AI Stories

Our guest today is Ritchie Vink, CEO & Founder of Polars: an open source data manipulation library known for being extremely fast. As of today, polars has over 32k stars on github. In our conversation, Ritchie first explains how Polar which started as a side project evolved to what it is today. We then discuss the differences between Polars and Pandas, why Polars is fast and optimised and dig into Polars cloud: a platform which manages the compute infrastructure, allowing users to focus solely on writing queries. If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.

Our guest is Philippe Mizrahi, CEO of Linkup: a french startup building the world's best search agents. In our conversation, Philippe first shares how he got into search by building an internal dataset search tool at Lyft. We then dive into Linkup where Phil explains how linkup started, how it evolved and how they managed to build the best search agents achieving state-of-the-art results on OpenAI SimpleQA dataset. If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.

Our guest is Samuel Colvin, Co-Founder and CEO of pydantic: a data validation library with millions of downloads per month. In our conversation, we first discuss Pydantic and their observability platform: logfire. We then dive into agents where Samuel shares his vision on how to build production ready agents and what makes PydanticAI different than other frameworks. If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.

Our guest today is Thomas Scialom, Senior Staff Research Scientist at Meta. In our conversation, we first discuss Thomas' PhD where he explains how he managed to publish around 20 academic papers. We then dive into several LLMs that Thomas built at Meta including Galactica, Llama 2 and Llama 3. We finally dig into AI Agents, their limitations and how close we are to AGI and ASI. If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.

Our guest today is Başak Eskili, Machine Learning Engineer at Booking.com and C-Founder of Marvelous MLOps. In our conversation, we first dive into MLOps, its key components and how Başak got into the field. We then talk about Marvelous MLOps and her new course: "End to end MLOps with Databricks". Başak finally shares more about her current role at Booking with a focus on building feature stores. If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.From beginner to advanced LLM developer course by Towards AI (use the code AISTORIES10 to get a 10% discount): https://academy.towardsai.net/courses/beginner-to-advanced-llm-dev?ref=63e5e3To learn more about Marvelous MLOps: https://www.marvelousmlops.io/End to End MLOps course with Databricks: https://maven.com/marvelousmlops/mlops-with-databricksFollow Başak on LinkedIn: https://www.linkedin.com/in/ba%C5%9Fak-tu%C4%9F%C3%A7e-eskili-61511b58/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ---(00:00) Intro  (02:18) How Başak Got into AI & MLOps  (06:55) Key Components of MLOps  (12:05) Deploying First ML Model  (15:58) Joining Booking.com  (18:11) Best Practices for Building Scalable and Reliable ML Systems  (23:01) Databricks (27:50) Batch vs. Real-Time Predictions  (31:15) Marvelous MLOps  (33:52) Role at Booking.com  (35:45) Feature Stores (45:45) Career Advice  

Our guest today is Max Mergenthaler, Co-Founder and CEO of Nixtla: one of the most popular libraries for time series forecasting. In this conversation, Max first explains how he got into AI and the lessons he learned from building a couple of tech startups. We then dive into Nixtla and forecasting. Max explains how he founded Nixtla and the different libraries available to build stats, ml and deep learning forecasting algorithms. We also tallk about TimeGPT, Nixtla's closed-source foundation model for time series. We finally discuss the future of the field along with mistakes and best practices when working on forecasting projects. If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.To learn more about Nixtla: https://www.nixtla.io/ Open source librairies (StatsForecast, MLForecast, NeuralForecast): https://www.nixtla.io/open-source TimeGPT: https://github.com/Nixtla/nixtlaFollow Max on LinkedIn: https://www.linkedin.com/in/mergenthaler/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ---(00:00) - Intro(02:00) - How Max got into Data & AI(03:44) - Combining Philosophy with Analytics(09:49) - Lessons from building Startups(14:00) - Founding Nixtla(16:23) - Time Series Forecasting(19:25) - StatsForecast, MLForecast, and NeuralForecast(26:16) - TimeGPT & LLMs for Forecasting(34:30) - Why people love Nixtla(42:34) - Future of Forecasting(45:51) - Mistakes & Best Practices in Forecasting(52:12) - Max's role as CEO (56:09) - Career Advice

