Agentic Conversations (formally mlops.community)

Latest Episodes:

Collective Memory for AI on Decentralized Knowledge Graph // Tomaž Levak // #285 Jan 24, 2025

Show notes

Tomaž Levak is the Co-founder and CEO of Trace Labs – OriginTrail core developers. OriginTrail is a web3 infrastructure project combining a decentralized knowledge graph (DKG) and blockchain technologies to create a neutral, inclusive ecosystem.

Collective Memory for AI on Decentralized Knowledge Graph // MLOps Podcast #285 with Tomaz Levak, Founder of Trace Labs, Core Developers of OriginTrail.

// Abstract

The talk focuses on how OriginTrail Decentralized Knowledge Graph serves as a collective memory for AI and enables neuro-symbolic AI. We cover the basics of OriginTrail’s symbolic AI fundamentals (i.e., knowledge graphs) and go over the details of how decentralization improves data integrity, provenance, and user control. We’ll cover the DKG role in AI agentic frameworks and how it helps with verifying and accessing diverse data sources, while maintaining compatibility with existing standards. We’ll explore practical use cases from the enterprise sector as well as the latest integrations into frameworks like ElizaOS. We conclude by outlining the future potential of decentralized AI, AI becoming the interface to “eat” SaaS, and the general convergence of AI, Internet, and Crypto.

// Bio

Tomaz Levak, founder of OriginTrail, is active at the intersection of Cryptocurrency, the Internet, and Artificial Intelligence (AI). At the core of OriginTrail is a pursuit of Verifiable Internet for AI, an inclusive framework addressing critical challenges of the world in an AI era. To achieve the goal of Verifiable Internet for AI, OriginTrail's trusted knowledge foundation ensures the provenance and verifiability of information while incentivizing the creation of high-quality knowledge. These advancements are pivotal to unlock the full potential of AI as they minimize the technology’s shortfalls, such as hallucinations, bias, issues of data ownership, and model collapse.

Tomaz's contributions to OriginTrail span over a decade and across multiple fields. He is involved in strategic technical innovations for OriginTrail Decentralized Knowledge Graph (DKG) and NeuroWeb blockchain and was among the authors of all three foundational White Paper documents that defined how OriginTrail technology addresses global challenges. Tomaz contributed to the design of OriginTrail token economies and is driving adoption with global brands such as British Standards Institution, Swiss Federal Railways, and World Federation of Haemophilia, among others.

Committed to the ongoing expansion of the OriginTrail ecosystem, Tomaz is a regular speaker at key industry events. In his appearances, he highlights the significant value that the OriginTrail DKG brings to diverse sectors, including supply chains, life sciences, healthcare, and scientific research. In a rapidly evolving digital landscape, Tomaz and the OriginTrail ecosystem as a whole are playing an important role in ensuring a more inclusive, transparent, and decentralized AI.

// MLOps Swag/Merch

https://shop.mlops.community/

// Related Links

Website: https://origintrail.io

Song recommendation: https://open.spotify.com/track/5GGHmGNZYnVSdRERLUSB4w?si=ae744c3ad528424b

--------------- ✌️Connect With Us ✌️ -------------

Join our Slack community: https://go.mlops.community/slack

Catch all episodes, blogs, newsletters, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

Connect with Tomaz on LinkedIn: https://www.linkedin.com/in/tomazlevak/

Timestamps:

[00:00] Tomaz's preferred coffee

[00:20] Takeaways

[01:08] Knowledge Graphs and V8

[02:47] Knowledge Graph Neighborhoods

[07:08] DKG Enterprise Value Prop

[17:33] DKG Multi-Chain Agent Use

[23:40] DKG Use Cases

[26:57] Wearables Data Integration Ideas

[33:19] Knowledge Graph Value Models

[36:14] Replacing Bad Information

[44:13] Node Hosting Responsibilities

[50:47] Wrap up

Efficient Deployment of Models at the Edge // Krishna Sridhar // #284 Jan 17, 2025

Show notes

Krishna Sridhar is an experienced engineering leader passionate about building wonderful products powered by machine learning.

Efficient Deployment of Models at the Edge // MLOps Podcast #284 with Krishna Sridhar, Vice President of Qualcomm.

Big shout-out to Qualcomm for sponsoring this episode!

