The roles within AI engineering are as diverse as the challenges they tackle. From integrating models into larger systems to ensuring data quality, the day-to-day work of AI professionals is anything but routine. How do you navigate the complexities of deploying AI applications? What are the key steps from prototype to production? For those looking to refine their processes, understanding the full lifecycle of AI development is essential. Let's delve into the intricacies of AI engineering and the strategies that lead to successful implementation.
Maxime Labonne is a Senior Staff Machine Learning Scientist at Liquid AI, serving as the head of post-training. He holds a Ph.D. in Machine Learning from the Polytechnic Institute of Paris and is recognized as a Google Developer Expert in AI/ML. An active blogger, he has made significant contributions to the open-source community, including the LLM Course on GitHub, tools such as LLM AutoEval, and several state-of-the-art models like NeuralBeagle and Phixtral. He is the author of the best-selling book “Hands-On Graph Neural Networks Using Python,” published by Packt.
Paul-Emil Iusztin designs and implements modular, scalable, and production-ready ML systems for startups worldwide. He has extensive experience putting AI and generative AI into production. Previously, Paul was a Senior Machine Learning Engineer at Metaphysic.ai and a Machine Learning Lead at Core.ai. He is a co-author of The LLM Engineer's Handbook, a best seller in the GenAI space.
In the episode, Richie, Maxime, and Paul explore misconceptions in AI application development, the intricacies of fine-tuning versus few-shot prompting, the limitations of current frameworks, the roles of AI engineers, the importance of planning and evaluation, the challenges of deployment, and the future of AI integration, and much more.
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