Max Mergenthaler, Co-founder and CEO of Nixtla, shares his journey from philosophy to building innovative forecasting libraries. He discusses founding Nixtla and its popular tools like StatsForecast, MLForecast, and NeuralForecast. The conversation highlights TimeGPT, an advanced model for time series analysis, emphasizing its potential over traditional methods. Max also touches on best practices and common pitfalls in forecasting, along with the evolving role of data scientists in this rapidly changing field.

Sebastian Raschka, a Senior Staff Research Engineer at Lightning AI and bestselling author, dives into the art of building large language models. He shares insights on two significant open-source libraries, PyTorch Lightning and LitGPT, that enhance LLM training and deployment. The discussion shifts to his new book, where he outlines essential steps in LLM training and contrasts models like GPT-2 with the latest Llama 3. Sebastian also explores the universe of multimodal LLMs and their potential, highlighting exciting developments on the horizon.

Our guest today is Loubna Ben Allal, Machine Learning Engineer at Hugging Face 🤗 . In our conversation, Loubna first explains how she built two impressive code generation models: StarCoder and StarCoder2. We dig into the importance of data when training large models and what can be done on the data side to improve LLMs performance. We then dive into synthetic data generation and discuss the pros and cons. Loubna explains how she built Cosmopedia, a dataset fully synthetic generated using Mixtral 8x7B.Loubna also shares career mistakes, advice and her take on the future of developers and code generation.  If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.Cosmopedia Dataset: https://huggingface.co/blog/cosmopediaStarCoder blog post: https://huggingface.co/blog/starcoderFollow Loubna on LinkedIn: https://www.linkedin.com/in/loubna-ben-allal-238690152/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ---(00:00) - Intro(02:00) - How Loubna Got Into Data & AI(03:57) - Internship at Hugging Face(06:21) - Building A Code Generation Model: StarCoder(12:14) - Data Filtering Techniques for LLMs(18:44) - Training StarCoder(21:35) - Will GenAI Replace Developers? (25:44) - Synthetic Data Generation & Building Cosmopedia(35:44) - Evaluating a 1B Params Model Trained on Synthetic Data(43:43) - Challenges faced & Career Advice

Our guest today is Petar Veličković, Staff Research Scientist at Google DeepMind and Affiliated Lecturer at University of Cambridge.In our conversation, we first dive into how Petar got into Graph ML and discuss his most cited paper: Graph Attention Networks. We then dig into DeepMind where Petar shares tips and advice on how to get into this competitive company and explains the difference between research scientists and research engineering roles. We finally talk about applied work that Petar worked on including building Google Maps' ETA algorithm and an AI coach football coach assistant to help Liverpool FC improve corner kicks. If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.Graph Attention Networks Paper: https://arxiv.org/abs/1710.10903ETA Prediction with Graph Neural Networks in Google Maps: https://arxiv.org/abs/2108.11482TacticAI: an AI assistant for football tactics (with Liverpool FC): https://arxiv.org/abs/2402.01306Follow Petar on LinkedIn: https://www.linkedin.com/in/petarvelickovic/ Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ---(00:00) - Intro(02:44) - How Petar got into AI(06:14) - GraphML and Geometric Deep Learning(10:10) - Graph Attention Networks(17:00) - Joining DeepMind(20:24) - What Makes DeepMind People Special?(22:28) - Getting into DeepMind(24:36) - Research Scientists Vs Research Engineer(30:40) - Petar's Career Evolution at DeepMind(35:20) - Importance of Side Projects(38:30) - Building Google Maps ETA Algorithm(47:30) - Tactic AI: Collaborating with Liverpool FC(01:03:00) - Career advice 

Lewis Tunstall, an LLM Engineer at Hugging Face and co-author of "Natural Language Processing with Transformers," dives into captivating discussions on topological machine learning and its applications. He contrasts open source and closed source LLMs, shedding light on their implications for security and collaboration. Tunstall shares insights on fine-tuning language models, innovative training techniques, and the importance of community-driven advancements in AI. His journey from Kaggle competitions to real-world applications offers valuable lessons for aspiring data scientists.

Guest Maria Vechtomova is a skilled ML Engineering Manager at Ahold Delhaize and co-founder of the Marvelous MLOps blog. She shares essential coding best practices for data scientists, emphasizing modularity and CI/CD pipelines. Maria discusses her experience deploying a fraud detection algorithm, highlighting the necessity of collaboration and infrastructure monitoring. Additionally, she dives into the distinct roles of ML and MLOps engineers and shares her journey in content creation, offering insights into building a community around MLOps.

Reah Miyara, an expert in LLMs evaluation at OpenAI with a rich background at Google and IBM, shares his career journey from software engineering to product leadership. He discusses the evolution of AI, focusing on the importance of validating innovations in real-world applications. Reah delves into the complexities of LLM evaluation and the significance of safety metrics in AI models. He emphasizes the vital role of feedback in career growth and offers insights into the future landscape of generative AI and its implications for society.

Erwin Huizenga, Machine Learning Lead at Google, discusses his journey from SAS and IBM to Google. Topics include early days of cloud computing, Gemini vs other LLMs, LLMOps, evaluating and monitoring LLMs, and deploying LLMs vs traditional ML models.

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 algorithms to enable autonomous systems. He finally gives his take on the future of autonomous vehicles and shares learnings from his experience in the field.  If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.Link to Train in Data courses (use the code AISTORIES to get a 10% discount): https://www.trainindata.com/courses?affcode=1218302_5n7krabaTo learn more about Perciv AI: https://www.perciv.ai/ Follow Andras on LinkedIn: https://www.linkedin.com/in/andraspalffy/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ---(00:00) - Intro(02:57) - Andras' Journey into AI (06:11) - Getting into Robotics (10:15) - Evolution of Computer Vision Algorithms(13:38) - PhD on Autonomous Driving & Road Users Detection(28:01) - Launching Perciv AI(35:19) - Augmenting Radars Performance with AI(44:45) - Inside Perciv AI: Roles, Challenges, and Stories(48:43) - Future of Autonomous Vehicles and Road Safety(51:46) - Solving a Technical Challenge with Camera Calibration(54:12) - Andras' First Self-Driving Car Experience(56:09) - Career Advice

Franziska Kirschner, Co-Founder of Intropy AI and former AI Lead at Tractable, discusses her impressive journey from physics to AI product management. She shares insights on launching AI tools for scrapyards and how these innovations enhance vehicle recycling. Franziska reflects on deep learning's impact in accident recovery and the complexities of bringing AI products to market. She also emphasizes building trust in AI adoption by engaging non-technical users, illustrating her passion for problem-solving and personal growth through unique experiences.