🎙️ Who is Peter Gostev?

Peter Gostev is the Head of AI at Moonpig, specializing in generative AI and its transformative applications in e-commerce. With a strong background in structured problem-solving and data analytics, Peter emphasizes the importance of collaboration and rapid prototyping to drive innovation in AI projects.

💡 In this episode...

... we explore Peter's journey in the AI landscape, discussing the balance between delivering immediate value and fostering innovative experimentation. He shares insights on the distinctions between classical and generative AI, highlighting their respective use cases in structured and unstructured data. Peter elaborates on how generative AI can enhance data quality and operational efficiency, particularly in customer service through automation and insights extraction.

The conversation also addresses the challenges of integrating AI into software development, advocating for specialized teams to effectively manage new technologies. Peter encourages continuous learning and experimentation in AI, urging listeners to engage with emerging tools and models to drive innovation in their own practices.

Peter Gostev: https://www.linkedin.com/in/peter-gostev/

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🎙️ Who is Daniel Lenton?

Daniel Lenton is the CEO of Unify, on a mission tounify and simplify the LLM landscape. He has a background in machine learning deployment, as well as in robotics and computer vision. Daniel is an intellectual and creative problem solver who values consistency, balance, and expectation management when building a startup.

💡 In this episode...

... we discuss Daniel's background in machine learning deployment and pursuit of interests in robotics and computer vision. He shares insights on effective communication, user discovery, and gaining insights from customer sales calls for building a successful start-up. He values consistency, balance, and expectation management and credits YC for giving him productivity tips to achieve this. He believes in prioritizing joy and ethics and acknowledges advancements and limitations in AI and AGI.

Daniel Lenton: https://www.linkedin.com/in/daniellenton/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

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🎙️ Who is Demetrios Brinkmann?

Demetrios Brinkmann is the Founder and CEO of the MLOps Community, and also serves as a Conference Host. He is an expert in AI implementation for technical & non-technical professionals and has extensive knowledge of workflow optimization and AI automation. He has a passion for helping people find joy in AI implementation, and believes in augmenting expertise with AI instead of replacing it. Demetrios is also skilled in optimizing productivity through human-AI collaboration and effective decision-making.

💡 In this episode...

... we discuss AI incorporation and workflow optimization. Demetrios shares his expertise on improving efficiency and finding joy in AI implementation for professionals. Demetrios also emphasizes the importance of augmenting expertise with AI rather than replacing it, and optimizing productivity through human-AI collaboration. He provides insightful tips for improving case studies workflow, integrating AI in workflows, and optimizing web development processes. The episode also includes discussions on voice AI use cases, AI in customer service, and streamlining podcast production. Additionally, Demetrios shares his valuable insights on data engineering, financial operations, and MLOps in AI (and more!!).

Demetrios Brinkmann: https://www.linkedin.com/in/nozurbina/

MLOps Community: https://mlops.community/

Deepgram: https://deepgram.com/

Data Engineering for AI/ML Conference (12 september 2024): https://home.mlops.community/home/events/dataengforai?agenda_day=665ee46e12463aa4df4d48b2&agenda_track=665ee46f12463aa4df4d48c4&agenda_stage=665ee46e12463aa4df4d48b8&agenda_filter_view=stage&agenda_view=list

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

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🎙️ Who is Noz Urbina?

Noz Urbina is a content value designer, strategist, and podcaster who specializes in helping enterprises leverage AI and omnichannel for building brands that have relationships with their customers and to foster communities digitally. He has over 14 years of experience in content strategy and customer experience consultancy, where he is a pioneer in adaptive content modelling for delivering personalized, contextually relevant content experiences in an omnichannel environment. Noz has coached teams, developed processes, and spearheaded solutions for some of the world's largest organizations such as Eli Lilly Pharmaceuticals, Barclays Bank, Microsoft, and many more. He is also the founder of Urbina Consulting and OmnichannelX.digital, where he facilitates community learning and enables cross-industry collaboration. Noz is well-known for his work on customer journey mapping, structured content, project planning and scoping, and high-tech product evangelism.

💡 In this episode...

... we discuss how AI is enhancing content creation and the entire content life cycle, specifically in journey mapping, persona creation, and lead scoring. Noz also shares insights on building adaptable AI frameworks, optimizing customer engagement through omnichannel design, and measuring success in patient-centered content projects.

Noz Urbina: https://www.linkedin.com/in/nozurbina/

Urbina Consulting: https://urbinaconsulting.com/

Omnichannel X: https://www.omnichannelx.digital/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

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🎙️ Who is Markus Stoll?