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/1633437167Blog post on Multimodal LLMs: https://magazine.sebastianraschka.com/p/understanding-multimodal-llmsLightning AI (with pytorch lightning and litgpt repos): https://github.com/Lightning-AIFollow Sebastian on LinkedIn: https://www.linkedin.com/in/sebastianraschka/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ---(00:00) - Intro(02:27) - How Sebastian got into Data & AI(06:44) - Regression and Loss Function(13:32) - Academia to Join 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

Our guest today is Loubna Ben Allal, Machine Learning Engineer at Hugging Face

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 

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_5n7krabaNatural Language Processing with Transformers book: https://www.oreilly.com/library/view/natural-language-processing/9781098136789/Giotto-tda library: https://github.com/giotto-ai/giotto-tdaKTO alignment paper: https://arxiv.org/abs/2402.01306Follow Lewis on LinkedIn: https://www.linkedin.com/in/lewis-tunstall/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ---(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

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_5n7krabaCheck 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/  ---(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

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_5n7krabaFollow Reah on LinkedIn: https://www.linkedin.com/in/reah/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ---(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 

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_5n7krabaErwin'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/  ---(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

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

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_5n7krabaFollow Franziska on LinkedIn: https://www.linkedin.com/in/frankirsch/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ---(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

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_5n7krabaCheck out Maxime's LLM course: https://github.com/mlabonne/llm-courseFollow 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

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_5n7krabaFollow 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

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_5n7krabaFollow 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

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_5n7krabaFollow 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/  ---(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

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_5n7krabaFollow Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ----(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

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_5n7krabaFollow Noah on LinkedIn: https://www.linkedin.com/in/noahgift/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ————(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

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_5n7krabaFollow Marianne on LinkedIn: https://www.linkedin.com/in/mborzic/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ————(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_5n7krabaFollow Christof on LinkedIn: https://www.linkedin.com/in/dr-christof-henkel-766a54ba/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ————(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 

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/  ————(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

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. If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.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

Our guest today is Rémi Ounadjela, Senior Data Science Manager at TikTok and ex-Data Scientist at Google and Amazon. During the first part of our conversation, Rémi talks about his experience working on shipment optimisation at Amazon and on Data Science for risk and safety at TikTok. During the second part, we discuss the differences between working as a Data Scientist at TikTok, Google and Amazon. Rémi also shares advice on how to get into Big Tech and the common mistakes that you should avoid. If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.Follow Rémi on LinkedIn: https://www.linkedin.com/in/remiounadjela/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ————(00:00) : Intro(01:34) : How Rémi got into the field(06:06) : How Rémi got into Amazon(08:36) : Data Science at Amazon and difference with ML engineering(20:00) : Machine Learning for shipment optimisation (25:44) : Success metrics(30:10) : Data Science for risk and safety at TikTok(41:43) : Amazon vs Google vs TikTok(49:10) : How to land a DS job in Big Tech(01:02:47) : Career advice

Our guest today is Barr Moses, Co-Founder & CEO of Monte Carlo, the first end-to-end data observability platform. In our conversation, we first talk about how Barr got into the field and the early influence of her parents. Barr shares her previous experiences working with data in the Israeli Army and working on data strategy at Bain. We then dig into Monte Carlo and the new field of DataOps along with data observability and its 5 pillars . Barr explains how and why she founded this company and walks us through the key challenges she faced. If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.To learn more about Monte Carlo: https://www.montecarlodata.com/Follow Barr on LinkedIn: https://www.linkedin.com/in/barrmoses/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ————(00:00) : Intro(01:18) : How Barr got into Data Science(03:09) : Data in the Israeli Army (08:24) : Influence from her parents(11:54) : Data Strategy and consulting at Bain(19:10) : How to quickly become an expert(25:30) : What is Monte Carlo(32:00) : DataOps & 5 pillars of data observability(43:54) : Challenges when building a tech company(49:57) : Mistakes and career advice

