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 Intro
02:09 Is AI Resurgence of Graph tech?
03:46 GraphRAG popularity
05:39 Top Use Cases in GenAI
11:08 Gen AI in supply chain
16:46 Graph and its types in enterprise
24:03 GraphRag
25:25 GNNs in GraphRAG
29:30 Graphs are eating the world
35:16 Knowledge Graph
36:06 Drawbacks of vector based rag
37:43 Neo4j vector database
41:27 Filtering with Knowledge Graph
45:02 Execution Time of LLMs
49:03 Does longer prompts mean longer graph query?
54:26 Scale of Graph
57:05 Marriage of Graphs and Vectors
59:46 Fine Tuning with Graphs
01:00:46 Graphs Use less tokens
01:02:46 Multiple vs One GraphRAG
01:05:38 Updating Knowledge in Graph
01:10:50 large Vs small models
01:13:09 MultiModal GraphRAG
01:15:36 Graphs in Robotics
01:17:11 Neo4j journey
01:20:03 Phillip Linkedin Post
01:21:56 What's different with AI
01:23:31 Advice for Gen AI startups
01:26:00 CTO advice
01:29:36 Chemical Engineering
01:32:00 Career optimization function
01:35:00 Book Recommendations
01:37:06 Rapid Round

Get 1000 free inference requests for LLMs on build.nvidia.com
Kyle 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/2408.04658

Mark Moyou, PhD Socials:
LinkedIn: https://www.linkedin.com/in/markmoyou/
Twitter: https://twitter.com/MarkMoyou

Chapters:
00:00 Intro
01:51 Current Gen AI
04:40 Evolution of Conceptualization in ML Models
06:59 Measuring Tonality in Data Sets
08:51 Multi-Modal Data Sets in Text Based Models
11:56 Large Vs Small Language Models
13:46 KDD 2024 Competition
23:28 Prompt Formatting and Bribing the Model
28:08 Qwen2 Vs LLama
30:39 WiSE - FT
33:53 LoRA on all the layers
35:43 Logit Preprocessor
42:05 Personality of Small Vs Large Model
44:02 Models Understanding Shopping Concepts for E-Commerce
47:26 Offline Purchase Data in E-Commerce Personalization
55:56 Navigating the Problem with Required Data
58:33 Constraining LLM Output
01:00:45 LLMs in Search and Personalization
01:02:03 Kaggle Grandmaster
01:09:45 Gen AI in Kaggle Competition
01:13:07 Learning ML in Non-Traditional Way
01:16:15 Thoughts on doing PhD
01:17:58 Mathematics
01:22:22 Advice for PhD students
01:24:32 Hardest Kaggle Competition
01:27:32 Level of Grit in Competitions
01:32:59 Career Optimization Function
01:35:00 Management vs Technical IC Roles
01:37:27 Making Progress
01:39:48 Book Recommendations
01:44:43 Thoughts on Writing Book
01:46:20 Advice for High Schooler, College Students and Professionals
01:52:20 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=all
Twitter: https://mobile.x.com/OmniTalk

Mark Moyou, PhD Socials:
LinkedIn: https://www.linkedin.com/in/markmoyou/
Twitter: https://twitter.com/MarkMoyou


Chapters
00:00 Intro
01:58 Gen AI in Retail
04:53 E-Commerce
06:10 When E-Commerce boomed
09:15 Advancement of Retail with accelerated compute
11:03 Fast Commerce is eating business of large ecommerce players
12:43 Labour Dynamics in Retail (Indian vs US market)
14:40 Real Business Value of Gen AI
17:03 Electronic Dynamic Tags
19:35 Computer Vision and Personalization
22:10 The art of merchandising
24:02 Importance of Merchant
29:30 MerchantGPT
35:07 Customer loyalty
37:39 Segmentation of Retail market
39:45 Impact of Gen AI in Retail Market
42:00 Shift in Retail Market
48:28 Marketing with Gen AI
52:07 Why AI can't replicate sale associates and customer service
55:44 Challenges of data in Retail Market
59:54 Doing my own way mindset in retail
01:01:07 Executive Big Bets
01:03:13 Search in Product Discovery
01:06:13 Retail Media Networks
01:08:20 Retail Ads, YouTube and Brand Recognition
01:11:47 AI Generated Content
01:14:40 Are Malls dead?
01:15:20 AI Agents in Retail
01:16:40 Career Optimization Function
01:19:23 Three Book Recommendations
01:20:04 Career Advice
01:22:15 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