Markus Stoll is an expert AI guest who is well-versed in creating advanced software solutions for the efficient exploration of unstructured data. Markus is the Co-Founder and CTO of Renumics, a company that specializes in developing open-source tools for visualization in Data-Centric AI. Not only has Markus extensive experience in solving complex problems using workflow and visualization, but he also has a strong engineering background too. From Engineer to CTO, Markus has taken his passion for AI and founded Renumics, which has evolved over the years, and today, it's successfully developing machine learning and visualization solutions.

💡 In this episode...

... we discuss Markus's experience in creating advanced software solutions for exploring unstructured data. Markus discusses how feedback, domain understanding, and foundational models can be used to build scalable data evaluation projects and recommendation systems. He also highlights the challenges and opportunities of building RAG systems, the importance of glossaries and future fine-tuning, and introduces Spotlight, a powerful open-source tool for comprehensive data analysis. Finally, Markus introduces an open-source audio data segmentation and labeling tool that shifts from 3D visualization to audio data labeling based on user feedback.

Markus Stoll: https://www.linkedin.com/in/markus-stoll-b39a42138/

Markus's Medium: https://medium.com/@markus.stoll

Github spotlight: https://github.com/Renumics/spotlight

Renumics: https://renumics.com/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

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🌐 Website 👉 https://lets-talk-ai.com/

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🎙️ Who is Maxime Labonne?

Maxime Labonne is a Staff Machine Learning Scientist at Liquid AI. Maxime received his PhD from the Polytechnic Institute of Paris and has been working with machine learning since 2019. He has applied his expertise in various contexts, including R&D, industry, finance, and academia. He is also an AI/ML Google Developer Expert. Maxime is widely known for creating popular LLMs on Hugging Face, like AlphaMonarch-7B, Beyonder-4x7B, Phixtral, and NeuralBeagle14. He also released LLM tools, such as LLM AutoEval, LazyMergekit, LazyAxolotl, and AutoGGUF. Maxime is the author of the technical book "Hands-On Graph Neural Networks using Python," published with Packt. He has written technical articles on his blog and Towards Data Science and created the popular LLM course on GitHub, which has over 27,000 stars.

💡 In this episode...

... we discuss Maxime's expertise in large language models (LLMs) and graph neural networks, as well as his journey from cybersecurity to machine learning. We also talk about insights on LLMS misconceptions, evaluation optimization techniques, and creating augmented datasets for fine-tuning. Maxime also talked about graph neural networks and non-transformer architectures and provided advice for leading language model research and mentoring language model research beginners. Finally, we discuss Maxime's passion for open source and knowledge sharing.

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🎙️ Who is Jakub Czakon?

Jakub Czakon is a senior data scientist turned CMO, currently working at neptune.ai. He has a strong background in AI and LLMs in marketing and has extensive knowledge on how to apply AI in marketing for practical implementation and trust navigation. Jakub draws inspiration from chess skill levels to teach us about data science and emphasizes the importance of learning by doing. He is also an expert in strategies for effective resource allocation and trust-building in marketing and Dev Rel, and he utilizes AI and data to inform decision-making and focuses on problem solving.

💡 In this episode...

... we discuss the application of AI in marketing for practical implementation and trust navigation. Jakub draws inspiration from chess skill levels to teach us about data science, emphasizes the importance of learning by doing, and shares strategies for effective resource allocation and trust-building in marketing and Dev Rel. Jakub also utilizes AI and data to inform decision-making and focuses on problem-solving.

Jakub Czakon: https://www.linkedin.com/in/jakub-czakon/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

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🎙️ Who is Andrew Jardine?

Andrew Jardine is the Head of GTM at Adaptive ML and an advocate for open-source ML. He is also the leader of the MLOps community in Toronto. Andrew's AI journey began as an early employee at Kira Systems, a Doc AI and NLP startup, where he developed the GTM playbook for the corporate market. He has since held commercial roles at Hugging Face, and worked in data science and MLOps industries. Andrew has a diverse background, having worked as a management consultant in London, qualified as a chartered accountant, and obtained a master's degree in engineering.

💡 In this episode...

... we discuss Andrew's fascination with gen AI use cases and LLMS advancements, and the challenges and benefits of implementing open-source AI. We talk about overcoming the challenges of open-source language models through an innovative LLM platform, evaluating LLMs for specific use cases, ensuring safety and toxicity, and more. Andrew also explains how open-source models can help large enterprises control costs and extract data, and discusses the choice between open or closed source models.

Andrew Jardine: https://www.linkedin.com/in/andrew-iain-jardine/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

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🌐 Website 👉 https://lets-talk-ai.com/

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