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