Our guest today is Parul Pandey, Principal Data Scientist at H2O.ai, Kaggle Grandmaster (notebooks) & book author of “Machine Learning for High Risk Applications”. In our conversation, we first dig into Kaggle. Parul explains how she became a Grandmaster, shares tips about data analysis and discusses the pros of learning on Kaggle. The second part of the episode is around machine learning for high risk applications. We talk about the risks of using AI to make decisions, talk about interpretable algorithms and give advice on how to deploy robust models. If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.Machine Learning for High Risk Applications: https://www.oreilly.com/library/view/machine-learning-for/9781098102425/Kaggle notebook (gold medal), "Geek Girls Rising : Myth or Reality!": https://www.kaggle.com/code/parulpandey/geek-girls-rising-myth-or-reality/notebookBlog post on Explainable Boosting Machines: https://towardsdatascience.com/the-explainable-boosting-machine-f24152509ebbFollow Parul on LinkedIn: https://www.linkedin.com/in/parulpandeyindia/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ————(00:00) : Intro(01:57) : How Parul got into AI(07:20) : Kaggle & Becoming a Grandmaster(13:52) : Advice for good data analysis(20:27) : Pros of learning on Kaggle(24:28) : ML for high risk applications(49:20) : Interpretable algorithms (55:28) : Deploying robust models(01:01:22) : Future of AI(01:05:47) : Career advice

Our guest today is Marijn Markus, Managing Data Scientist at Capgemini. In our conversation, we first talk about data analysis to model and visualise the spread of Ebola. We then dig into crime analysis back to when Marijn worked with the police in the Netherlands. Marijn also shares his thoughts on the importance of causal inference and the value that humans can add to AI algorithms. We then explore machine learning applied to the consulting sector and discuss the pros and cons of working as a Data Scientist in a consulting firm. Marijn talks about project FARM and explains how he helped farmers across the world using AI and Big Data. He then finally shares advice on how to have an impact on our world and progress in your career. If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.Follow Marijn on LinkedIn: https://www.linkedin.com/in/marijnmarkus/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ————(00:00) : Intro(02:16) : Modelling the spread of Ebola(10:47) : Crime analysis (20:20) : Causal Inference(27:24) : Diversity(32:19) : AI in consulting(39:54) : project FARM(51:27) : Making the world a better place with data(01:00:15) : Career advice

Our guest today is Louis Bouchard, founder of the What's AI Youtube channel & Towards AI. Louis is also a PhD student at the Quebec Artificial Intelligence Institute (Mila) founded by Yoshua Bengio.In our conversation, Louis first talks about What's AI and explains how and why he launched his own Youtube channel. We then explore the world of Research and AI Algorithms. We discuss some of the latest techniques, chat about what intelligence actually means and debate on whether AI algorithms are actually intelligent or not. We also dive into research papers where Louis shares advice on how to easily read and understand technical content. Finally, Louis explains how he got into the field, talks about his PhD and shares more about applying computer vision for medical images.  If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.Follow Louis on LinkedIn: https://www.linkedin.com/in/whats-ai/What's AI Youtube channel: https://www.youtube.com/@WhatsAITowards AI: https://towardsai.net/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ---(00:00) : Intro(02:16) : What's AI Youtube Channel(08:11) : AI Algorithms(12:10) : AI & Intelligence(18:58) : How to read research papers(35:47) : Latent Diffusion(41:06) : How Louis got into the field(54:25) : Master's research project(01:00:37) : Computer vision for medical imaging(01:12:24) : Career Advice

