AI Portfolio Podcast

Philip Rathle, the Chief Technical Officer of Neo4j, the popular graph database company which has now taken off by storm because of GraphRag, a new approach for making LLM Retrieval Augmented Generation applications more accurate by leveraging graphs, so you know today will be all about GraphRag and its impact on the market.Chapters:00:00 Intro02:09 Is AI Resurgence of Graph tech?03:46 GraphRAG popularity05:39 Top Use Cases in GenAI11:08 Gen AI in supply chain16:46 Graph and its types in enterprise24:03 GraphRag25:25 GNNs in GraphRAG29:30 Graphs are eating the world35:16 Knowledge Graph36:06 Drawbacks of vector based rag37:43 Neo4j vector database41:27 Filtering with Knowledge Graph45:02 Execution Time of LLMs49:03 Does longer prompts mean longer graph query?54:26 Scale of Graph57:05 Marriage of Graphs and Vectors59:46 Fine Tuning with Graphs01:00:46 Graphs Use less tokens01:02:46 Multiple vs One GraphRAG01:05:38 Updating Knowledge in Graph01:10:50 large Vs small models01:13:09 MultiModal GraphRAG01:15:36 Graphs in Robotics01:17:11 Neo4j journey01:20:03 Phillip Linkedin Post01:21:56 What's different with AI01:23:31 Advice for Gen AI startups01:26:00 CTO advice01:29:36 Chemical Engineering01:32:00 Career optimization function01:35:00 Book Recommendations01:37:06 Rapid Round

Get 1000 free inference requests for LLMs on build.nvidia.comKyle Kranen, an engineering leader at NVIDIA, who is at the forefront of deep learning, real-world applications, and production. Kyle shares his expertise on optimizing large language models (LLMs) for deployment, exploring the complexities of scaling and parallelism.📲 Kyle Kranen Socials:LinkedIn: https://www.linkedin.com/in/kyle-kranen/Twitter: https://x.com/kranenkyle📲 Mark Moyou, PhD Socials:LinkedIn: https://www.linkedin.com/in/markmoyou/Twitter: https://twitter.com/MarkMoyou📗 Chapters[00:00] Intro[01:26] Optimizing LLMs for deployment[10:23] Economy of Scale (Batch Size)[13:18] Data Parallelism[14:30] Kernels on GPUs[18:48] Hardest part of optimizing[22:26] Choosing hardware for LLM[31:33] Storage and Networking - Analyzing Performance[32:33] Minimum size of model where tensor parallel gives you advantage[35:20] Director Level folks thinking about deploying LLM[37:29] Kyle is working on AI foundation models[40:38] Deploying Models with endpoints[42:43] Fine Tuning, Deploying Loras[45:02] SteerLM[48:09] KV Cache[51:43] Advice for people for deploying reasonable and large scale LLMs[58:08] Graph Neural Networks[01:00:04] GNNs[01:04:22] Using GPUs to do GNNs[01:08:25] Starting your GNN journey[01:12:51] Career Optimization Function[01:14:46] Solving Hard Problems[01:16:20] Maintaining Technical Skills[01:20:53] Deep learning expert[01:26:00] Rapid Round