// Abstract

Qualcomm® AI Hub helps to optimize, validate, and deploy machine learning models on-device for vision, audio, and speech use cases. With Qualcomm® AI Hub, you can: Convert trained models from frameworks like PyTorch and ONNX for optimized on-device performance on Qualcomm® devices.

Profile models on-device to obtain detailed metrics, including runtime, load time, and compute unit utilization. Verify numerical correctness by performing on-device inference. Easily deploy models using Qualcomm® AI Engine Direct, TensorFlow Lite, or ONNX Runtime.

The Qualcomm® AI Hub Models repository contains a collection of example models that use Qualcomm® AI Hub to optimize, validate, and deploy models on Qualcomm® devices. Qualcomm® AI Hub automatically handles model translation from source framework to device runtime, applying hardware-aware optimizations, and performs physical performance/numerical validation. The system automatically provisions devices in the cloud for on-device profiling and inference. The following image shows the steps taken to analyze a model using Qualcomm® AI Hub.

// Bio

Krishna Sridhar leads engineering for Qualcomm™ AI Hub, a system used by more than 10,000 AI developers spanning 1,000 companies to run more than 100,000 models on Qualcomm platforms. Prior to joining Qualcomm, he was Co-founder and CEO of Tetra AI, which made it easy to efficiently deploy ML models on mobile/edge hardware. Prior to Tetra AI, Krishna helped design Apple's CoreML, which was a software system mission-critical to running several experiences at Apple, including Camera, Photos, Siri, FaceTime, Watch, and many more across all major Apple device operating systems and all hardware and IP blocks. He has a Ph.D. in computer science from the University of Wisconsin-Madison and a bachelor’s degree in computer science from Birla Institute of Technology and Science, Pilani, India.

// MLOps Swag/Merch

https://shop.mlops.community/

// Related Links

Website: https://www.linkedin.com/in/srikris/

--------------- ✌️Connect With Us ✌️ -------------

Join our Slack community: https://go.mlops.community/slack

Catch all episodes, blogs, newsletters, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

Connect with Krishna on LinkedIn: https://www.linkedin.com/in/srikris/

Timestamps:

[00:00] Krishna's preferred coffee

[00:12] Takeaways

[01:27] Please like, share, leave a review, and subscribe to our MLOps channels!

[01:56] AI Entrepreneurship Journey

[04:25] Core ML and Edge AI

[08:44] AI Stack & Workflow Strategy

[11:42] On-device AI Foundations[17:15] Hardware vs Software Optimization

[21:32] On-device AI Challenges

[26:19] Small LLM Orchestration

[28:03] Memory Constraints and Shared Pools

[30:05] Qualcomm AI Hub Edge

[32:53] AI in Unexpected Places

[41:53] Deploying AI on Edge

[45:58] 4X Battery Optimization Tips

[51:00] Wrap up

Real World AI Agent Stories // Zach Wallace // #283 Jan 15, 2025

Show notes

Machine Learning, AI Agents, and Autonomy // MLOps Podcast #283 with Zach Wallace, Staff Software Engineer at Nearpod Inc.

// Abstract

Demetrios chats with Zach Wallace, engineering manager at Nearpod, about integrating AI agents in e-commerce and edtech. They discuss using agents for personalized user targeting, adapting AI models with real-time data, and ensuring efficiency through clear task definitions. Zach shares how Nearpod streamlined data integration with tools like Redshift and DBT, enabling real-time updates. The conversation covers challenges like maintaining AI in production, handling high-quality data, and meeting regulatory standards. Zach also highlights the cost-efficiency framework for deploying and decommissioning agents and the transformative potential of LLMs in education.

// Bio

Software Engineer with 10 years of experience. Started my career as an Application Engineer, but I have transformed into a Platform Engineer. As a Platform Engineer, I have handled the problems described below: - Localization across 6-7 different languages - Building a custom local environment tool for our engineers - Building a Data Platform - Building standards and interfaces for Agentic AI within ed-tech.