Our guest today is Miguel Fierro, Lead Data Scientist at Microsoft working in the personalisation team. In our conversation, we first dig into Miguel's PhD in robotics and the connection between robotics and AI.  Miguel then shares a few stories on reverse learning to learn faster, how to switch fields, taking initiatives, the importance of diversity and the power of practical knowledge. We finally talk about Data Science at Microsoft where Miguel talks about his career path and learnings he got from some of the best in the field.  If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.Follow Miguel on LinkedIn: https://www.linkedin.com/in/miguelgfierro/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ---00:00) : Intro(02:05) : How Miguel got into the field(10:30) : Robotics(16:03) : Taking initiatives(17:49) : Importance of Diversity(22:24) : Switching fields (27:30) : Reverse learning & Practical Knowledge(42:18) : Getting projects done(47:57) : Future of AI & Robotics(55:19) : Data Science at Microsoft(01:00:17) : Asking for help(01:11:52) : Miguel's path @Microsoft(01:13:45) : Career advice

Our guest today is Greg Coquillo, Technology Manager at Amazon and startup investor. Greg is also a LinkedIn Top Voice in Technology & Innovation (2021) and Data Science & AI (2020).In our conversation, Greg first explains how he got into the field and why he is so excited about working in the AI space. He also shares his view on Data Science and the skills required to become a great Data Scientist. We then dig into his career focusing on Lonza and Amazon. Greg talks about a price optimisation model he built to increase Lonza's profit margin by around 20%, generating additional millions in profit. He also shares his experience working as a Technology Manager at Amazon. We finally talk about ChatGPT, the future of AI and career advice.   If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.Follow Greg on LinkedIn: https://www.linkedin.com/in/greg-coquillo/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ---(00:00) : Intro(02:28) : How Greg got into the field(07:35) : Why working in AI is exciting(10:20) : Data Scientists vs Product Managers(19:46) : What makes a Good Data Scientist ? (28:21) : Transitioning to Product Manager at Lonza(35:38) : Price optimisation model(47:26) : The power of starting simple !(53:05) : ChatGPT(58:46) : Technology Manager at Amazon(01:02:50) : The future of AI (01:05:43) : Career advice

Our guest today is Mike Wimmer, an incredible 14 year old kid who is already the CEO of two companies: Next Era Innovation and Reflect Social. In our conversation, we first talk about Mike's childhood from starting to code at only 5 years old to his experience at school.  We then discuss his first company, Next Era Innovation, which he founded at 7. Mike shares a few projects that he worked on including an education app connected to a physical robot to help children interactively learn about the US presidents. We also dig into AI, machine learning and deep learning and share our thoughts on how to get started and where the field is heading. Mike finally talks about the internet of things (IoT) and his second company Reflect Social which develops a software to easily interact with IoT devices. If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.Follow Mike on LinkedIn: https://www.linkedin.com/in/mike-wimmer-9738b5167/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ---(00:00) : Intro(02:56) : School and coding (12:01) : CEO at 7 years old(15:13) : Next Era Innovation(24:22) : Learning(29:50) : Getting into AI and ML projects(36:55) : Reflect Social and the internet of things (IoT)(50:40) : Impact and prioritisation(53:36) : The future of AI(01:02:46) : Career advice

Our guest today is Adam Sroka, Founder and Director at Hypercube Consulting.In our conversation, Adam first explains how he got into the field. He then shares management mistakes and advice from his experience at Incremental Group where he transitioned from Senior Data Scientist to Data & AI Director. We then dig into ML applied to the energy sector and talk about Adam's new company: Hypercube Consulting. We finally discuss data strategy, how to build strong data teams and the importance of writing online.   If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.Follow Adam on LinkedIn: https://www.linkedin.com/in/aesroka/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ---(00:00) : Intro.(02:06) : How Adam got into Data Science.(07:38) : ML in startups.(20:12) :  From senior DS to AI Director @Incremental Group.(27:18) : Management mistakes & advice.(35:38) : ML applied to the energy sector @Origami.(44:21) : Building a data consulting company. (49:04) : Data strategy.(55:24) : Building strong data teams .(59:17) : Career advice.

