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    Technology

    Agentic Conversations (formally mlops.community)

    Relaxed conversations and technical deep dives around AI Agents. This Show is brought to you by the Agentic AI Foundation where the leading agentic open-source projects like MCP, Agents.md, and Goose live. See more at aaif.io

    Advertise

    Copyright: © Demetrios Brinkmann

    • Apple Podcasts
    • Google Play
    • Spotify

    Latest Episodes:
    AI-Driven Code: Navigating Due Diligence & Transparency in MLOps // Matt van Itallie // #275 Nov 29, 2024
    Show notes

    Matt Van Itallieis the founder and CEO ofSema. Prior to this, they were the Vice President of Customer Support and Customer Operations at Social Solutions.

    AI-Driven Code: Navigating Due Diligence & Transparency in MLOps // MLOps Podcast #275 with Matt van Itallie, Founder and CEO of Sema.


    // Abstract

    Matt Van Itallie, founder and CEO of Sema, discusses how comprehensive codebase evaluations play a crucial role in MLOps and technical due diligence. He highlights the impact of Generative AI on code transparency and explains the Generative AI Bill of Materials (GBOM), which helps identify and manage risks in AI-generated code. This talk offers practical insights for technical and non-technical audiences, showing how proper diligence can enhance value and mitigate risks in machine learning operations.


    // Bio

    Matt Van Itallie is the Founder and CEO of Sema. He and his team have developed Comprehensive Codebase Scans, the most thorough and easily understandable assessment of a codebase and engineering organization. These scans are crucial for private equity and venture capital firms looking to make informed investment decisions. Sema has evaluated code within organizations that have a collective value of over $1 trillion. In 2023, Sema served 7 of the 9 largest global investors, along with market-leading strategic investors, private equity, and venture capital firms, providing them with critical insights.

    In addition, Sema is at the forefront of Generative AI Code Transparency, which measures how much code created by GenAI is in a codebase. They are the inventors behind the Generative AI Bill of Materials (GBOM), an essential resource for investors to understand and mitigate risks associated with AI-generated code.

    Before founding Sema, Matt was a Private Equity operating executive and a management consultant at McKinsey. He graduated from Harvard Law School and has had some interesting adventures, like hiking a third of the Appalachian Trail and biking from Boston to Seattle.

    Full bio: https://alistar.fm/bio/matt-van-itallie


    // MLOps Swag/Merch

    https://shop.mlops.community/


    // Related LinksWebsite: https://en.m.wikipedia.org/wiki/Michael_Gschwind


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

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

    Follow us on Twitter: @mlopscommunity

    Sign up for the next meetup: https://go.mlops.community/register

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


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

    Connect with Matt on LinkedIn: https://www.linkedin.com/in/mvi/


    Timestamps:

    [00:00] Matt's preferred coffee

    [00:07] Takeaways

    [01:25] Please like, share, leave a review, and subscribe to our MLOps channels!

    [02:23] Code-based scans overview

    [09:16] FinOps Automation and Recommendations

    [12:03] Code quality evaluation layers

    [16:10] Bridging Tech-Biz gap

    [21:16] Measurable insights for leadership

    [29:09] Startup prioritization and metrics

    [32:53] GenAI Code Deletion Insights

    [37:07] AI vs Developer Expertise

    [41:47] GenAI Copyright Concerns

    [46:41] Open Source Risks AI

    [53:57] Code Defensibility and Acquisitions

    [56:19] Wrap up


    PyTorch's Combined Effort in Large Model Optimization // Michael Gschwind // #274 Nov 26, 2024
    Show notes

    Dr. Michael Gschwind is a Director / Principal Engineer for PyTorch at Meta Platforms. At Meta, he led the rollout of GPU Inference for production services.


    // MLOps Podcast #274 with Michael Gschwind, Software Engineer, Software Executive at Meta Platforms.


    // Abstract

    Explore the role in boosting model performance, on-device AI processing, and collaborations with tech giants like ARM and Apple. Michael shares his journey from gaming console accelerators to AI, emphasizing the power of community and innovation in driving advancements.