// MLOps Swag/Merch

https://shop.mlops.community/

// Related Links

https://medium.com/renaissance-learning-r-d/data-platform-transform-a-data-monolith-9d5290a552ef

--------------- ✌️Connect With Us ✌️ -------------

Join our Slack community: https://go.mlops.community/slack

Catch all episodes, blogs, newsletters, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: /dpbrinkm/

Connect with Zach on LinkedIn: https: /zachary-wallace/

Timestamps:

[00:00] Zach's preferred coffee

[00:24] Takeaways

[01:25] Data platform pivot

[04:06] Data integration with DBT

[06:50] Data mesh partial adoption

[08:55] Data product

[10:11] Agent Architectures and Deployment

[15:35] Agent vs LLM

[20:28] AI Agent Analytics

[22:18] Agent Design and Scope

[26:52] DAG Agent Workflow Design

[30:25] Cost Considerations in AI

[35:00] Agent Deployment and Costing

[42:25] AI Evaluation Use Cases

[45:25] Agent vs ML for Contracts

[46:55] Wrap up

Machine Learning, AI Agents, and Autonomy // Egor Kraev // #282 Jan 08, 2025

Show notes

For three years, Egor has been bringing the power of AI to bear at Wise, across domains as varied as trading algorithms for Treasury, fraud detection, experiment analysis, causal inference, and, recently, the numerous applications unlocked by large language models. Open-source projects initiated and guided by Egor include wise-pizza, causaltune, and neural-lifetimes, with more on the way.

Machine Learning, AI Agents, and Autonomy // MLOps Podcast #282 with Egor Kraev, Head of AI at Wise Plc.

// Abstract

Demetrios chats with Egor Kraev, principal AI scientist at Wise, about integrating large language models (LLMs) to enhance ML pipelines and humanize data interactions. Egor discusses his open-source MotleyCrew framework, career journey, and insights into AI's role in fintech, highlighting its potential to streamline operations and transform organizations.

// Bio

Egor first learned mathematics in the Russian tradition, then continued his studies at ETH Zurich and the University of Maryland. Egor has been doing data science since the last century, including economic and human development data analysis for nonprofits in the US, the UK, and Ghana, and 10 years as a quant, solutions architect, and occasional trader at UBS, then Deutsche Bank. Following last decade's explosion in AI techniques, Egor became Head of AI at Mosaic Smart Data Ltd, and for the last four years has been bringing the power of AI to bear at Wise, in a variety of domains, from fraud detection to trading algorithms and causal inference for A/B testing and marketing. Egor has multiple side projects, such as RL for molecular optimization, GenAI for generating and solving high school math problems, and others.

// MLOps Swag/Merch

https://shop.mlops.community/

// Related Links

https://github.com/transferwise/wise-pizza https://github.com/py-why/causaltune https://www.linkedin.com/posts/egorkraev_a-talk-on-experimentation-best-practices-activity-7092158531247755265-q0kt?utm_source=share&utm_medium=member_desktop

--------------- ✌️Connect With Us ✌️ -------------

Join our Slack community: https://go.mlops.community/slack

Catch all episodes, blogs, newsletters, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

Connect with Egor on LinkedIn: https://www.linkedin.com/in/egorkraev/

Timestamps:

[00:00] Egor's preferred coffee

[00:40] Takeaways

[01:56] Swiss pirate party

[04:08] Ghana Work and Wise

[07:21] AI Bridge Unstructured Structured

[12:40] AI in Wise Fintech

[16:26] AI as Dag Node

[19:38] Causal Inference with ML

[25:35] Insight validation process

[31:00] Agent UX and challenges

[37:11] Monthly crew vision

[41:00] Montley Crew Framework Abstraction

[42:57] Causal tune

[47:38] Wise email campaigns

[50:29] Organizational structures in AI

[53:19] AI uncertainty and hallucinations

[55:08] Decentralized org structures

[1:01:34] Permaculture and team synergy

[1:03:29] Wrap up

Re-Platforming Your Tech Stack // Michelle Marie Conway & Andrew Baker // #281 Jan 03, 2025

Show notes

Re-Platforming Your Tech Stack // MLOps Podcast #281 with Michelle Marie Conway, Lead Data Scientist at Lloyds Banking Group and Andrew Baker, Data Science Delivery Lead at Lloyds Banking Group.

// Abstract

Lloyds Banking Group is on a mission to embrace the power of cloud and unlock the opportunities that it provides. Andrew, Michelle, and their MLOps team have been on a journey over the last 12 months to take their portfolio of circa 10 Machine Learning models in production and migrate them from an on-prem solution to a cloud-based environment. During the podcast, Michelle and Andrew share their reflections as well as some dos (and don’ts!) of managing the migration of an established portfolio.