Our guest today is Kyle Kranen, Senior Deep Learning Algorithm Engineer at Nvidia.In our conversation, Kyle first explains how he got into the field and talks about his internship at Condati where he worked on Data Science and visualisation applied to marketing. We then dig into deep learning and dive into Kyle's first project at Nvidia, working with Mask R-CNN in the deep learning algorithm team. We finally explore graph neural networks which have recently become quite popular. Kyle explains the theory behind those algorithms and gives clear examples on how they can be applied in industry. If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.Follow Kyle on LinkedIn: https://www.linkedin.com/in/kyle-kranen/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ---(00:00) : Intro(01:41) : How Kyle got into machine learning(09:16) : Data Science applied to Marketing @Condati(15:22) : Deep learning(24:10) : Kyle's first project working on Mask R-CNN @NVIDIA(30:02): Deep learning algorithms team @NVIDIA(35:12) : Debugging models(41:00) : Graph Neural Networks(58:00) : Career advice

Our guest today is Jess Ramos, Senior Data Analyst at Crunchbase and LinkedIn Learning Instructor. In our conversation, Jess first talks about how she got into the field and her first analytics project.  We then dig into her experience at FormFree where Jess worked on tracking the performance of integration partners. We discuss the value that data analysts can bring to a business and share some tips on how to process and clean your data.  Jess finally shares advice on how to land a job in data analytics / data science and explains how LinkedIn completely changed her life.If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.Follow Jess on LinkedIn: https://www.linkedin.com/in/jessramosmsba/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ---(00:00) : Intro. (02:00) : Jess's first data project. (11:07) : Data analytics @FormFree. (14:22) : The impact of building robust monitoring.  (22:23) : Academia vs industry. (25:50) : How to clean and process your data. (28:50) : Management - mistakes & advice. (39:43) : How to land a data job ?(52:31) : The power of LinkedIn. (57:57) : Career advice. 

Our guest today is Soledad Galli, Lead Data Scientist and Founder of Train In Data. In our conversation, we first talk about Soledad's transition from working as a research scientist in biology to working as a Data Scientist in industry. We then dig into Soledad's experiences at Zopa and LV= where she built machine learning algorithms for credit risk and fraud detection. We explore the challenges behind working in credit risk and discuss different methods to deal with imbalanced data and identify frauds. We finally talk about train in data, a platform with intermediate and advanced data science courses. Soledad explains what she likes about teaching and shares advice on how to uplift your data science skills. If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.Follow Soledad on LinkedIn: https://www.linkedin.com/in/soledad-galli/Improve you Data Science skills with Train In Data: https://www.trainindata.com/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/ --(00:00) : Intro.(02:09) : From Biology to Data Science. (11:01) : Building credit risk models at Zopa. (21:34) : Fraud detection at LV=. (28:18) : Dealing with imbalanced data. (35:32) : ML tips when working in industry. (41:36) : Teaching Data Science. (52:59) : Mistakes, advice and career progression. 

Our guest today is Santiago Valdarrama, Director of Computer Vision at Levatas and founder of Bnomial. In our conversation, we first talk about Santiago's transition from software engineering to the world of machine learning. He explains how he got into AI and why software engineering skills are important when working as a Data Scientist / ML engineer. We then dig into computer vision at Levatas, a company which develops solutions for automating and scaling visual inspection. Santiago walks us through different machine learning projects that he worked on, summarises the latest advances in computer vision and shares his thoughts on the future of the field. We finally talk about Bnomial, a website created by Santiago which posts one machine learning questions every day. Santiago explains the importance of building good habits and shares advice on how to progress in your career. If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.Follow Santiago on LinkedIn: https://www.linkedin.com/in/svpino/Follow Santiago on Twitter: https://twitter.com/svpinoSubscribe to Santiago's Youtube channel: https://www.youtube.com/c/Santiagox0 Answer one machine learning question every day on Bnomial: https://today.bnomial.com/ Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/--(00:00) : Intro. (03:45) : How did you get into the field ? (07:53) : Example of problems solved using AI. (12:47) : Software engineering skills for Data Scientists / ML engineers.(20:08) : Computer vision at Levatas.(28:16) : Management. (31:43) : Common mistakes made by Data Scientists / ML engineers.(42:00) : Computer vision & the future of the field.(47:55) : Bnomial.(58:57) : One advice to progress in your career. 