    // Bio

    Dr. Michael Gschwind is a Director / Principal Engineer for PyTorch at Meta Platforms. At Meta, he led the rollout of GPU Inference for production services. He led the development of MultiRay and Textray, the first deployment of LLMs at a scale exceeding a trillion queries per day shortly after its rollout. He created the strategy and led the implementation of PyTorch donation optimization with Better Transformers and Accelerated Transformers, bringing Flash Attention, PT2 compilation, and ExecuTorch into the mainstream for LLMs and GenAI models. Most recently, he led the enablement of large language models on-device AI with mobile and edge devices.


    // MLOps Swag/Merch

    https://mlops-community.myshopify.com/

    // Related Links

    Website: https://en.m.wikipedia.org/wiki/Michael_Gschwind


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

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

    Follow us on Twitter: @mlopscommunity

    Sign up for the next meetup: https://go.mlops.community/register

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


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

    Connect with Michael on LinkedIn: https://www.linkedin.com/in/michael-gschwind-3704222/?utm_source=share&utm_campaign=share_via&utm_content=profile&utm_medium=ios_app


    Timestamps:

    [00:00] Michael's preferred coffee

    [00:21] Takeaways

    [01:59] Please like, share, leave a review, and subscribe to our MLOps channels!

    [02:10] Gaming to AI Accelerators

    [11:34] Torch Chat goals

    [18:53] Pytorch benchmarking and competitiveness

    [21:28] Optimizing MLOps models

    [24:52] GPU optimization tips

    [29:36] Cloud vs On-device AI

    [38:22] Abstraction across devices

    [42:29] PyTorch developer experience

    [45:33] AI and MLOps-related antipatterns

    [48:33] When to optimize

    [53:26] Efficient edge AI models

    [56:57] Wrap up


    LLMs to agents: The Beauty & Perils of Investing in GenAI // VC Panel // Agents in Production Nov 22, 2024
    Show notes

    //Abstract In this segment, the Panel will dive into the evolving landscape of AI, where large language models (LLMs) power the next wave of intelligent agents. In this engaging panel, leading investors Meera (Redpoint), George (Sequoia), and Sandeep (Prosus Ventures) discuss the promise and pitfalls of AI in production. From transformative industry applications to the challenges of scalability, costs, and shifting business models, this session unpacks the metrics and insights shaping GenAI's future. Whether you're excited about AI's potential or wary of its complexities, this is a must-watch for anyone exploring the cutting edge of tech investment. //Bio Host: Paul van der Boor Senior Director Data Science @ Prosus Group Sandeep Bakshi Head of Investments, Europe @ Prosus Meera Clark Principal @ Redpoint Ventures George Robson Partner @ Sequoia Capital A Prosus | MLOps Community Production


    We Can All Be AI Engineers and We Can Do It with Open Source Models // Luke Marsden // #273 Nov 20, 2024
    Show notes

    Luke Marsden, is a passionate technology leader. Experienced in consultant, CEO, CTO, tech lead, product, sales, and engineering roles. Proven ability to conceive and execute a product vision from strategy to implementation, while iterating on product-market fit.


    We Can All Be AI Engineers and We Can Do It with Open Source Models // MLOps Podcast #273 with Luke Marsden, CEO of HelixML.


    // Abstract

    In this podcast episode, Luke Marsden explores practical approaches to building Generative AI applications using open-source models and modern tools. Through real-world examples, Luke breaks down the key components of GenAI development, from model selection to knowledge and API integrations, while highlighting the data privacy advantages of open-source solutions.


    // Bio

    Hacker & entrepreneur. Founder at helix.ml. Career spanning DevOps, MLOps, and now LLMOps. Working on bringing business value to local, open-source LLMs.


    // MLOps Swag/Merch

    https://mlops-community.myshopify.com/

    // Related LinksWebsite: https://helix.ml

    About open source AI: https://blog.helix.ml/p/the-open-source-ai-revolution

    Ratatat Cream on Chrome: https://open.spotify.com/track/3s25iX3minD5jORW4KpANZ?si=719b715154f64a5f


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

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

    Follow us on Twitter: @mlopscommunity

    Sign up for the next meetup: https://go.mlops.community/register

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


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

    Connect with Luke on LinkedIn: https://www.linkedin.com/in/luke-marsden-71b3789/


    Timestamps:

    [00:00] Michael's preferred coffee

    [00:21] Takeaways

    [01:59] Please like, share, leave a review, and subscribe to our MLOps channels!

