In this MLOps Community podcast, Demetrios chats with Raj Rikhy, Principal Product Manager at Microsoft, about deploying AI agents in production. They discuss starting with simple tools, setting clear success criteria, and deploying agents in controlled environments for better scaling. Raj highlights real-time uses like fraud detection and optimizing inference costs with LLMs while stressing human oversight during early deployment to manage LLM randomness. The episode offers practical advice on deploying AI agents thoughtfully and efficiently, avoiding over-engineering and integrating AI into everyday applications.

Like many companies, Picnic started out with a small, central data science team. As this grows larger, focussing on more complex models, it questions the skillsets & organisational set up. Use an ML platform, or build ourselves? A central team vs. embedded? Hire data scientists vs. ML engineers vs. MLOps engineers How to foster a team culture of end-to-end ownership How to balance short-term & long-term impact

Being an LLM-native is becoming one of the key differentiators among companies, in vastly different verticals. Everyone wants to use LLMs, and everyone wants to be on top of the current tech but - what does it really mean to be LLM-native? LLM-native involves two ends of a spectrum. On the one hand, we have the product or service that the company offers, which surely offers many automation opportunities. LLMs can be applied strategically to scale at a lower cost and offer a better experience for users. But being LLM-native not only involves the company's customers, it also involves each stakeholder involved in the company's operations. How can employees integrate LLMs into their daily workflows? How can we as developers leverage the advancements in the field not only as builders but as adopters? We will tackle these and other key questions for anyone looking to capitalize on the LLM wave, prioritizing real results over the hype.