Kaggle Grandmaster Chris Deotte, he is currently ranked 1 on notebooks and discussions on Kaggle and is part of the KGMON team, Kaggle Grandmasters of NVIDIA. We’ll be discussing GEN AI and personalization, optimizing your kaggle game and other strategies to make progress in your career.Solution: https://arxiv.org/pdf/2408.04658Mark Moyou, PhD Socials:LinkedIn: https://www.linkedin.com/in/markmoyou/Twitter: https://twitter.com/MarkMoyouChapters:00:00 Intro01:51 Current Gen AI04:40 Evolution of Conceptualization in ML Models06:59 Measuring Tonality in Data Sets08:51 Multi-Modal Data Sets in Text Based Models11:56 Large Vs Small Language Models13:46 KDD 2024 Competition23:28 Prompt Formatting and Bribing the Model28:08 Qwen2 Vs LLama30:39 WiSE - FT33:53 LoRA on all the layers35:43 Logit Preprocessor42:05 Personality of Small Vs Large Model44:02 Models Understanding Shopping Concepts for E-Commerce47:26 Offline Purchase Data in E-Commerce Personalization55:56 Navigating the Problem with Required Data58:33 Constraining LLM Output01:00:45 LLMs in Search and Personalization01:02:03 Kaggle Grandmaster01:09:45 Gen AI in Kaggle Competition01:13:07 Learning ML in Non-Traditional Way01:16:15 Thoughts on doing PhD01:17:58 Mathematics01:22:22 Advice for PhD students01:24:32 Hardest Kaggle Competition01:27:32 Level of Grit in Competitions01:32:59 Career Optimization Function01:35:00 Management vs Technical IC Roles01:37:27 Making Progress01:39:48 Book Recommendations01:44:43 Thoughts on Writing Book01:46:20 Advice for High Schooler, College Students and Professionals01:52:20 Rapid Round

Chris Walton, a top expert in omnichannel retailing, has nearly 20 years of experience in retail and retail technology. As Co-CEO and Founder of Omni Talk, one of the fastest-growing retail blogs, he's a leading voice in the Retail Technology industry. Chris brings deep insights with a background at Target as VP of Store of the Future and Merchandising for Home Furnishings. He holds an MBA from Harvard and a BA from Stanford.Chris Walton Socials:LinkedIn: https://www.linkedin.com/in/chriswaltonretail/OmniTalk Retail:https://www.linkedin.com/company/omnitalk/posts/?feedView=allTwitter: https://mobile.x.com/OmniTalkMark Moyou, PhD Socials:LinkedIn: https://www.linkedin.com/in/markmoyou/Twitter: https://twitter.com/MarkMoyouChapters00:00 Intro01:58 Gen AI in Retail04:53 E-Commerce06:10 When E-Commerce boomed09:15 Advancement of Retail with accelerated compute11:03 Fast Commerce is eating business of large ecommerce players12:43 Labour Dynamics in Retail (Indian vs US market)14:40 Real Business Value of Gen AI17:03 Electronic Dynamic Tags19:35 Computer Vision and Personalization22:10 The art of merchandising24:02 Importance of Merchant29:30 MerchantGPT35:07 Customer loyalty37:39 Segmentation of Retail market39:45 Impact of Gen AI in Retail Market42:00 Shift in Retail Market48:28 Marketing with Gen AI52:07 Why AI can't replicate sale associates and customer service55:44 Challenges of data in Retail Market59:54 Doing my own way mindset in retail01:01:07 Executive Big Bets01:03:13 Search in Product Discovery01:06:13 Retail Media Networks01:08:20 Retail Ads, YouTube and Brand Recognition01:11:47 AI Generated Content01:14:40 Are Malls dead?01:15:20 AI Agents in Retail01:16:40 Career Optimization Function01:19:23 Three Book Recommendations01:20:04 Career Advice01:22:15 Rapid Round

Ramsri, an AI expert with 12+ years of experience in AI and product development, specializing in computer vision and NLP. Ramsri is the founder of Questgen.ai and Supermeme.ai, innovative NLP SaaS apps. He also teaches NLP and AI SaaS courses at LearnNLP Academy and shares insights on Medium, Twitter, and YouTube.📲 Ramsri Goutham Golla Socials:LinkedIn: https://www.linkedin.com/in/ramsrig/Twitter: https://twitter.com/ramsri_goutham📲 Mark Moyou, PhD Socials:LinkedIn: https://www.linkedin.com/in/markmoyou/Twitter: https://twitter.com/MarkMoyou📗 Chapters00:00 Intro02:43 Bootstrapping Two Companies05:09 PhD and Entrepreneurship08:11 Overview of Companies09:53 Memes12:23 Bootstrapping Startups22:27 Minimalistic Philosophy for Startups25:47 Building Distribution through Personal Brand34:39 Saas vs Micro Saas40:15 Happy Place47:51 Major Components of AI saas apps54:04 Open Source Models vs API58:26 Getting Featured in Google01:08:17 Coming back to India01:17:36 Takeaways from Silicon Valley01:32:48 Company of one01:35:39 Number One on Product Hunt01:45:25 Generalize High Cognition Barrier01:53:15 Mistakes in building Saas01:59:17 Unfair Advantage02:01:53 Advice for launching micro saas apps02:05:09 If you have to start over02:08:36 Advice for high schooler, college student and professionals02:22:17 Rapid Round