    [02:10] Gaming to AI Accelerators

    [11:34] Torch Chat goals

    [18:53] Pytorch benchmarking and competitiveness

    [21:28] Optimizing MLOps models

    [24:52] GPU optimization tips

    [29:36] Cloud vs On-device AI

    [38:22] Abstraction across devices

    [42:29] PyTorch developer experience

    [45:33] AI and MLOps-related antipatterns

    [48:33] When to optimize

    [53:26] Efficient edge AI models

    [56:57] Wrap up


    Exploring AI Agents: Voice, Visuals, and Versatility // Panel // Agents in Production Nov 15, 2024
    Show notes

    // Abstract

    This panel speaks about the diverse landscape of AI agents, focusing on how they integrate voice interfaces, GUIs, and small language models to enhance user experiences. They'll also examine the roles of these agents in various industries, highlighting their impact on productivity, creativity, and user experience, and how these empower developers to build better solutions while addressing challenges like ensuring consistent performance and reliability across different modalities when deploying AI agents in production.


    //Bio


    Speakers:

    Diego Oppenheimer - Co-founder @ Guardrails AI

    Jazmia Henry - Founder and CEO @ Iso AI

    Rogerio Bonatti - Researcher @ Microsoft

    Julia Kroll - Applied Engineer @ Deepgram

    Joshua Alphonse - Director of Developer Relations @ PremAIA Prosus | MLOps Community Production


    The Impact of UX Research in the AI Space // Lauren Kaplan // #272 Nov 13, 2024
    Show notes

    Lauren Kaplan is a sociologist and writer. She earned her PhD in Sociology at Goethe University Frankfurt and worked as a researcher at the University of Oxford and UC Berkeley. The Impact of UX Research in the AI Space


    // MLOps Podcast #272 with Lauren Kaplan, Sr UX Researcher.


    // Abstract

    In this MLOps Community podcast episode, Demetrios and UX researcher Lauren Kaplan explore how UX research can transform AI and ML projects by aligning insights with business goals and enhancing user and developer experiences. Kaplan emphasizes the importance of stakeholder alignment, proactive communication, and interdisciplinary collaboration, especially in adapting company culture post-pandemic. They discuss UX’s growing relevance in AI, challenges like bias, and the use of AI in research, underscoring the strategic value of UX in driving innovation and user satisfaction in tech.


    // Bio

    Lauren is a sociologist and writer. She earned her PhD in Sociology at Goethe University Frankfurt and worked as a researcher at the University of Oxford and UC Berkeley. Passionate about homelessness and AI, Lauren joined UCSF and later Meta. Lauren recently led UX research at a global AI chip startup and is currently seeking new opportunities to further her work in UX research and AI. At Meta, Lauren led UX research for 1) Privacy-Preserving ML and 2) PyTorch.

    Lauren has worked on NLP projects such as Word2Vec analysis of historical HIV/AIDS documents presented at TextXD, UC Berkeley, 2019. Lauren is passionate about understanding technology and advocating for the people who create and consume AI. Lauren has published over 30 peer-reviewed research articles in domains including psychology, medicine, sociology, and more.”


    // MLOps Swag/Merchhttps:

    mlops-community.myshopify.com/


    // Related Links

    Podcast on AI UX https://open.substack.com/pub/aistudios/p/how-to-do-user-research-for-ai-products?r=7hrv8&utm_medium=ios

    2024 State of AI Infra at Scale Research Report

    https://ai-infrastructure.org/wp-content/uploads/2024/03/The-State-of-AI-Infrastructure-at-Scale-2024.pdf

    Privacy-Preserving ML UX Public Article

    https://www.ttclabs.net/research/how-to-help-people-understand-privacy-enhancing-technologies

    Homelessness research and more: https://scholar.google.com/citations?user=24zqlwkAAAAJ&hl=en

    Agents in Production: https://home.mlops.community/public/events/aiagentsinprod

    Mk.gee Si (Bonus Track): https://open.spotify.com/track/1rukW2Wxnb3GGlY0uDWIWB?si=4d5b0987ad55444a


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

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

    Follow us on Twitter: @mlopscommunity

    Sign up for the next meetup: https://go.mlops.community/register

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


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

    Connect with Lauren on LinkedIn: https://www.linkedin.com/in/laurenmichellekaplan?utm_source=share&utm_campaign=share_via&utm_content=profile&utm_medium=ios_app


    Timestamps:

    [00:00] Lauren's Introduction

    [00:07] Join the AI Agents in Production Conference on November 13!