📗 Chapters
00:00 Intro
02:43 Bootstrapping Two Companies
05:09 PhD and Entrepreneurship
08:11 Overview of Companies
09:53 Memes
12:23 Bootstrapping Startups
22:27 Minimalistic Philosophy for Startups
25:47 Building Distribution through Personal Brand
34:39 Saas vs Micro Saas
40:15 Happy Place
47:51 Major Components of AI saas apps
54:04 Open Source Models vs API
58:26 Getting Featured in Google
01:08:17 Coming back to India
01:17:36 Takeaways from Silicon Valley
01:32:48 Company of one
01:35:39 Number One on Product Hunt
01:45:25 Generalize High Cognition Barrier
01:53:15 Mistakes in building Saas
01:59:17 Unfair Advantage
02:01:53 Advice for launching micro saas apps
02:05:09 If you have to start over
02:08:36 Advice for high schooler, college student and professionals
02:22:17 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

📗 Chapters
00:00 Intro
03:02 Interpretable Machine Learning System
11:06 post-hoc interpretability
12:57 Quality of Interpretability bounded by the breadth of inputs
18:52 EDA is more than just exploring the data
21:12 Interpretable Models
27:19 Upper Limit
28:59 Causal reasoning
35:02 Domain Knowledge in Causality
37:42 Causality on existing and generative data
40:42 AGI and Simulation
47:33 LLMs to Interpret Data
52:25 Interaction with Machine Learning Systems
54:53 Modeling in EDA
57:34 Training Data Mixtures
01:02:26 Impact of Generative AI
01:06:05 Mixture of Experts
01:12:46 Serg's upcoming book
01:18:54 Building Product Mindset
01:23:59 Barrier of Entry for Data Scientists
01:28:01 Advice for Book Writers
01:33:43 Career Optimization Function
01:37:45 Web Development Background
01:42:24 Advice for High Schooler, College Student and Professionals
01:47:57 Book Recommendations
01:49:26 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

📗 Chapters
00:00 Intro
02:46 200 days of LLMs
06:16 Venture Capital
08:40 Setting Goals in Public
09:45 Fine tuning Experiment
14:02 Kaggle Grandmasters Team
15:55 Doing Challenges & Reading Research Papers
17:47 Hardest topic to learn in AI
19:05 Are you afraid to ask stupid questions?
20:43 Learning how LLMs work
22:54 Academic vs Product First Mindset
27:51 Training or Inference on LLMs
29:15 Favorite LLM Agent
32:10 How to go about learning LLMs?
36:55 Open Source LLMs on Research Papers
37:41 Capability of Modern GPUs
45:48 Journey to H20.ai
50:07 Why Sanyam stopped podcasting?
56:25 Podcasting Experience
58:39 Top Data Scientists
01:00:19 Advice for New Podcasts
01:03:32 Breaking into Data Science
01:12:23 Career Optimization Function
01:14:02 Making Progress Everyday
01:15:05 Advice for New Professionals
01:17:00 Book Recommendations
01:18:04 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=au
Twitter: https://twitter.com/radekosmulski?lang=en

📲 Mark Moyou, PhD Socials:
LinkedIn: https://www.linkedin.com/in/markmoyou/
Twitter: https://twitter.com/MarkMoyou

📗 Chapters
0:00:00 Opening
0:01:45 NVIDIA
0:04:37 Kaggle Competitions
0:11:11 Fast AI Course
0:15:12 Learning AI
0:17:06 NVIDIA Job Title
0:18:40 Learning's at NVIDIA
0:21:47 Meta Learning
0:28:13 Becoming Great at ML
0:30:36 Building Career Outside US
0:33:56 Hustling
0:38:19 Code Cleaning Practices
0:41:58 Programming Skills
0:48:50 Learning with LLM
0:51:51 Product Mindset for Learning
0:54:10 Tweeting Advice
1:01:02 Radek's Career Optimization Function
1:06:36 Advice for Writers
1:21:36 Outside Work Interests
1:23:53 Book Recommendations
1:27:56 Advice for High School, College Student and Working Professional
1:29:36 Rapid Round