// Bio

Michelle Marie ConwayMichelle is a Lead Data Scientist in the high-performance data science team at Lloyds Banking Group. With deep expertise in managing production-level Python code and machine learning models, she has worked alongside fellow senior manager Andrew to drive the bank's transition to the Google Cloud Platform. Together, they have played a pivotal role in modernising the ML portfolio in collaboration with a remarkable ML Ops team.

Originally from Ireland and now based in London, Michelle blends her technical expertise with a love for the arts. Andrew Baker graduated from the University of Birmingham with a first-class honours degree in Mathematics and Music with a Year in Computer Science and joined Lloyds Banking Group on their Retail graduate scheme in 2015.

Since 2021, Andrew has worked in the world of data, firstly in shaping the Retail data strategy and most recently as a Data Science Delivery Lead, growing and managing a team of Data Scientists and Machine Learning Engineers. He has built a high-performing team responsible for building and maintaining ML models in production for the Consumer Lending division of the bank.

Andrew is motivated by the role that data science and ML can play in transforming the business and its processes, and is focused on balancing the power of ML with the need for simplicity and explainability that enables business users to engage with the opportunities that exist in this space and the demands of a highly regulated environment.

// MLOps Swag/Merch

https://shop.mlops.community/

--------------- ✌️Connect With Us ✌️ -------------

Join our Slack community: https://go.mlops.community/slack

Catch all episodes, blogs, newsletters, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

Connect with Michelle on LinkedIn: https://www.linkedin.com/in/michelle--conway/

Connect with Andrew on LinkedIn: https://www.linkedin.com/in/andrew-baker-90952289

Timestamps:

[00:00] Michelle and Andrew's preferred coffee

[00:25] Takeaways

[01:03] Please like, share, leave a review, and subscribe to our MLOps channels!

[01:26] 5B Cloud Investment!

[04:54] MLOps end-to-end process

[06:21] ML model handoff evolution

[09:14] ML project finalization questions

[14:15] Cloud migration journey

[17:48] Cloud Flexibility vs Rigidity

[19:18] On-prem to Cloud Transition

[23:02] Vertex AI vs Pointed Solutions

[27:47] Standardizing Model Documentation

[30:17] Cloud Optimization and Efficiency

[35:47] Tech debt challenges overcome

[42:11] Chaos engineering insights

[46:11] MLOps platform team collaboration

[51:02] Wrap up

Holistic Evaluation of Generative AI Systems // Jineet Doshi // #280 Dec 23, 2024

Show notes

Jineet Doshi is an award-winning Scientist, Machine Learning Engineer, and Leader at Intuit with over 7 years of experience. He has a proven track record of leading successful AI projects and building machine-learning models from design to production across various domains, which have impacted 100 million customers and significantly improved business metrics, leading to millions of dollars of impact.

Holistic Evaluation of Generative AI Systems // MLOps Podcast #280 with Jineet Doshi, Staff AI Scientist or AI Lead at Intuit.

// Abstract

Evaluating LLMs is essential in establishing trust before deploying them to production. Even post-deployment, evaluation is essential to ensure LLM outputs meet expectations, making it a foundational part of LLMOps. However, evaluating LLMs remains an open problem. Unlike traditional machine learning models, LLMs can perform a wide variety of tasks, such as writing poems, Q&A, summarization, etc. This leads to the question of how to evaluate a system with such broad intelligence capabilities. This talk covers the various approaches for evaluating LLMs, such as classic NLP techniques, red teaming, and newer ones like using LLMs as a judge, along with the pros and cons of each. The talk includes an evaluation of complex GenAI systems like RAG and Agents. It also covers evaluating LLMs for safety and security, and the need to have a holistic approach for evaluating these very capable models.

// Bio

Jineet Doshi is an award-winning AI Lead and Engineer with over 7 years of experience. He has a proven track record of leading successful AI projects and building machine learning models from design to production across various domains, which have impacted millions of customers and have significantly improved business metrics, leading to millions of dollars of impact. He is currently an AI Lead at Intuit, where he is one of the architects and developers of their Generative AI platform, which is serving Generative AI experiences for more than 100 million customers around the world. Jineet is also a guest lecturer at Stanford University as part of their Building LLM Applications class. He is on the Advisory Board of the University of San Francisco’s AI Program. He holds multiple patents in the field, is on the steering committee of MLOps World Conference, and has also co-chaired workshops at top AI conferences like KDD. He holds a Master's degree from Carnegie Mellon University.