    [01:26] Takeaways

    [02:13] Please like, share, leave a review, and subscribe to our MLOps channels!

    [02:26] UX research overview

    [03:31] UX research methods

    [06:22] Effective interview strategies

    [10:33] Broader UX Understanding

    [14:42] Data Synthesis and Prioritization

    [20:28] Measuring Impact in ML

    [27:32] Phased Project Rollout

    [31:36] UXR in Startups vs Big Companies

    [40:32] AI Research Project Scope

    [48:03] Increasing UX Maturity

    [51:15] Career Paths in UX Research

    [55:51] UX Beyond Tech

    [59:31] Engineering User Research Tools

    [1:06:16] Wrap up


    EU AI Act - Navigating New Legislation // Petar Tsankov // MLOps Podcast #271 Nov 01, 2024
    Show notes

    Dr. Petar Tsankov is a researcher and entrepreneur in the field of Computer Science and Artificial Intelligence (AI). EU AI Act - Navigating New Legislation


    // MLOps Podcast #271 with Petar Tsankov, Co-Founder and CEO of LatticeFlow AI.


    Big thanks to LatticeFlow for sponsoring this episode!


    // Abstract

    Dive into AI risk and compliance. Petar Tsankov, a leader in AI safety, talks about turning complex regulations into clear technical requirements and the importance of benchmarks in AI compliance, especially with the EU AI Act. We explore his work with big AI players and the EU on safer, compliant models, covering topics from multimodal AI to managing AI risks. He also shares insights on "Comply," an open-source tool for checking AI models against EU standards, making compliance simpler for AI developers. A must-listen for those tackling AI regulation and safety.


    // Bio

    Co-founder & CEO at LatticeFlow AI, building the world's first product enabling organizations to build performant, safe, and trustworthy AI systems. Before starting LatticeFlow AI, Petar was a senior researcher at ETH Zurich working on the security and reliability of modern systems, including deep learning models, smart contracts, and programmable networks.

    Petar has co-created multiple publicly available security and reliability systems that are regularly used:

    = ERAN, the world's first scalable verifier for deep neural networks: https://github.com/eth-sri/eran

    = VerX, the world's first fully automated verifier for smart contracts: https://verx.ch

    = Securify, the first scalable security scanner for Ethereum smart contracts: https://securify.ch

    = DeGuard, de-obfuscates Android binaries: http://apk-deguard.com

    = SyNET, the first scalable network-wide configuration synthesis tool: https://synet.ethz.ch

    Petar also co-founded ChainSecurity, an ETH spin-off that, within 2 years, became a leader in formal smart contract audits and was acquired by PwC Switzerland in 2020.


    // MLOps Swag/Merch

    https://mlops-community.myshopify.com/


    // Related Links

    Website: https://latticeflow.ai/


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

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

    Follow us on Twitter: @mlopscommunity

    Sign up for the next meetup: https://go.mlops.community/register

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


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

    Connect with Petar on LinkedIn: https://www.linkedin.com/in/petartsankov/


    Timestamps:

    [00:00] Petar's preferred coffee

    [00:13] Takeaways

    [01:12] AI Governance and EU Compliance

    [04:16] AI Governance and Model Management

    [09:48] AI Governance and Risk Management

    [11:38] COMPL-AI

    [15:02] EU AI Act Compliance Challenges

    [17:16] EU AI Act Actionability

    [22:26] Compliance and toxicity issues

    [25:48] Model benchmarking and metrics

    [28:13] Copyright and model evaluation

    [32:28] SOC AI certification

    [33:07] EU AI Act Gaps

    [37:05] Integrity, safety, and compliance

    [41:13] Benchmarking and Overfitting Concerns

    [43:15] Tiered compliance approaches

    [46:03] Bridging Law and Tech

    [48:00] Multimodal AI Feature

    [51:45] AI Risk and Mitigation

    [55:37] Future Directions for Multimodal Models

    [57:33] Wrap up


    Boosting LLM/RAG Workflows & Scheduling w/ Composable Memory and Checkpointing // Bernie Wu // #270 Oct 22, 2024
    Show notes

