エピソード
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Our guest today is Sebastian Raschka, Senior Staff Research Engineer at Lightning AI and bestselling book author.
In our conversation, we first talk about Sebastian's role at Lightning AI and what the platform provides. We also dive into two great open source libraries that they've built to train, finetune, deploy and scale LLMs.: pytorch lightning and litgpt.
In the second part of our conversation, we dig into Sebastian's new book: "Build and LLM from Scratch". We discuss the key steps needed to train LLMs, the differences between GPT-2 and more recent models like Llama 3.1, multimodal LLMs and the future of the field.
If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.
Build a Large Language Model From Scratch Book: https://www.amazon.com/Build-Large-Language-Model-Scratch/dp/1633437167
Blog post on Multimodal LLMs: https://magazine.sebastianraschka.com/p/understanding-multimodal-llms
Lightning AI (with pytorch lightning and litgpt repos): https://github.com/Lightning-AI
Follow Sebastian on LinkedIn: https://www.linkedin.com/in/sebastianraschka/
Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/
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(00:00) - Intro(02:27) - How Sebastian got into Data & AI
(06:44) - Regressions and loss functions
(13:32) - Academia to joining LightningAI
(21:14) - Lightning AI VS other cloud providers
(26:14) - Building PyTorch Lightning & LitGPT
(30:48) - Sebastian’s role as Staff Research Engineer
(34:35) - Build an LLM From Scratch
(45:00) - From GPT2 to Llama 3.1
(48:34) - Long Context VS RAG
(56:15) - Multimodal LLMs
(01:03:27) - Career Advice
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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/cosmopedia
StarCoder blog post: https://huggingface.co/blog/starcoder
Follow Loubna on LinkedIn: https://www.linkedin.com/in/loubna-ben-allal-238690152/
Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/
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(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
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エピソードを見逃しましたか？
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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/1710.10903
ETA Prediction with Graph Neural Networks in Google Maps: https://arxiv.org/abs/2108.11482
TacticAI: an AI assistant for football tactics (with Liverpool FC): https://arxiv.org/abs/2402.01306
Follow Petar on LinkedIn: https://www.linkedin.com/in/petarvelickovic/
Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/
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(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
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Our guest today is Lewis Tunstall, LLM Engineer and researcher at Hugging Face and book author of "Natural Language Processing with Transformers".
In our conversation, we dive into topological machine learning and talk about giotto-tda, a high performance topological ml Python library that Lewis worked on. We then dive into LLMs and Transformers. We discuss the pros and cons of open source vs closed source LLMs and explain the differences between encoder and decoder transformer architectures. Lewis finally explains his day-to-day at Hugging Face and his current work on fine-tuning LLMs.
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
Natural Language Processing with Transformers book: https://www.oreilly.com/library/view/natural-language-processing/9781098136789/
Giotto-tda library: https://github.com/giotto-ai/giotto-tda
KTO alignment paper: https://arxiv.org/abs/2402.01306
Follow Lewis on LinkedIn: https://www.linkedin.com/in/lewis-tunstall/
Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/
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(00:00) - Intro(03:00) - How Lewis Got into AI
(05:33) - From Kaggle Competitions to Data Science Job
(11:09) - Get an actual Data Science Job!
(15:18) - Deep Learning or Excel?
(19:14) - Topological Machine Learning
(28:44) - Open Source VS Closed Source LLMs
(41:44) - Writing a Book on Transformers
(52:33) - Comparing BERT, Early Transformers, and GPT-4
(54:48) - Encoder and Decoder Architectures
(59:48) - Day-To-Day Work at Hugging Face
(01:09:06) - DPO and KTO
(01:12:58) - Stories and Career Advice
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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 engineer.
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 Marvelous MLOps: https://marvelousmlops.substack.com/
Follow Maria on LinkedIn: https://www.linkedin.com/in/maria-vechtomova/
Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/
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(00:00) - Intro(02:59) - Maria’s Journey to MLOps
(08:50) - Code Best Practices
(18:39) - MLOps Infrastructure
(29:10) - ML Engineering for Fraud Detection
(40:42) - Content Creation & Marvelous MLOps
(49:01) - ML Engineer vs MLOps Engineer
(56:00) - Stories & Career Advice
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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 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 Reah on LinkedIn: https://www.linkedin.com/in/reah/
Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/
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(00:00) - Intro(03:09) - Getting into AI and Machine Learning
(08:33) - Why Stay in AI?