Serg Masís, a renowned Data Scientist in the agriculture sector, with a multifaceted background in entrepreneurship and web/app development. Serg is also the author of the acclaimed book "Interpretable Machine Learning with Python."📲 Serg Masís Socials:LinkedIn: https://www.linkedin.com/in/smasis/📲 Mark Moyou, PhD Socials:LinkedIn: https://www.linkedin.com/in/markmoyou/Twitter: https://twitter.com/MarkMoyou📗 Chapters00:00 Intro03:02 Interpretable Machine Learning System11:06 post-hoc interpretability12:57 Quality of Interpretability bounded by the breadth of inputs18:52 EDA is more than just exploring the data21:12 Interpretable Models27:19 Upper Limit28:59 Causal reasoning35:02 Domain Knowledge in Causality37:42 Causality on existing and generative data40:42 AGI and Simulation47:33 LLMs to Interpret Data52:25 Interaction with Machine Learning Systems54:53 Modeling in EDA57:34 Training Data Mixtures01:02:26 Impact of Generative AI01:06:05 Mixture of Experts01:12:46 Serg's upcoming book01:18:54 Building Product Mindset01:23:59 Barrier of Entry for Data Scientists01:28:01 Advice for Book Writers01:33:43 Career Optimization Function01:37:45 Web Development Background01:42:24 Advice for High Schooler, College Student and Professionals01:47:57 Book Recommendations01:49:26 Rapid Round

Sanyam Bhutani, a leading figure in the data science community. Sanyam is a Sr. Data Scientist at H2O.ai, with previous tenures at Weights & Biases and H2O.ai, and an International Fellow at fast.ai. As a Kaggle Grandmaster, his contributions to the field are well-recognized and highly respected.Sanyam delves into the nuances of fine-tuning and optimizing Large Language Models (LLMs). He provides a detailed exploration of the current state and future potential of LLMs, breaking down their architecture and functionality in a way that's accessible to both newcomers and seasoned data scientists. Sanyam discusses the importance of fine-tuning in enhancing the performance and applicability of LLMs, providing practical insights and strategies for effective implementation.📲 Radek Osmulski Socials:LinkedIn: https://www.linkedin.com/in/sanyambhutani/Twitter: https://x.com/bhutanisanyam1?lang=en📲 Mark Moyou, PhD Socials:LinkedIn: https://www.linkedin.com/in/markmoyou/Twitter: https://twitter.com/MarkMoyou📗 Chapters00:00 Intro02:46 200 days of LLMs06:16 Venture Capital08:40 Setting Goals in Public09:45 Fine tuning Experiment14:02 Kaggle Grandmasters Team15:55 Doing Challenges & Reading Research Papers17:47 Hardest topic to learn in AI19:05 Are you afraid to ask stupid questions?20:43 Learning how LLMs work22:54 Academic vs Product First Mindset27:51 Training or Inference on LLMs29:15 Favorite LLM Agent32:10 How to go about learning LLMs?36:55 Open Source LLMs on Research Papers37:41 Capability of Modern GPUs 45:48 Journey to H20.ai 50:07 Why Sanyam stopped podcasting?56:25 Podcasting Experience58:39 Top Data Scientists01:00:19 Advice for New Podcasts01:03:32 Breaking into Data Science01:12:23 Career Optimization Function01:14:02 Making Progress Everyday01:15:05 Advice for New Professionals01:17:00 Book Recommendations01:18:04 Rapid Round