Our guest today is Damien Benveniste, CTO at Motivee and Ex Tech Lead at Meta. In our conversation, Damien first explains how he transitioned from a PhD in Physics to the world of AI and talks about the differences between machine learning in industry and doing a PhD. He also shares his experience getting laid off three times in a row and gives advice on how to do well in industry, how to choose the right company to work for and how to keep moving forward.  We then discuss Machine learning for ads ranking and talk about deep learning architecture optimisation. Damien explains how he made Meta earn millions of dollars by improving the performance of machine learning models. We finally talk about Motivee, a tech startup developing a smart decision insight tool powered by employee listening. We dig into some of the challenges when building a startup like the need to satisfy clients while keeping investors happy. Damien finally shares advice on how to progress in your career. If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.Follow Damien on LinkedIn: https://www.linkedin.com/in/damienbenveniste/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/--(00:00) : Intro(02:10) : How did Damien get into the field ? (07:28) : Why AI ?  (13:51) : ML in industry vs PhD(32:46) : Choosing the right company to work for(38:36) : Getting laid off(52:30) : Machine learning for ads ranking at Meta(1:09:21) : $$ value of deep learning algorithms optimisation(1:20:26) : ML at Meta vs ML in Startups(1:26:03) : Founding a tech Startup (Motivee)(1:47:28) : One advice to progress in your career

Our guest today is Marguerite Graveleau, Technical Product Manager at Iwoca; a Fintech company lending money to small and medium businesses; and Ex Data Scientist at Lyft, one of the main competitors of Uber. In our conversation, Marguerite first explains how she got into the field. We talk about her Master at Stanford and give advice on how to get a first job after university. We discuss the importance of cold emails and building a portfolio of Data Science projects. We then explore Data Science at Lyft where Marguerite explains how she used data and econometric models to maintain a market equilibrium between the demand for rides and the number of available drivers. We finally talk about strategy and AI at Iwoca. We dive into the importance of monitoring, talk about A/B testing but also discuss what it feels like to work in a fast paced credit risk company. If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel. Follow Marguerite on LinkedIn: https://www.linkedin.com/in/margueritegraveleau/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/--[00:00] : Intro[02:40] : How did you get into the field ?[06:03] : How to get your first job ?[12:29] : Data Science at Lyft.[23:56] : Doing an MBA.[29:09] : What is Iwoca ?[32:22] : Operation Strategy at Iwoca. [37:54] : The importance of monitoring.[40:50] : Challenges of working in a credit risk company.[44:56] : A / B tests.[49:10] : Trying multiple different jobs.[54:16]: What is a good Data Scientist ?[58:36]: One advice to progress in your career.

