From Observability to Deployment: Exploring AI System Lifecycles

📅 Date: 11-12-2024
📍 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

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### 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.

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#### 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.

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Looking forward to seeing you there!