// MLOps Swag/Merch

https://shop.mlops.community/

// Related Links

Website: https://www.intuit.com/

--------------- ✌️Connect With Us ✌️ -------------

Join our Slack community: https://go.mlops.community/slack

Catch all episodes, blogs, newsletters, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

Connect with Jineet on LinkedIn: https://www.linkedin.com/in/jineetdoshi/

Timestamps:

[00:00] Jineet's preferred coffee

[00:20] Takeaways

[01:24] Please like, share, leave a review, and subscribe to our MLOps channels!

[01:36] LLM evaluation at scale

[03:13] Challenges in GenAI evaluation

[08:09] Eval products vs platforms

[09:28] Evaluation methods for models

[14:03] NLP evaluation techniques

[25:06] LLM as a judge/jury

[31:56] LLMs and pizza brainstorming

[34:07] Cost per answer breakdown

[38:29] Evaluating RAG systems

[44:00] Testing with LLMs and humans

[49:23] Evaluating AI use cases

[54:19] AI workflow stress testing

[55:40] Wrap up

Unleashing Unconstrained News Knowledge Graphs to Combat Misinformation // Robert Caulk // #279 Dec 20, 2024

Show notes

Robert Caulk is responsible for directing software development, enabling research, coordinating company projects, quality control, proposing external collaborations, and securing funding. He believes firmly in open-source, having spent 12 years accruing over 1000 academic citations building open-source software in domains such as machine learning, image analysis, and coupled physical processes. He received his Ph.D. from Université Grenoble Alpes, France, in computational mechanics.

Unleashing Unconstrained News Knowledge Graphs to Combat Misinformation // MLOps Podcast #279 with Robert Caulk, Founder of Emergent Methods.

// Abstract

Indexing hundreds of thousands of news articles per day into a knowledge graph (KG) was previously impossible due to the strict requirement that high-level reasoning, general world knowledge, and full-text context *must* be present for proper KG construction.

The latest tools now enable such general world knowledge and reasoning to be applied cost-effectively to high volumes of news articles. Beyond the low cost of processing these news articles, these tools are also opening up a new, controversial approach to KG building - unconstrained KGs. We discuss the construction and exploration of the largest news-knowledge-graph on the planet - hosted on an endpoint at AskNews.app. During the talk, we aim to highlight some of the sacrifices and benefits that go hand-in-hand with using the infamous unconstrained KG approach.

We conclude the talk by explaining how knowledge graphs like these help to mitigate misinformation. We provide some examples of how our clients are using this graph, such as generating sports forecasts, generating better social media posts, generating regional security alerts, and combating human trafficking.

// Bio

Robert is the founder of Emergent Methods, where he directs research and software development for large-scale applications. He is currently overseeing the structuring of hundreds of thousands of news articles per day in order to build the best news retrieval API in the world: https://asknews.app.

// MLOps Swag/Merch

https://shop.mlops.community/

// Related Links

Website: https://emergentmethods.ai

News Retrieval API: https://asknews.app

--------------- ✌️Connect With Us ✌️ -------------

Join our Slack community: https://go.mlops.community/slack

Catch all episodes, blogs, newsletters, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

Connect with Rob on LinkedIn: https://www.linkedin.com/in/rcaulk/

Timestamps:

[00:00] Rob's preferred coffee

[00:05] Takeaways

[00:55] Please like, share, leave a review, and subscribe to our MLOps channels!

[01:00] Join our Local Organizer Carousel!

[02:15] Knowledge Graphs and ontology

[07:43] Ontology vs Noun Approach

[12:46] Ephemeral tools for efficiency

[17:26] Oracle to PostgreSQL migration

[22:20] MEM Graph life cycle

[29:14] Knowledge Graph Investigation Insights

[33:37] Fine-tuning and distillation of LLMs

[39:28] DAG workflow and quality control

[46:23] Crawling nodes with Phi 3 Llama

[50:05] AI pricing risks and strategies

[56:14] Data labeling and poisoning

[58:34] API costs vs News latency

[1:02:10] Product focus and value

[1:04:52] Ensuring reliable information

[1:11:01] Podcast transcripts as News

[1:13:08] Ontology trade-offs explained

[1:15:00] Wrap up

LLM Distillation and Compression // Guanhua Wang // #278 Dec 17, 2024

Show notes

Guanhua Wang is a Senior Researcher in DeepSpeed Team at Microsoft. Before Microsoft, Guanhua earned his Computer Science PhD from UC Berkeley. Domino: Communication-Free LLM Training Engine

// MLOps Podcast #278 with Guanhua "Alex" Wang, Senior Researcher at Microsoft.