Our guest today is Aleksa Gordic, Research Engineer at Deepmind and founder of the AI Epiphany Youtube channel. In our conversation, we first talk about Aleksa's Bachelor in Electronics and Computer Science at the University of Belgrade and then discuss his experience as a Software Engineer at Microsoft. He then explains how he transitioned to the world of AI by doing courses, bootcamps and learning subfields like GANs, Neural Style Transfer or Reinforcement Learning on his own. He shares his learning strategy and gives advice on how to get into the field. We finally talk about Deepmind, how Aleksa managed to get an offer and his latest work on Flamingo, an impressive visual language model which sets state-of-the-art results in few shots learning on a wide range of tasks. If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.Follow Aleksa on LinkedIn: https://www.linkedin.com/in/aleksagordic/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/Useful resources:- Getting started in AI by Aleksa Gordic- Learning to Learn Coursera course- Flamingo blog post——[00:00] : Intro[02:50] : Bachelor in Electronics and Computer Science[05:51] : Transitioning to Software Engineering[07:40] : Fighting the imposter syndrome[12:49] : Software Engineer at Microsoft[21:07] : How Aleksa got into AI and ML[25:04] : Intelligence[28:36] : ML courses and Hackathons[33:37] : Productivity[38:04] : Learning subfields of AI in depth[46:36] : Advice to get started in the field[50:18] : Getting into Deepmind[55:27] : Working at Deepmind[58:06] : Flamingo and other algorithms[01:08:19] : Challenges and mistakes faced[01:13:10]: What is a good ML engineer ? [01:21:23] : One advice to progress in your career

Our guest today is Smriti Mishra, applied AI and Data Science Researcher at the KTH Royal Institute of Technology in Stockholm and ex Head of AI at Earthbanc. In our conversation, we first talk about Smriti's career, how she transitioned from electrical engineering to the world of Data Science and AI and how she became passionate about healthcare and climate change. Smriti then describes her experience in tackling climate change with AI. She introduces the concept of carbon sequestration and explains how she estimates it using deep learning algorithms, satellite images and additional data sources. We finally talk about her current research, AI for diversity but also share advice on how to get into the field and how to become a better Data Scientist.If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.Follow Smriti on LinkedIn: https://www.linkedin.com/in/smritimishra/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/----[00:00] : Intro[02:51] : Smriti's bachelor in electronics[05:58] : 2 events which made Smriti get into Data science[11:43] : Switching from AI in healthcare to AI for climate change[16:34] : How Earthbanc tackles climate change[19:38] : The concept of carbon sequestration[25:09] : Earthbanc[26:53] : Predicting carbon sequestration using deep learning[37:44] : AI in research vs AI in industry[40:23] : AI for diversity[46:27] : Getting into Data Science[49:10] : What is a good Data Scientist[51:00] : One advice to progress in your career

Our guest today is Joshua Starmer, Founder and CEO of StatQuest, a Youtube channel with over 700k subscribers which clearly explains complex statistics and machine learning methods.In our conversation, Josh first talks about his career: his PhD in Bioinformatics, how he got introduced to the concepts of Statistics and variation and how he learned more about it. We then explore StatQuest, how the channel started and discuss the difficulties behind leaving a full time job to start your own project. We also explain what the channel is about and why it became so successful. We finally talk about Josh's new book "The StatQuest Illustrated Guide to Machine Learning" and give advice on how to make progress in Data Science. If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel. Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/----[00:00] : Intro[01:57] : Bachelor Degree in Computer Science and Music[05:59] : Why choosing Computer Science over Music ?[09:55] : Why doing a PhD in Bioinformatics ?[13:17] : Being your own boss[18:31] : How Josh got introduced to the world of Data Science and Statistics[24:50] : Caring about general concepts rather than focusing on the details[27:36] : Working in a research lab[30:55] : How StatQuest started[38:47] : What is StatQuest ? [41:47] : Why is StatQuest so successful ?[46:10] : How to build a successful Youtube channel[51:41] : The StatQuest Illustrated Guide to Machine Learning (new book)[56:37] : One advice for someone to progress in their career

Our guest today is Jan Xu, Senior Machine Learning Engineer at Deep Render, a startup developing the next generation of compression technology using Deep Learning. This episode is a special one since Jan and I studied Civil Engineering at Imperial College London together between 2015 and 2019. In our conversation, we first talk about our bachelor degree at Imperial and how Jan made the transition from Engineering to Data Science and AI. He explains how he learned to code, the first machine learning projects that he worked on and how he managed to get a Job as a Machine Learning Engineer without any bachelor, master or even PhD in the field. We then dig further into Deep Learning applied to computer vision where Jan describes how Deep Render uses an algorithm called Autoencoder which improves video compression and outperforms traditional approaches. He also clarifies why video compression is such an interesting problem and explains how AI can help tackle this issue. If you enjoyed the episode, please subscribe to my Youtube channel: https://www.youtube.com/channel/UCWvn6k4aeceyYKjAleML9VA