Radek Osmulski, a Senior Data Scientist specializing in Recommender Systems at NVIDIA, brings a wealth of experience to the table. With a foundation laid by Fast.ai's Deep Learning course, Radek has honed his skills through extensive project work, emerging as a champion in Kaggle competitions. Radek sheds light on his role at NVIDIA, sharing valuable insights gained from his time there and delving into topics like meta-learning and the path to mastery in machine learning.📲 Radek Osmulski Socials:LinkedIn: https://www.linkedin.com/in/radek-osmulski-6b935794/?originalSubdomain=auTwitter: https://twitter.com/radekosmulski?lang=en📲 Mark Moyou, PhD Socials:LinkedIn: https://www.linkedin.com/in/markmoyou/Twitter: https://twitter.com/MarkMoyou📗 Chapters0:00:00 Opening0:01:45 NVIDIA0:04:37 Kaggle Competitions0:11:11 Fast AI Course0:15:12 Learning AI0:17:06 NVIDIA Job Title0:18:40 Learning's at NVIDIA0:21:47 Meta Learning0:28:13 Becoming Great at ML0:30:36 Building Career Outside US0:33:56 Hustling0:38:19 Code Cleaning Practices0:41:58 Programming Skills0:48:50 Learning with LLM0:51:51 Product Mindset for Learning0:54:10 Tweeting Advice1:01:02 Radek's Career Optimization Function1:06:36 Advice for Writers1:21:36 Outside Work Interests1:23:53 Book Recommendations1:27:56 Advice for High School, College Student and Working Professional1:29:36 Rapid Round

Harpreet brings a trove of knowledge and insights to the table. In this engaging conversation, we explore various facets of his expertise, from his role in developer relations to his deep understanding of Large Language Models (LLMs) and their evaluation. Harpreet provides invaluable insights into the intricacies of LLM evaluation, shedding light on methodologies, challenges, and best practices. His deep understanding of LLMs stems from years of hands-on experience and a commitment to pushing the boundaries of AI research and development.📲 Harpreet Sahota Socials:LinkedIn: https://www.linkedin.com/in/harpreetsahota204/?originalSubdomain=caTwitter: https://twitter.com/DataScienceHarp📲 Mark Moyou, PhD Socials:LinkedIn: https://www.linkedin.com/in/markmoyou/Twitter: https://twitter.com/MarkMoyou📗Chapters00:00 Intro02:10 What problems DECI AI is solving?04:42 DeciLM-7B06:41 Supergradients YOLO-NAS09:03 Deci Team09:40 DeciLM-7B vs Mistral 7B14:54 LLM Evaluation21:41 Fine tuning 7B and 13B Models24:16 Fine tuning in RAG29:24 Deploying Gen AI in Workloads31:59 Non Technical Executives33:15 Harpreet's Career Journey38:34 Developer Relations Position41:37 Your personal brand is an investment48:20 Finding your own vibe53:12 Data Science in 202456:32 Podcasting01:05:02 When to quit01:11:45 Career Optimization Function01:18:03 Harpreet's Obsession besides Gen AI01:19:30 AI for Babies01:21:26 Curiosity01:24:08 Changing Jobs01:28:32 Book Recommendations01:30:21 Advice for High Schooler, College Student and Professionals01:32:15 Fav Meals01:33:15 Legacy to leave behind.