    Bernie Wu is VP of Business Development for MemVerge. He has 25+ years of experience as a senior executive for data center hardware and software infrastructure companies, including companies such as Conner/Seagate, Cheyenne Software, Trend Micro, FalconStor, Levyx, and MetalSoft.


    Boosting LLM/RAG Workflows & Scheduling w/ Composable Memory and Checkpointing // MLOps Podcast #270 with Bernie Wu, VP Strategic Partnerships/Business Development of MemVerge.


    // Abstract

    Limited memory capacity hinders the performance and potential of research and production environments utilizing Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) techniques. This discussion explores how leveraging industry-standard CXL memory can be configured as a secondary, composable memory tier to alleviate this constraint.


    We will highlight some recent work we’ve done in integrating this novel class of memory into LLM/RAG/vector database frameworks and workflows. Disaggregated shared memory is envisioned to offer high-performance, low-latency caches for model/pipeline checkpoints of LLM models, KV caches during distributed inferencing, LORA adaptors, and in-process data for heterogeneous CPU/GPU workflows. We expect to showcase these types of use cases in the coming months.


    // Bio

    Bernie is VP of Strategic Partnerships/Business Development for MemVerge. His focus has been on building partnerships in the AI/ML, Kubernetes, and CXL memory ecosystems. He has 25+ years of experience as a senior executive for data center hardware and software infrastructure companies, including companies such as Conner/Seagate, Cheyenne Software, Trend Micro, FalconStor, Levyx, and MetalSoft. He is also on the Board of Directors for Cirrus Data Solutions. Bernie has a BS/MS in Engineering from UC Berkeley and an MBA from UCLA.


    // MLOps Swag/Merch

    https://mlops-community.myshopify.com/


    // Related Links

    Website: www.memverge.com

    Accelerating Data Retrieval in Retrieval Augmentation Generation (RAG) Pipelines using CXL: https://memverge.com/accelerating-data-retrieval-in-rag-pipelines-using-cxl/

    Do Re MI for Training Metrics: Start at the Beginning // Todd Underwood // AIQCON: https://youtu.be/DxyOlRdCofo

    Handling Multi-Terabyte LLM Checkpoints // Simon Karasik // MLOps Podcast #228: https://youtu.be/6MY-IgqiTpg

    Compute Express Link (CXL) FPGA IP: https://www.intel.com/content/www/us/en/products/details/fpga/intellectual-property/interface-protocols/cxl-ip.htmlUltra Ethernet Consortium: https://ultraethernet.org/

    Unified Acceleration (UXL) Foundation: https://www.intel.com/content/www/us/en/developer/articles/news/unified-acceleration-uxl-foundation.html

    RoCE networks for distributed AI training at scale: https://engineering.fb.com/2024/08/05/data-center-engineering/roce-network-distributed-ai-training-at-scale/


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

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

    Follow us on Twitter: @mlopscommunity

    Sign up for the next meetup: https://go.mlops.community/register

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


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

    Connect with Bernie on LinkedIn: https://www.linkedin.com/in/berniewu/


    Timestamps:

    [00:00] Bernie's preferred coffee

    [00:11] Takeaways

    [01:37] First principles thinking focus

    [05:02] Memory Abundance Concept Discussion

    [06:45] Managing load spikes

    [09:38] GPU checkpointing challenges

    [16:29] Distributed memory problem solving

    [18:27] Composable and Virtual Memory

    [21:49] Interactive chat annotation

    [23:46] Memory elasticity in AI

    [27:33] GPU networking tests

    [29:12] GPU Scheduling workflow optimization

    [32:18] Kubernetes Extensions and Tools

    [37:14] GPU bottleneck analysis

    [42:04] Economical memory strategies

    [45:14] Elastic memory management strategies

    [47:57] Problem-solving approach

    [50:15] AI infrastructure elasticity evolution

    [52:33] RDMA and RoCE explained

    [54:14] Wrap up


    How to Systematically Test and Evaluate Your LLMs Apps // Gideon Mendels // #269 Oct 18, 2024
    Show notes

    Gideon Mendels is the Chief Executive Officer at Comet, the leading solution for managing machine learning workflows. How to Systematically Test and Evaluate Your LLMs Apps


    // MLOps Podcast #269 with Gideon Mendels, CEO of Comet.


