Drawing from his expertise, Joao emphasizes the significance of accessible data, standardized processes, and streamlined implementation in the MLOps landscape. He highlights the crucial roles and responsibilities of various team members involved, including data scientists and MLOps engineers, stressing the need for collaboration and cohesive efforts. Throughout the presentation, Joao addresses common challenges faced by organizations, such as the intricate nature of reversing production deployments and the absence of clearly defined responsibilities within organizational structures. By exploring these hurdles, he equips the audience with valuable insights to overcome obstacles and optimize MLOps practices.

Busuu's journey from a greenfield to a mature MLOps platform is a story of growth, learning, and adaptation. As a language learning platform, Busuu relies heavily on machine learning models to provide personalized and effective learning experiences to its users.

Large Language Models with billions of parameters have the possibility to change how we work with textual data. However, running them on scale at potentially hundred millions of texts a day is a massive challenge. Nils talks about finding the right model size for respective tasks, model distillation, and promising new ways on transferring knowledge from large to smaller models.

Scalable, Serverless deployments of LangChain apps on the cloud without sacrificing the ease and convenience of local development. Streaming experiences without worrying about infrastructure

Daniel Montilla Navas, a mechanical engineer turned programmer, shares his experience working in startups and his current role at Correcto, an AI tool for text correction and generation in Spanish. He discusses the challenges of developing the product, including dealing with different dialects and constantly changing objectives. Montilla Navas highlights the importance of research and decision-making in a small team, as well as the need for caution when moving quickly in the development process. Finally, he emphasizes the importance of continuous learning in the ever-evolving field of AI.

Adam will highlight potential negative user outcomes that can arise when adding LLM-driven capabilities to an existing product. He will also discuss strategies and best practices that can be used to ensure a high-quality user experience for customers.

As a small business, competing with large incumbents can be a daunting challenge. They have more money, more people, and more data, but they can also be inflexible and slow to adopt new technologies. In this talk, we will explore how small businesses can use the power of large language models (LLMs) to compete with large incumbents, particularly in industries like insurance. We will present two examples of how we are using LLMs at Anzen to streamline insurance underwriting and analyze employment agreements and discuss ideas for future applications. By harnessing the power of LLMs, small businesses can level the playing field and compete more effectively with larger companies.

In this panel discussion, the topic of the cost of running large language models (LLMs) is explored, along with potential solutions. The benefits of bringing LLMs in-house, such as latency optimization and greater control, are also discussed. The panelists explore methods such as structured pruning and knowledge distillation for optimizing LLMs. OctoML's platform is mentioned as a tool for the automatic deployment of custom models and for selecting the most appropriate hardware for them. Overall, the discussion provides insights into the challenges of managing LLMs and potential strategies for overcoming them.