Spotlight on AI in your DevOps Lifecycle

4:00 下午 - 5:00 下午 (UTC)

The development life-cycle of intelligent applications has witnessed a significant change with the advent of generative AI. This is indeed a paradigm shift from classical ML - where we train a model with specific statical algorithms to deliver pre-defined outcomes - to LLMs based solution - where we add our own data to pre-trained LLMs to get a non-deterministic outcome. Such a change has larger implications not only on how we build applications but also in how we test, evaluate, deploy and monitor them – leading to the introduction of LLMOps. In this session we are going to explore the foundations of LLMOps, providing examples of tools and practices at our disposal in the Azure ecosystem.

4:00 下午 - 5:00 下午 (UTC)

The story of building an AI application does not stop at deployment. At their core, AI applications use AI models to perform certain tasks and give predictions to our users. The truth is, AI models and their responses change over time and our applications need to adapt to these changes. In this session, we will explore how we can use tools to do this is in a scalable and automated way.

4:00 下午 - 5:00 下午 (UTC)

Whilst AI brings many exciting new features into the tech landscape, it also brings new security risks and introduces new angles on existing security issues. Join us to learn about the best practices and tools you can use to secure the AI-enabled applications that you're building to keep your organization productive and secure.

4:00 下午 - 5:00 下午 (UTC)

This session will jump into the practical aspects of running your applications on Azure, with a special focus on leveraging Azure OpenAI and Python FastAPI. We’ll explore how to effectively scale your applications to meet demand and share best practices for maintaining application health and performance. Whether you’re a seasoned Azure user or just getting started, this session will provide valuable insights to continue your cloud journey

4:00 下午 - 5:00 下午 (UTC)

This talk will focus on MLOps for your Machine learning and AI projects. It will show you how to: Take your Notebook and put it in the cloud and even expose it as an endpoint consumers can consume. Use GitHub to collaborate with others and setup CI/CD and include things like version control, automated testing and discuss deployment strategies.