Demetrios Brinkmann, the Founder and CEO of MLOps Community, as he shares his expertise on the dynamic landscape of Machine Learning Operations (MLOps) and the transformative impact of Large Language Models (LLMs). He also highlights the importance of authenticity and fun in community building, addresses common challenges in MLOps, and shares his experiences leading a technical community.📲 Demetrios Brinkmann Socials:LinkedIn: https://www.linkedin.com/in/dpbrinkm/Twitter: https://twitter.com/Dpbrinkm📲 Mark Moyou, PhD Socials:LinkedIn: https://www.linkedin.com/in/markmoyou/Twitter: https://twitter.com/MarkMoyou📗Chapters00:00 - Intro02:56 - How has MLOps changed after the LLM explosion?10:14 - Convergence of MLOps13:37 - MLOps in Tech-First vs Legacy Companies17:24 - Rise of MLOps (LLM) Companies20:35 - Generative DevOps22:08 - How Big is the MLOps Community?24:10 - Inception of the MLOps Community30:30 - Authenticity and Fun Are the Key37:43 - Most Commonly Asked Problems/Topics in MLOps41:28 - Learning New Things47:00 - Leading a Technical Community50:36 - Monetizing the Community59:44 - Why isn't the MLOps Community on Discord?01:02:48 - Importance of a Meme Channel01:04:37 - Do Lurkers Add Value to the Community?01:07:48 - Mistakes Made While Building the Community01:14:41 - Lessons Learned on Selling01:19:06 - Advice for Podcasters01:26:58 - Motivation01:28:30 - Success01:34:12 - Book Recommendations01:40:24 - Rapid Round

Sujatha Sundararaman, a distinguished engineering leader and Head of Search, Understanding, and Voice at Google's YouTube Music (YTM). With a stellar track record in both startup and enterprise environments, Sujatha excels in steering ideas from conception to successful, revenue-generating products. 📲 Sujatha Sundararaman Socials:LinkedIn: https://www.linkedin.com/in/sujatha-sundararaman-49a339/📲 Mark Moyou, PhD Socials:LinkedIn: https://www.linkedin.com/in/markmoyou/Twitter: https://twitter.com/MarkMoyou📗 Chapters00:00 Intro02:17 LLM in Music Recommendations04:28 Why music is important?08:05 Exploration Vs Exploitation11:48 Prevalence of remix songs15:07 Why music recommendation problem is difficult?20:30 Large datasets and diversity of users25:54 Handling User experience and expectations in music28:20 Categorizing Music and Content Understanding32:28 Glocal Music Behavior36:20 Multilingual Users41:22 Building trust with YT music46:19 Generative AI in Music58:54 How dance drives music listening behavior?01:01:17 Startup Lessons01:06:02 Career Optimization Function01:08:20 Networking and Switching Companies01:11:14 Mentors01:15:46 Managing a Team01:18:43 How to manage world class teams?01:21:32 Advice for Investors in AI space01:23:32 Advice for startup founders01:25:35 What value MBA provides?01:28:58 3 book recommendations01:32:15 How to approach job market?01:37:50 Rapid Round

Igor Jablokov, the visionary Founder and CEO of Pryon, an AI company at the forefront of innovation in enterprise knowledge management. In this captivating podcast episode, we delve into Igor's remarkable journey, from his formative years at IBM to his pivotal decision to establish Pryon. Fueled by a deep-seated impatience with the pace of AI commercialization, Igor embarked on a mission to bridge the gap between AI invention and real-world application.📲 Igor Jablokov Socials:LinkedIn: https://www.linkedin.com/in/ijablokov/Twitter: https://twitter.com/ijablokov📲 Mark Moyou, PhD Socials:LinkedIn: https://www.linkedin.com/in/markmoyou/Twitter: https://twitter.com/MarkMoyou📗 Chapters00:00 Intro02:39 What problem are you solving at Pryon?04:48 Navigating Enterprise LLM06:59 Data Quality09:17 Sustainable Compute15:16 Responsive Companies20:03 Capturing Legacy Information23:04 Connectors26:16 Scaling and Energy Efficiency28:46 Content Understanding (Images and Video)30:46 LLMs35:49 Enterprise Knowledge Management43:41 Search47:42 Philosophy on Running Pryon54:19 Attaining $100 million Funding01:03:08 Attracting and Retaining Talent01:10:55 Career Optimization Function