(11:39) - From Software Engineer to Product Manager
(18:27) - Experience at Google
(25:28) - Applications of Graph ML
(31:10) - Joining OpenAI
(35:15) - LLM Evaluation
(44:30) - The Future of GenAI and LLMs
(55:48) - Safety Metrics for LLMs
(1:00:30) - Career Advice
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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
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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
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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
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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/---
(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
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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/---
(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
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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/---
(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
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Our guest today is Christoph Molnar, expert in Interpretable Machine Learning and book author.
In our conversation, we dive into the field of Interpretable ML. Christoph explains the difference between post hoc and model agnostic approaches as well as global and local model agnostic methods. We dig into several interpretable ML techniques including permutation feature importance, SHAP and Lime. We also talk about the importance of interpretability and how it can help you build better models and impact businesses.
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 Christoph on LinkedIn: https://www.linkedin.com/in/christoph-molnar/
Check out the books he wrote here: https://christophmolnar.com/books/
Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/
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(00:00) - Introduction(02:42) - Christoph's Journey into Data Science and AI
(07:23) - What is Interpretable ML?
(18:57) - Global Model Agnostic Approaches
(24:20) - Practical Applications of Feature Importance
(28:37) - Local Model Agnostic Approaches
(31:17) - SHAP and LIME
(40:20) - Advice for Implementing Interpretable Techniques
(43:47) - Modelling Mindsets
(48:04) - Stats vs ML Mindsets
(51:17) - Future Plans & Career Advice
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Our guest today is Demetrios Brinkmann, Founder and CEO of the MLOps Community.
In our conversation, Demetrios first explains how he transitioned from being an English teacher to working in sales and then founding the MLOps community. He also talks about the role of MLOps in the ML lifecycle and shares a bunch of resources to level up your MLOps skills. We then dive into the hot topic of GenAI and LLMOps where Demetrios shares his view on specialised vs generalised LLMs and why it can be dangerous to build a startup on top of OpenAI.
Demetrios finally explains what the MLOps community is all about. They are organising live events in around 40 countries, a great podcast, a slack channel, some new courses on generative AI and much more. Check out there website here: https://mlops.community/
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 Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/
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(00:00) - Introduction(01:50) - From English Teacher to MLOps
(08:32) - How to get into MLOps
(12:46) - MLOps and the ML Lifecycle
(22:54) - GenAI & LLMOps
(32:32) - Business Implications of Relying on OpenAI
(35:32) - The MLOps Community
(43:03) - Career Advice: The Power of Writing
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Our guest today is Noah Gift, MLOps Leader and award winning book author. Noah has over 30 years of experience in the field and has taught to hundreds of thousands of students online.
In our conversation, we first talk about Noah's experience building data pipelines in the movie industry and his experience in the startup world. We then dive into MLOps. Noah highlights the importance of MLOps, outlines the Software Engineering best practices that Data Scientists must learn and explains why we shouldn't always use Python. Noah finally shares his thoughts on the difference between MLOps and LLMOps, Python vs Rust and the future of 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
Follow Noah on LinkedIn: https://www.linkedin.com/in/noahgift/
Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/
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(00:00) - Intro(02:14) - Building data pipelines in the film industry
(11:47) - Noah's experience in Startups
(17:57) - What is MLOps?
(20:52) - Why should Data Scientists learn Software Engineering?
(27:59) - Importance of MLOps
(30:54) - Rust vs Python
(43:48) - Why we shouldn't always use Python
(49:26) - Difference between LLMOps and MLOps
(53:50) - Security and ethical concerns with LLMOps
(56:27) - The future of the field
(01:08:41) - Career advice
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Our guest today is Marianne Ducournau, Head of Data Science at Qonto and ex Data Scientist at Amazon and Uber.
In our conversation, we first discuss Marianne's first job in Data Science working in the public sector and managing a 10-15 people team. Marianne then talks about her experience at Uber and shares various projects that she worked on. We dive into price elasticity modelling and financial forecasting where her team built thousands of model to forecast financial metrics in multiple cities. Marianne finally explains her current role as the Head of Data Science at Qonto and gives advice on how to progress in Big Techs and in your career.
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 Marianne on LinkedIn: https://www.linkedin.com/in/mborzic/
Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/
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(00:00) - Introduction
(02:12) - Marianne's Journey Into Data Science
(05:05) - Managing A 10-15 People Team In Her First Job
(10:02) - Pros And Cons Of Working In The Public Sector
(16:51) - Transition From The Public Sector To Uber
(22:25) - Price Elasticity Modelling
(35:42) - Building 1000+ Models For Financial Forecasting
(42:10) - Progressing In Big Techs
(45:01) - What Is Qonto And Marianne's Role There?
