Machine Learning Youtuber Umar Jamil, His channel has some of the top technical explanations on technical topics related to Gen AI with over 50k followers.

Follow/Connect:
Umar LinkedIn: https://www.linkedin.com/in/ujamil/
Mark Linkedin: https://www.linkedin.com/in/markmoyou/

Chapters:
00:00 Intro
01:54 Umar's YouTube Channel
06:10 Umar's Data Science Journey
11:30 Show Your Work
16:35 Pre-Training
20:19 Research Without a PhD
22:22 Lessons Learned as a Research Scientist
23:41 Advice for Technical Creators
25:30 Building
30:17 Hardest Challenges
43:58 Research Papers
49:41 How Language Models Learn
50:48 Coding IDEs
53:19 Choosing a Model
54:50 AI Coding Editors
55:55 Career Optimization Function
57:09 Changing Jobs and Approaching the Job Market
01:00:30 Book Recommendations
01:02:37 Why We Struggle to Finish Things
01:05:15 Career Advice
01:07:26 Coding the Transformer
01:15:27 CLIP Models
01:17:06 Diffusion Models
01:18:22 Autoencoder
01:20:23 Segment Anything Model
01:21:30 Direct Preference Optimization
01:23:30 Multimodal Models
01:26:09 Test-Time Compute
01:27:05 Rapid Round

Maxime Labonne, Co-author of the LLM Engineers Handbook, creator of the LLM course on github with over 40k stars, and author of Hands on Graph Neural Networks.

Follow/Connect:
Maxime LinkedIn:https://www.linkedin.com/in/maxime-labonne/
Mark Linkedin: https://www.linkedin.com/in/markmoyou/

Chapters:

📌 00:00 – Intro
📚 01:51 – Maxime: Books & Courses
🤖 07:30 – AI Scientist vs. AI Engineer
🚀 09:05 – Path to Becoming an AI Expert
🎓 11:13 – Do You Need a Degree?
⏳ 13:01 – How Long Does It Take to Become an AI Scientist?
👨‍🔬 15:58 – Individual Contributor Role as an LLM Scientist
🧠 26:04 – Understanding LLM Personality
🎯 30:07 – Objective Functions in LLMs
🆕 34:08 – Emerging AI Models
📄 40:51 – How to Read Research Papers Effectively
🗺 45:26 – Roadmap to Becoming an LLM Scientist (Key Section ⭐)
🔎 54:15 – Deep Dive: Understanding the Attention Mechanism
🖼 01:15:33 – Roadmap for a Multi-Modal AI Scientist
📊 01:17:12 – Optimizing Your AI Career Path
💼 01:19:38 – AI Job Market Insights
📖 01:21:50 – Must-Read Books for AI Enthusiasts
💡 01:22:41 – Expert Career Advice
⚡ 01:24:07 – Rapid-Fire Round

Tomás Hernando Kofman of Not Diamond, discussing about gen ai model routing.

Follow/Connect:
Tomas LinkedIn:https://www.linkedin.com/in/tomashk/
Mark Linkedin: https://www.linkedin.com/in/markmoyou/

Chapters: 
00:00 Intro
01:56 Not Diamond
08:36 Router Problem
13:33 Time to Build v1
15:49 Human Categories
19:01 System Prompt
27:33 Prompt Drift
30:14 Guardrails
35:48 Model Spectrum
42:23 Caching
45:50 Training Router
54:44 Cost and Latency
01:01:22 Routing Across Agents
01:03:38 Prompt Pasting
01:09:17 Routing for Multimodal Models
01:12:08 Startup
01:21:20 Building Companies in the Gen AI Era
01:27:00 Career Optimization Function
01:29:33 Career Advice
01:32:00 Rapid Round

Dr. Raza Habib, CEO of HumanLoop, discussing the complexities of large language model (LLM) deployment, monitoring, and evaluation.

Follow/Connect:
Raza Linkedin: https://www.linkedin.com/in/humanloop-raza/
Mark LinkedIn: https://www.linkedin.com/in/markmoyou/

Chapters:
00:00 Intro
02:19 Humanloop
06:08 Prompt Management
13:26 LLM System
17:57 AI Guardrails
22:32 Workflow
27:14 User Generated Hyperparameters
31:30 LLM Routing
34:20 Prompt Engineering
38:22 Data Poisoning
45:17 Latency in LLMs
50:25 Monitoring LLMs
57:57 LLM Eval Benchmarks
01:03:27 Startup
01:17:51 Career Optimization Function
01:19:44 Books
01:24:27 Career Advice
01:27:51 Rapid Round

Ravit Jain of the Ravit Show, a top media outlet covering the latest trends in Data and AI

Follow/Connect:
Mark LinkedIn: https://www.linkedin.com/in/markmoyou/
Ravit Linkedin: https://www.linkedin.com/in/ravitjain/

Chapters: 
00:00 Intro
02:26 Ravit Show
06:38 Data Orchestration
11:48 Gen AI Strategy
19:05 Skepticism
24:29 Prompt Engineering
29:53 Top 3 Problems to Invest In
36:44 Open Source Vs Enterprise
39:40 Developers as CEOs
43:07 Challenges in Value Offering
51:26 Targeting Actual Users
01:00:41 Reinforcement through Content
01:03:47 GenAI in Influencer Marketing
01:06:27 Value of Long Form Content
01:09:37 Building Personal Brands
01:19:47 Career Optimization Function
01:25:03 AI Job Market
01:30:55 Book Recommendations
01:32:29 Career Advice
01:32:49 Rapid Round

Welcome to AI Porfolio Podcast Episode 17, Dr. William Gaviria Rojas, Cofounder of Coactive AI a data analytics platform for unstructured visual data at the largest scale.

Follow/Connect:
Mark LinkedIn: https://www.linkedin.com/in/markmoyou/
William Linkedin: https://www.linkedin.com/in/williamgaviria/

Chapters:
00:00 Intro
01:58 Billion Dollar Startup  
02:57 Coactive AI  
05:29 Market Size of MultiModal AI  
07:43 Large Data Problem  
14:56 Ideal Market  
20:22 Taxonomy Refinement  
24:42 Insight Economy (Derivative Data)  
28:15 Computer Vision (High Fidelity Data Analysis)  
35:13 Personalized Video Media  
38:22 Coactive Software Deployment  
42:22 Agents Integration  
44:14 Coactive GPUs  
45:27 Embedding Models  
48:25 Largest Video Dataset  
49:29 Unfit Customers  
53:04 Starting Coactive  
01:02:48 Venture Funding  
01:05:50 Unfair Advantage  
01:07:52 PhD  
01:11:30 Career Advice

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