In this episode, Mike shares his extensive expertise in building and leading high-performance machine learning teams, delivering cutting-edge data products, and leveraging state-of-the-art technologies for various real-world applications. His contributions in text comprehension using large language models (LLMs), image recognition, Graph Neural Networks (GNNs), learned representation-based recommender systems, targeted advertising, time series forecasting, user understanding, and customer analytics are nothing short of awe-inspiring.📲 Mike Tamir Socials:LinkedIn: https://www.linkedin.com/in/miketamir/Twitter: https://twitter.com/MikeTamir📲 Mark Moyou, PhD Socials:LinkedIn: https://www.linkedin.com/in/markmoyou/Twitter: https://twitter.com/MarkMoyou📗 Chapters00:00 Inro02:29 Shift in LLM Paradigm08:50 First Impression of ChatGPT15:00 LLM Fad21:00 Mastering LLM 26:48 Test & Evaluation35:08 Hallucinations37:40  Retrieval-Augmented Generation (RAG)40:41 Large vs Small Model & Enterprise vs Open Source Models52:44 LLM Strategy for Teams 58:01 Evolution of Instructions tailored towards agent59:52 Prediction Hypothesis  01:01:52 Startup01:09:50 Advice for VCs01:13:12 Differences in Data Science Culture01:16:38 Approach for Leading Teams01:18:18 Reading01:19:20 Succeeding as a Team01:21:07 Small vs Big Teams01:23:06 What makes you really good at your job?01:25:52 PhD and Teaching01:31:22 Research Papers01:32:35 Career Optimization Function01:34:45 Book Recommendations01:37:15 Rapid Round

Jacopo Tagliabue is one of the most honest and talented open-source contributors in the Data Science community, previously focusing on NLP and Recommender Systems. Currently, he eats pizza, plays very occasional tennis, and is building a data pipeline company, Bauplan.A true founder and builder at heart, Jacapo and team pioneered the movement for doing data science at reasonable scale. His NLP company Tooso, was acquired by Coveo where he led Data Science for search all the way to an IPO.He - has been educated at top institutions such as UNISR, SFI, and MIT- has collaborated on open-source research with Stanford, Netflix, Farfetch, and NVIDIA- has invested in startups and funds as an LP - teaches machine learning at NYU as an adjunct professor📲 Jacopo Tagliabue Socials:LinkedIn: https://www.linkedin.com/in/jacopotagliabue/ (don't try to sell him anything)Twitter: https://twitter.com/jacopotagliabueWebsite: https://jacopotagliabue.it/📲 Mark Moyou, PhD Socials:LinkedIn: https://www.linkedin.com/in/markmoyou/Twitter: https://twitter.com/MarkMoyou📗 Chapters00:00 Intro03:34 LLM Progress09:07 Small LLMs11:38 Search and Information Retrieval15:12 Recommender System19:20 Robust Recommender System22:37 Hardest Domain for Recommender System24:54 Fake Datasets for Research26:48 Mistakes in Building Recommender System 30:10 Building a Reasonable Recommender System33:46 Reasonable Scale38:17 What Bauplan is trying to accomplish?44:41 Data Processing50:05 Build Vs Buy 55:12 Building a New Startup57:35 Tennis58:31 Investments in ML Community01:02:14 First Company01:10:37 Advice for Founders/Lessons Learned from First Startup01:15:03 PhD01:18:39 Career Optimization Function01:23:06 Advice for Open Source Contribution (Do the freaking commit!)01:27:30 Journal for Failed Papers01:29:53 Book Recommendations01:34:00 Advice for High Schooler, College Student & Professional01:36:10 Rapid Round

