Session Outline

The talk delves into the emerging field of Large Language Model Operations (LLMOps), highlighting its critical role in the AI landscape for enhancing value delivery and minimising risks.

Key Takeaways

  • Understanding LLMOps: Defining Large Language Model Operations and its distinction from traditional MLOps.
  • Emergence of LLMOps: Exploring the reasons behind the rise of LLMOps in the AI industry.
  • Value Delivery: How LLMOps accelerates the process from experimentation to deployment, enhancing business outcomes.
  • Risk Minimisation: Strategies LLMOps offers for anticipating, identifying, and mitigating potential risks.



Arash Kaviani – Machine Learning Engineering Lead | Australia Post | Australia

Arash is a seasoned AI and data science leader with over twelve years of experience in the data & analytics industry. His expertise lies in leading the development and operation of production-grade and scalable ML and AI solutions that are tightly aligned with business goals and strategy, consistently yielding enhanced customer experience, operational efficiency, and financial gains. His professional experience in tandem with his PhD in Data Science and training in software engineering and IT enable him to seamlessly bridge experimentation and rapid value creation with practical engineering at scale.

June 6 @ 16:40
16:40 — 17:10 (30′)

ANZ-Stage 2 2024

Arash Kaviani – Machine Learning Engineering Lead | Australia Post | Australia