🎙️ In episode 5 of "AI and ML Conversations," I chat with Thomas Schmidt, an AI engineer at Metabase, who pivoted from agricultural science to data at Shopify and AI agent development. 🚀Thomas shares his unique journey from a master's thesis on predicting farm animals health, to roles at an agritech startup, Shopify, and now building Metabase's AI analytics agent (Metabot).​We dive into key topics like the importance of context in decision-making and communication (like using TLDRs in Slack and "assume good intent"), remote work rituals (retrospectives for safe feedback), and the high technical standards at Shopify vs. startups.Thomas then talks about practical AI agent challenges such as dealing with the AI overhype, benchmarks for evaluation (query correctness, hallucination rates), choosing frameworks like Langchain for control, and managing costs.​Thomas advises juniors to stay curious, follow unconventional paths, master communication to stand out, and use LLMs intentionally without blindly relying on them. LinksIavor Botev: www.linkedin.com/in/iavorbotev/Thomas Schmidt: https://www.linkedin.com/in/thomas-heinz-schmidt/Talk by Thomas at the AI Engineer conference - Everything That Can Go Wrong Building Analytics Agents (And How We Survived It): https://www.youtube.com/watch?v=EnvozxnWjP4Article by Thomas on effective (data) communication: https://dataanalysis.substack.com/p/how-to-communicate-data-effectivelyTimestamps00:00 – Introduction01:12 – Thomas’s background: From dairy farm to data science06:46 – Learning to code & the transition from Excel to R10:28 – Breaking into the industry at an agri-tech startup15:25 – Simple solutions vs. AI hype in agriculture20:03 – Why context is everything in remote communication29:22 – The power of retrospectives & team rituals34:51 – Working at Shopify: Trust batteries & engineering standards45:33 – Using LLMs for personal productivity & coding53:46 – Building AI Agents at Metabase: Reality vs. Hype59:43 – Challenges in agent optimization & evaluation01:06:38 – Tools & Frameworks: LangChain, LightLLM, Pydantic AI01:11:57 – Managing the costs of LLM-powered features01:16:56 – Career advice for juniors & staying curious01:24:26 – Closing

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