Paige Bailey, a Lead Product Manager for Generative Models at Google DeepMind, shares her expertise in guiding teams training very large language models. She focuses on training code generation models, one of the industry's most valuable types of LLMs. Paige has held multiple leadership positions at Google, Microsoft, and Github. In this interview, you will understand the responsibilities of a top Product Manager at a forefront AI company and how you can navigate your career from a data position to a product manager position. 📲 Paige Bailey Socials:LinkedIn: https://www.linkedin.com/in/dynamicwebpaige/Twitter: https://twitter.com/DynamicWebPaigehttps://github.com/dynamicwebpaige📲 Mark Moyou, PhD Socials:LinkedIn: https://www.linkedin.com/in/markmoyou/Twitter: https://twitter.com/MarkMoyouSee other interviews and talks from Paige:- Generative Models: https://www.youtube.com/live/wqkKResXWB8?si=Vxwspw_TXGs9XoG9&t=2356- Learning From Machine Learning - https://youtu.be/BNz2yFVppts?si=aA2hjphacfDP2taW- Whats AI by Louis Bouchard - https://youtu.be/y5nxMnTtbD4?si=_eeADH9ZFAE-W6vy📗 Chapters00:00 Intro02:19 Product Manager at Google DeepMind04:12 Is LLM a fad?06:35 Large VS Small LLM08:37 Multimodal Model11:06 Generative AI for Code Generation in Enterprise14:25 Tokenization Approaches for Code Generation20:30 Data Poisoning23:31 Nervousness during Pre-Training Run26:27 Best Practices for Fine-Tuning and Instruction Training28:14 When to Redo a Training Run?30:15 Obsession with Generative Models34:18 Fine-Tuning Internal Codebase36:40 Future of Software Engineers39:30 LLM for Data Leaders40:58 Gen AI for Small and Medium Enterprise42:45 How do I Convince My Boss to Adopt Gen AI?45:20 Evolution of Data Science48:00 Gen AI for Investors50:52 Workspace Shrinkage53:06 Gen AI Impact on Academia57:16 Advice for Web3 Influencers01:01:52 Career Optimization Function01:05:13 Switching to Product Management01:07:05 Criteria for Changing the Job01:10:06 Consuming AI Research Content01:11:57 Top 1% Data Scientists01:12:41 Imposter Syndrome01:13:41 Unfair Advantage01:17:00 Book Recommendations01:20:11 Habits to Make Progress01:21:40 Two Meals01:23:00 Advice for High Schoolers, College Students, & Professionals01:24:59 What do you want people to remember about you?

In this first episode you will learn why Hamza left Google as a Senior Research Manager to start a search startup given all the progress of large language models (LLM). He covers some core tenants you need to start building LLM-based systems. More importantly, in this fast-moving field, you learn how Hamza navigated his career and what you can do to make progress.  Hamza Farooq is the CEO/founder of traversaal.ai. Previously, he was a Sr. Research Science Manager at Google, a lecturer at Stanford, and an adjunct professor at the University of Minnesota. Hamza has worked at top companies such as Walmart Labs and Gallup and teaches a course on Maven called Building LLMs Applications from Scratch into Production.📲 Hamza Farooq Socials:LinkedIn:   / hamzafarooq 📲 Mark Moyou, PhD Socials:LinkedIn:   / markmoyou  Twitter:   / markmoyou  Startup (Traversaal.ai) - https://www.traversaal.ai/📒  Chapters 00:00:00 Intro00:03:01 - LLM Summer00:04:06 - Falcon AI00:07:27 - Language Modelling00:08:33 - Distance Vs Metric Space Modeling00:11:36 - Context Learning vs fine-tuning00:14:38 - Benefit of larger LLM00:17:41 - Lexical, Hybrid, or Neural Approach00:21:41 - Natural preferences in LLM00:24:52 - LLM in user reviews00:27:34 - FOMO of AI00:30:16 - AI Hype or Innovation00:34:26 - Generative Models00:35:40 - Open AI vs open-source models00:38:36 - Open Source Models monetization00:40:18 - Mistakes in LLM journey00:41:53 - What is LLM00:44:01 - LLM optimization00:46:09 - NLP or LLM00:47:35 - LLM consumption00:49:04 - Models Hosting00:51:17 - Education00:53:33 - Open Source work00:55:29 - ML system design00:58:14 - Career01:04:15 - Career Optimization function01:08:24 - Advice for international students01:11:06 - MBA and PhD01:15:06 - Imposter Syndrome01:16:44 - Perspectives for People in their home country01:20:24 - Unfair Advantage