46 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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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.
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_5n7kraba
Erwin's LLMOps coursera course: https://www.deeplearning.ai/short-courses/llmops/
Follow Erwin on LinkedIn: https://www.linkedin.com/in/erwinhuizenga/
Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/
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(00:00) - Intro
(05:04) - Early Experiences
(15:51) - Joining Google
(20:20) - Early Days of Cloud Computing
(26:18) - Advantages of Cloud Infrastructure
(30:09) - Gemini and its Launch
(37:32) - Gemini vs Other LLMs
(46:15) - LLMOps
(50:50) - Evaluating and Monitoring LLMs
(57:34) - Deploying LLMs vs Traditional ML Models
(01:01:07) - Personal Stories and Career Insights -
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 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_5n7kraba
To 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/
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(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 -
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 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_5n7kraba
Follow Franziska on LinkedIn: https://www.linkedin.com/in/frankirsch/
Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/
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(00:00) - Introduction
(03:08) - Franziska's Journey into AI
(05:17) - Franziska's PhD in Condensed Matter Physics
(15:12) - Transition from Physics to AI
(19:20) - Deep Learning & Impact at Tractable
(33:21) - AI Researcher vs AI Product Manager
(37:52) - The Impact of AI on Scrapyards
(43:14) - Key Steps in Launching New AI Products
(53:31) - Founding Intropy AI
(01:00:37) - The Potato Travels
(01:04:10) - Advice for Career Progression -
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 fine-tuning, QLoRA & LoRA, DPO vs RLHF and how to deploy LLMs in production.
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_5n7kraba
Check out Maxime's LLM course: https://github.com/mlabonne/llm-course
Follow Maxime on LinkedIn: https://www.linkedin.com/in/maxime-labonne/
Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/
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(00:00) - Intro
(02:37) - From Cybersecurity to AI
(06:05) - GenAI at Airbus
(13:29) - What does Maxime use ChatGPT for?
(15:31) - Getting into GenAI and learning resources
(22:23) - Steps to build your own LLM
(26:44) - Pre-training
(29:16) - Supervised fine-tuning, QLoRA & LoRA
(34:45) - RAG vs fine-tuning
(37:53) - DPO vs RLHF
(41:01) - Merging Models
(45:05) - Deploying LLMs
(46:52) - Stories and career advice -
From Biostatistician to DevRel at Deci AI with Harpreet Sahota #42
Our guest today is Harpreet Sahota, Deep Learning Developer Relations Manager at Deci AI.
In our conversation, we first talk about Harpreet’s work as a Biostatistician and dive into A/B testing. We then talk about Deci AI and Neural Architecture Search (NAS): the algorithm used to build powerful deep learning models like YOLO-NAS. We finally dive into GenAI where Harpreet shares 7 prompting tips and explains how Retrieval Augmented Generation (RAG) works.
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_5n7kraba
Follow Harpreet on LinkedIn: https://www.linkedin.com/in/harpreetsahota204/
Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/
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(00:00) - Intro
(02:34) - Harpreet's Journey into Data Science
(07:00) - A/B Testing
(17:50) - DevRel at Deci AI
(26:25) - Deci AI: Products and Services
(32:22) - Neural Architecture Search (NAS)
(36:58) - GenAI
(39:53) - Tools for Playing with LLMs
(42:56) - Mastering Prompt Engineering
(46:35) - Retrieval Augmented Generation (RAG)
(54:12) - Career Advice -
Building AI Startups & Raising Funds with Ryan Shannon #41
Our guest today is Ryan Shannon, AI Investor at Radical Ventures, a world-known venture capital firm investing exclusively in AI. Radical's portfolio includes hot startups like Cohere, Covariant, V7 and many more.
In our conversation, we talk about how to start an AI company & what makes a good founding team. Ryan also explains what he and Radical look for when investing and how they help their portfolio after the investment. We finally chat about some cool AI Startups like Twelve Labs and get Ryan’s predictions on hot startups in 2024.
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_5n7kraba
Follow Ryan on LinkedIn: https://www.linkedin.com/in/ryan-shannon-1b3a7884/
Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/
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(0:00) - Intro
(2:42) - Ryan's background and journey into AI investing
(11:15) - Radical Ventures
(14:34) - How to keep up with AI breakthroughs?
(22:42) - How Ryan finds and evaluates founders to invest in
(32:54) - What makes a good founding team?
(38:57) - Ryan's role at Radical
(45:53) - How to start an AI company
(50:22) - Twelve Labs
(59:19) - Future of AI and hot startups in 2024
(1:09:48) - Career advice