Our guest today is Mark Freeman II, Senior Data Scientist at Humu and CEO of On the Mark Data. In our conversation, Mark first talks about his Bachelor in Sociology and his Master at Stanford. He explains how he managed to get into Stanford despite having bad grades and describes how he got into the world of Statistics and AI. We then discuss Data Science in Startups, the pros and cons of working in a Startup and the main differences between Data Science in small and large companies. Mark shares projects that he worked on and explains how he almost made his company lose $1 Million. We finally dive into the world of entrepreneurship and AI. We talk about On the Mark Data; Mark's latest Startup where he works on consulting and content creation. Mark shares his experience, his failures and advice on how to do well as a Data Scientist. If you enjoyed the episode, please subscribe to my Youtube channel: https://www.youtube.com/channel/UCWvn6k4aeceyYKjAleML9VA

Our guest today is Rishabh Mehrotra, ex Tech Lead at Spotify and Director of Machine Learning at ShareChat. In our conversation, Rishabh first talks about his transition from working as an analyst at Goldman Sachs to his PhD at UCL where he worked on deep models for user understanding, knowledge discovery and decision optimization. We then explore Machine Learning at Spotify which Rishabh joined in 2017 as a founding member of the research lab. We dig into recommender systems and discuss how AI can be used to improve user experience by understanding their behaviours and recommending personalised songs / playlists. Rishabh also talks about his new role at ShareChat, a leading Indian social media platform with over 300 million users. We discuss the impact of machine learning on the creator economy, the main challenges, the importance of metrics to train and monitor ML models but also share advice on how to become a good Data Scientist / ML Engineer and progress in your career. If you enjoyed the episode, please subscribe to my Youtube channel: https://www.youtube.com/channel/UCWvn6k4aeceyYKjAleML9VA 

Our guest today is Christina Stathopoulos, Analytical Lead at Waze and Adjunct Professor at IE University in Madrid. In our conversation,  Christina first shares her opinion on her MSc in Business Analytics and Big Data at IE university and discusses the pros and cons of an in-person vs an online master. Christina then talks about her transition into teaching, the challenges that she faced as well as advice that she has on how to start learning more about Data Science and Machine Learning. We also talk about the #bookaweekchallenge and discuss the importance of building a personal brand. We then focus on AI at Waze, a navigation app bought by Google in 2013 for around $1 billion.  Christina explains how she got into Google and why Machine Learning plays such an important role. We finally outline the importance of diversity in tech teams and share advice on what "tech minorities" such as women can do to break into the field and reject the "imposter syndrome".  Follow Christina on LinkedIn: https://www.linkedin.com/in/christinastathopoulos/Subscribe to my Youtube channel: https://www.youtube.com/channel/UCWvn6k4aeceyYKjAleML9VA

Our guest today is Serg Masís, author of the book "Interpretable Machine Learning with Python" and Data Scientist at Syngenta: a leading agriculture company helping to improve global food security. In our conversation, we first talk about Serg's transition from working as a web developer to joining the world of Data Science and AI where he explains why he decided to do a master in Data Science after working in industry for over 10 years. We also discuss AI at Syngenta and explore the main challenges behind AI for agronomy. We then dig into Interpretable Machine Learning, a topic that Serg is particularly interested in. We discuss what interpretable machine learning actually means, explain different approaches and give real world examples. We finally talk about Serg's career, mistakes that he made and share advice on how to become a great Data Scientist. Follow Serg on Linkedin: https://www.linkedin.com/in/smasis/Subscribe to my Youtube channel: https://www.youtube.com/channel/UCWvn6k4aeceyYKjAleML9VA