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From Observability to Deployment: Exploring AI System Lifecycles

2024-12-11 โ€“ 2024-12-11 Meetup Visit website โ†—

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๐Ÿ“… Date: 11-12-2024 ๐Ÿ“ Location: Team System, Via Emilio Cornalia, 11 (Gioia)

Agenda

  • 18:30 - Doors Open
  • 19:00 - Welcome to PyData
  • 19:10 - Talk 1: Observability for Large Language Models with OpenTelemetry Speaker: Nir Gazit, CEO @ Traceloop, OpenLLMetry Co-Creator
  • 19:55 - Talk 2: End-to-End Recommender Systems: Training to Deployment Speaker: Marco Bevilacqua, Machine Learning Engineer @ TeamSystem
  • 21:00 - Dinner & Networking

Talks & Speakers

Talk 1: Observability for Large Language Models with OpenTelemetry

Speaker: Nir Gazit, CEO @ Traceloop, OpenLLMetry Co-Creator Large Language Models (LLMs) represent a breakthrough in AI, performing tasks like text generation, translation, and querying. With LLMs becoming a core component of applications such as chatbots and search engines, monitoring their behavior and ensuring reliability is critical. This session will delve into the concept of observability for LLMs, focusing on:

  • Collecting and analyzing data to optimize performance, identify biases, and troubleshoot issues.
  • The new LLM semantic convention adopted by the OpenTelemetry community.
  • How OpenLLMetry uses OpenTelemetry to monitor the entire AI stack, including vector databases, model orchestration platforms, and more.

An essential talk for practitioners looking to understand how observability can enhance trustworthiness and reliability in AI systems.


Talk 2: MLOps with AzureML: Build and Deploy a Recommender System

Speaker: Marco Bevilacqua, Machine Learning Engineer @ TeamSystem For AI-focused organizations, MLOps is essential for managing machine learning workflows at scale. This session will present a practical MLOps use case using the Azure ecosystem, exploring its integration with various tools to streamline workflows from experimentation to deployment. Key highlights:

  • Building and deploying a Recommender System, covering data preparation, model training, validation, deployment, and monitoring.
  • Architectural decisions for MLOps and their next evolution.
  • Insights into integrating Azure MLops with third-party tools and handling data/model improvement.

This session will provide actionable strategies to optimize machine learning lifecycle management, helping organizations leverage AI at scale.


๐ŸŽŸ๏ธ RSVP now to secure your spot! โš ๏ธ Note: If you realize you cannot attend, please cancel your RSVP to allow others to join. Looking forward to seeing you there!

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