// Abstract

Given the popularity of generative AI, Large Language Models (LLMs) often consume hundreds or thousands of GPUs to parallelize and accelerate the training process. Communication overhead becomes more pronounced when training LLMs at scale. To eliminate communication overhead in distributed LLM training, we propose Domino, which provides a generic scheme to hide communication behind computation. By breaking the data dependency of a single batch training into smaller independent pieces, Domino pipelines these independent pieces of training and provides a generic strategy of fine-grained communication and computation overlapping. Extensive results show that compared with Megatron-LM, Domino achieves up to 1.3x speedup for LLM training on Nvidia DGX-H100 GPUs.

// Bio

Guanhua Wang is a Senior Researcher in the DeepSpeed team at Microsoft. His research focuses on large-scale LLM training and serving. Previously, he led the ZeRO++ project at Microsoft, which helped reduce over half of model training time inside Microsoft and LinkedIn. He also led and was a major contributor to Microsoft Phi-3 model training. He holds a CS PhD from UC Berkeley, advised by Prof Ion Stoica.

// MLOps Swag/Merchhttps://shop.mlops.community/

// Related Links

Website: https://guanhuawang.github.io/

DeepSpeed hiring: https://www.microsoft.com/en-us/research/project/deepspeed/opportunities/

Large Model Training and Inference with DeepSpeed // Samyam Rajbhandari // LLMs in Prod Conference: https://youtu.be/cntxC3g22oU

--------------- ✌️Connect With Us ✌️ -------------

Join our Slack community: https://go.mlops.community/slack

Catch all episodes, blogs, newsletters, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

Connect with Guanhua on LinkedIn: https://www.linkedin.com/in/guanhua-wang/

Timestamps:

[00:00] Guanhua's preferred coffee

[00:17] Takeaways

[01:36] Please like, share, leave a review, and subscribe to our MLOps channels!

[01:47] Phi model explanation

[06:29] Small Language Models optimization challenges

[07:29] DeepSpeed overview and benefits

[10:58] Crazy unimplemented crazy AI ideas

[17:15] Post-training vs QAT

[19:44] Quantization over distillation

[24:15] Using Lauras

[27:04] LLM scaling sweet spot

[28:28] Quantization techniques

[32:38] Domino overview

[38:02] Training performance benchmark

[42:44] Data dependency-breaking strategies

[49:14] Wrap up

AI's Next Frontier // Aditya Naganath // #277 Dec 11, 2024

Show notes

Thanks to the High Signal Podcast by Delphina: https://go.mlops.community/HighSignalPodcast

Aditya Naganath is an experienced investor currently working with Kleiner Perkins. He has a passion for connecting with people over coffee and discussing various topics related to tech, products, ideas, and markets.

AI's Next Frontier // MLOps Podcast #277 with Aditya Naganath, Principal at Kleiner Perkins.

// Abstract

LLMs have ushered in an unmistakable supercycle in the world of technology. The low-hanging use cases have largely been picked off. The next frontier will be AI coworkers who sit alongside knowledge workers, doing work side by side. At the infrastructure level, one of the most important primitives invented by man - the data center- is being fundamentally rethought in this new wave.

// Bio

Aditya Naganath joined Kleiner Perkins’ investment team in 2022 with a focus on artificial intelligence, enterprise software applications, infrastructure, and security. Prior to joining Kleiner Perkins, Aditya was a product manager at Google, focusing on growth initiatives for the next billion users team. He previously was a technical lead at Palantir Technologies and formerly held software engineering roles at Twitter and Nextdoor, where he was a Kleiner Perkins fellow. Aditya earned a patent during his time at Twitter for a technical analytics product he co-created.

