, Data Scientist and Architect on many projects.
Not every problem needs to be solved by AI. If you consider the full toolbox available to you to solve tasks, you will realize that AI is mainly an extension of it, adding new tools and therefore new possibilities to solve business problems. However, one of the main challenges is to cultivate a new mindset and culture around working with AI-related technologies and products. In this workshop, you will learn how to identify and prioritize business problems that can and should be solved with AI.
• Developing AI-literacy in the management teams
• Working through common mental blocks, such as the fear of being replaced by AI in the
near future.
• Creating the right expectations around project management in AI context.
• Finding a framework to prioritize AI projects according to risk vs. potential and
identifying low effort and high impact projects.
• Build vs. buy: opportunities and risks of using “out of the box” AI tools
Build once, use many: a reusable RAG boilerplate for large organizations
• What is and why do RAG
• Building and Deploying RAG IaaS
• Reusable and customizable components
• Getting and maintaining data for the LLM
• Quick start use case onboarding formula
• Common boilerplate frontend (Streamlit)
• Reusable observability and monitoring
Check out the incredible speaker line-up to see who will be joining Eric Joachim.
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