48 episodes
AI Stories Neil Leiser
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- Technology
Artificial Intelligence, Machine Learning, Data Science and Deep Learning are completely changing the world we live in today. Companies around the world start to make sensible use of big data to influence business decisions and create our future. From video recommendations to autonomous driving, from stock prediction to weather forecasting, the AI revolution is everywhere. The AI stories podcast brings together some of the best Data Scientists, Machine Learning Engineers, Business leaders and researchers that are at the front of this revolution. They are here to talk about their career, how they arrive where they are, give advice and share their vision. They explain how they make use of AI in their daily routine, how they use algorithms to solve business problems and make the world a better place. They are here to share their stories: their AI stories. Hosted by Neil Leiser, Data Scientist at Iwoca. Follow Neil to learn more about career, Data Science, AI and Machine Learning. Linkedin: https://www.linkedin.com/in/leiserneil/ Twitter: https://twitter.com/LeiserNeil
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MLOps Engineering & Coding Best Practices with Maria Vechtomova #48
Our guest today is Maria Vecthomova, ML Engineering Manager at Ahold Delhaize and Co-Founder of Marvelous MLOps.In our conversation, we first talk about code best practices for Data Scientists. We then dive into MLOps, discuss the main components required to deploy a model in production and get an overview of one of Maria's project where she built and deployed a fraud detection algorithm. We finally talk about content creation, career advice and the differences between an ML and an MLOps engi...
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OpenAI, AGI, LLMs Eval & Applied ML with Reah Miyara #47
Our guest today is Reah Miyara. Reah is currently working on LLMs evaluation at OpenAI and previously worked at Google and IBM. In our conversation, Reah shares his experience working as a product lead for Google's graph-based machine learning portfolio. He then explains how he joined OpenAI and his role there. We finally talk about LLMs evaluation, AGI, LLMs safety and the future of the field. If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube ch...
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Google, Gemini, Cloud & LLMOps with Erwin Huizenga #46
Our guest today is Erwin Huizenga, Machine Learning Lead at Google and expert in Applied AI and LLMOps. In our conversation, Erwin first discusses how he got into the field and his previous experiences at SAS and IBM. We then talk about his work at Google: from the early days of cloud computing when he joined the company to his current work on Gemini. We finally dive into the world of LLMOps and share insights on how to evaluate LLMs, how to monitor their performances and how to deploy them. ...
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Deep Learning for Autonomous Driving with Andras Palffy #45
Our guest today is Andras Palffy, Co-Founder of Perciv AI: a startup offering AI based software solutions to build robust and affordable autonomous systems. In our conversation, we first talk about Andras' PhD focusing on road users detection. We dive into AI applied to autonomous driving and discuss the pros and cons of the most common pieces of hardware: cameras, lidars and radars. We then focus on Perciv AI. Andras explains why he decided to focus on radars and how he uses Deep Learning al...
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Launching 7-Figures AI Products With Franziska Kirschner #44
Our guest today is Franziska Kirschner, Co-Founder of Intropy AI and ex AI & Product Lead at Tractable: the world’s first computer vision unicorn. In our conversation, we dive into Franziska's PhD, her career at Tractable and her experience building deep learning algorithms for computer vision products. She explains how she climbed the ladder from intern to AI Lead and shares how she launched new AI product lines generating £ millions in revenues. If you enjoyed the episode, please leave ...
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How He Built The Best 7B Params LLM with Maxime Labonne #43
Our guest today is Maxime Labonne, GenAI Expert, book author and developer of NeuralBeagle14-7B, one of the best performing 7B params model on the open LLM leaderboard. In our conversation, we dive deep into the world of GenAI. We start by explaining how to get into the field and resources needed to get started. Maxime then goes through the 4 steps used to build LLMs: Pre training, supervised fine-tuning, human feedback and merging models. Throughout our conversation, we also discuss RAG vs f...