Originally from Mumbai, India, Aditya graduated magna cum laude from Columbia University with a bachelor’s degree in Computer Science and an MBA from Stanford University. Outside of work, you can find him playing guitar with a hard rock band, competing in chess or on the squash courts, and fostering puppies. He is also an avid poker player.

// MLOps Swag/Merch

https://shop.mlops.community/

// Related Links

Faith's Hymn by Beautiful Chorus: ⁠⁠https://open.spotify.com/track/1bDv6grQB5ohVFI8UDGvKK?si=4b00752eaa96413b⁠⁠

Substack: ⁠⁠https://adityanaganath.substack.com/?utm_source=substack&utm_medium=web&utm_campaign=substack_profile⁠⁠
With thanks to the High Signal Podcast by Delphina: https://go.mlops.community/HighSignalPodcast
Building the Future of AI in Software Development // Varun Mohan // MLOps Podcast #195 - ⁠⁠https://youtu.be/1DJKq8StuTo⁠⁠
Do Re MI for Training Metrics: Start at the Beginning // Todd Underwood // AIQCON - ⁠⁠https://youtu.be/DxyOlRdCofo

--------------- ✌️Connect With Us ✌️ -------------

Join our Slack community: https://go.mlops.community/slack

Catch all episodes, blogs, newsletters, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

Connect with Aditya on LinkedIn: https://www.linkedin.com/in/aditya-naganath/

Timestamps:

[00:00] Aditya's preferred coffee

[00:07] Takeaways

[01:33] Please like, share, leave a review, and subscribe to our MLOps channels!

[01:44] Investing in the AI frenzy

[02:23] Team dynamics insights

[04:57] Evaluating fad companies

[05:39] AI infrastructure and MLOps

[07:58] Challenges in MLOps Standardization

[08:59] ML vs Data platforms

[14:08] LLMOps vs MLOps

[17:52] Together vs Competitors

[21:19] AI application areas

[27:43] High Signal Podcast by Delphina Ad

[28:36] AI co-pilot in coding

[33:50] LLM providers overview

[37:30] AI paradigms and competition

[46:01] GPU failure management strategies

[52:58] Inference drives cloud revenue

[55:37] Wrap up

PyTorch for Control Systems and Decision Making // Vincent Moens // #276 Dec 04, 2024

Show notes

Dr Vincent Moens is an Applied Machine Learning Research Scientist at Meta and an author of TorchRL and TensorDict in Pytorch. PyTorch for Control Systems and Decision Making

// MLOps Podcast #276 with Vincent Moens, Research Engineer at Meta.

// Abstract

PyTorch is widely adopted across the machine learning community for its flexibility and ease of use in applications such as computer vision and natural language processing. However, supporting reinforcement learning, decision-making, and control communities is equally crucial, as these fields drive innovation in areas like robotics, autonomous systems, and game-playing. This podcast explores the intersection of PyTorch and these fields, covering practical tips and tricks for working with PyTorch, an in-depth look at TorchRL, and discussions on debugging techniques, optimization strategies, and testing frameworks. By examining these topics, listeners will understand how to effectively use PyTorch for control systems and decision-making applications.

// Bio

Vincent Moens is a research engineer on the PyTorch core team at Meta, based in London. As the maintainer of TorchRL (https://github.com/pytorch/rl) and TensorDict (https://github.com/pytorch/tensordict), Vincent plays a key role in supporting the decision-making community within the PyTorch ecosystem. Alongside his technical role in the PyTorch community, Vincent also actively contributes to AI-related research projects.

Before joining Meta, Vincent worked as an ML researcher at Huawei and AIG. Vincent holds a Medical Degree and a PhD in Computational Neuroscience.

// MLOps Swag/Merch

https://shop.mlops.community/

// Related Links

Musical recommendation: https://open.spotify.com/artist/1Uff91EOsvd99rtAupatMP?si=jVkoFiq8Tmq0fqK_OIEglg

Website: github.com/vmoens

TorchRL: https://github.com/pytorch/rl

TensorDict: https://github.com/pytorch/tensordict

LinkedIn post: https://www.linkedin.com/posts/vincent-moens-9bb91972_join-the-tensordict-discord-server-activity-7189297643322253312-Wo9J?utm_source=share&utm_medium=member_desktop

--------------- ✌️Connect With Us ✌️ -------------

Join our Slack community: https://go.mlops.community/slack

Catch all episodes, blogs, newsletters, and more: https://mlops.community/