(48:08) - Understanding Qonto's Product
(49:29) - Building A Team As Head Of Data Science
(54:37) - Impact Estimation
(01:02:52) - Marianne's Advice For Career Progression -
Our guest today is Christof Henkel, Senior Deep Learning Data Scientist at NVIDIA and world number 1 on Kaggle: a competitive machine learning platform.
In our conversation, we first discuss Christof's PhD in mathematics and talk about the importance of maths in a Data Science career. Christof then explains how he started on Kaggle and how he progressed on the platform to become the world number 1 amongst millions of users. We also dive into recent competitions that he won and the algorithms that he used. Christof finally gives many advice on how to win Kaggle competitions and progress in your career.
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 Christof on LinkedIn: https://www.linkedin.com/in/dr-christof-henkel-766a54ba/
Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/
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(00:00) - Introduction(03:00) - How Christof Got Into The Field
(07:59) - The Role of Mathematics In Data Science Careers
(12:27) - Why Christof Joined Kaggle And How?
(21:11) - Reducing Model Overfitting
(27:03) - Three Steps To Succeed On Kaggle
(33:56) - Kaggle VS Applied Machine Learning In Industry
(40:12) - How He Became World Number 1
(46:02) - A Recent Competition That He Won
(56:59) - His Role At NVIDIA
(01:01:24) - Startup Experience
(01:06:43) - Career Advice
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Our guest today is Davis Blalock, Research Scientist and first employee of Mosaic ML; a startup which got recently acquired by Databricks for an astonishing $1.3 billion.
In our conversation, we first talk about Davis' PhD at MIT and his research on making algorithms more efficient. Davis then explains how and why he joined Mosaic and shares the story behind the company. He dives into the product and how they evolved from focusing on deep learning algorithms to generative AI and large language models.
If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.
Follow Davis on LinkedIn: https://www.linkedin.com/in/dblalock/
Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/
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(00:00) - Intro
(01:40) - How Davis entered the world of Data and AI?
(03:30) - Enhancing ML algorithms' efficiency
(12:50) - Importance of efficiency
(16:37) - Choosing MosaicML over starting his own startup
(25:30) - What is Mosaic ML?
(37:34) - How did the rise of LLM aid MosaicML's growth?
(46:54) - $1.3 billion acquisition by Databricks
(48:52) - Learnings and failures from working in a startup
(01:00:05) - Career advice -
Our guest today is Kellin Pelrine, Research Scientist at FAR AI and Doctoral Researcher at the Quebec Artificial Intelligence Institute (MILA).
In our conversation, Kellin first explains how he defeated a superhuman Go-playing AI engine named KataGo 14 games to 1. We talk about KataGo’s weaknesses and discuss how Kellin managed to identify them using Reinforcement Learning.
In the second part of the episode, we dive into Kellin’s research on building practical AI systems. We dig into his work on misinformation detection and political polarisation and discuss why building stronger models isn’t always enough to get real world impact.
If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.
Follow Kellin on LinkedIn: https://www.linkedin.com/in/kellin-pelrine/
Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/————
(00:00) - Intro
(01:54) - How Kellin got into the field
(03:23) - The game of Go
(06:10) - Lee Sedol vs AlphaGo
(11:42) - How Kellin defeated KataGo 14 -1
(26:24) - Using AI to detect KataGo’s weaknesses
(37:07) - Kellin’s research on building practical AI systems
(43:10) - Misinformation detection
(49:22) - Political polarisation
(54:39) - ML in Academia vs in Industry
(1:06:03) - Career Advice
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Our guest today is Chanuki Seresinhe, head of Data Science at Zoopla, a company which provides millions of users with access to properties for sale and for rent.
In our conversation, we first talk about Chanuki’s PhD where she used machine learning to identify relationships between beautiful places and happiness. We then dive into Data Science at Zoopla and talk about Generative AI and other exciting projects that Chanuki is currently working on. Throughout the episode, Chanuki shares great insights on why ML projects fail, the importance of good metrics, switching companies and how to progress in your career.
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Follow Chanuki on LinkedIn: https://www.linkedin.com/in/chanukiseresinhe/
Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/————
(00:00) : Intro(01:23) : How Chanuki got into the field
(04:58) : AI to better understand happiness
(16:37) : Generative AI
(21:26) : Generative AI vs supervised learning
(24:47) : Data Science at Zoopla
(31:46) : The importance of good metrics
(35:33) : Dealing with outliers
(39:41) : Why ML projects fail
(46:30) : Switching companies
(48:42) : Bias
(54:47) : Career advice
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