    // Abstract

    When building LLM Applications, Developers need to take a hybrid approach from both ML and SW Engineering best practices. They need to define eval metrics and track their entire experimentation to see what is and is not working. They also need to define comprehensive unit tests for their particular use case so they can confidently check if their LLM App is ready to be deployed.


    // Bio

    Gideon Mendels is the CEO and co-founder of Comet, the leading solution for managing machine learning workflows from experimentation to production. He is a computer scientist, ML researcher and entrepreneur at his core. Before Comet, Gideon co-founded GroupWize, where they trained and deployed NLP models processing billions of chats. His journey with NLP and Speech Recognition models began at Columbia University and Google, where he worked on hate speech and deception detection.


    // MLOps Swag/Merch

    https://mlops-community.myshopify.com/


    // Related Links

    Website: https://www.comet.com/site/

    All the Hard Stuff with LLMs in Product Development // Phillip Carter // MLOps Podcast #170: https://youtu.be/DZgXln3v85s

    Opik by Comet: https://www.comet.com/site/products/opik/


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

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

    Follow us on Twitter: @mlopscommunity

    Sign up for the next meetup: https://go.mlops.community/register

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


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

    Connect with Gideon on LinkedIn: https://www.linkedin.com/in/gideon-mendels/


    Timestamps:

    [00:00] Gideon's preferred coffee

    [00:17] Takeaways

    [01:50] A huge shout-out to Comet ML for sponsoring this episode!

    [02:09] Please like, share, leave a review, and subscribe to our MLOps channels!

    [03:30] Evaluation metrics in AI

    [06:55] LLM Evaluation in Practice

    [10:57] LLM testing methodologies

    [16:56] LLM as a judge

    [18:53] OPIC track function overview

    [20:33] Tracking user response value

    [26:32] Exploring AI metrics integration

    [29:05] Experiment tracking and LLMs

    [34:27] Micro Macro collaboration in AI

    [38:20] RAG Pipeline Reproducibility Snapshot

    [40:15] Collaborative experiment tracking

    [45:29] Feature flags in CI/CD

    [48:55] Labeling challenges and solutions

    [54:31] LLM output quality alerts

    [56:32] Anomaly detection in model outputs

    [1:01:07] Wrap up


    Exploring the Impact of Agentic Workflows // Raj Rikhy // #268 Oct 15, 2024
    Show notes

    Raj Rikhy is a Senior Product Manager at Microsoft AI +R, enabling deep reinforcement learning use cases for autonomous systems. Previously, Raj was the Group Technical Product Manager in the CDO for Data Science and Deep Learning at IBM. Prior to joining IBM, Raj has been working in product management for several years - at Bitnami, Appdirect, and Salesforce.


    // MLOps Podcast #268 with Raj Rikhy, Principal Product Manager at Microsoft.


    // Abstract

    In this MLOps Community podcast, Demetrios chats with Raj Rikhy, Principal Product Manager at Microsoft, about deploying AI agents in production. They discuss starting with simple tools, setting clear success criteria, and deploying agents in controlled environments for better scaling. Raj highlights real-time uses like fraud detection and optimizing inference costs with LLMs, while stressing human oversight during early deployment to manage LLM randomness. The episode offers practical advice on deploying AI agents thoughtfully and efficiently, avoiding over-engineering, and integrating AI into everyday applications.


    // Bio

    Raj is a Senior Product Manager at Microsoft AI + R, enabling deep reinforcement learning use cases for autonomous systems. Previously, Raj was the Group Technical Product Manager in the CDO for Data Science and Deep Learning at IBM. Prior to joining IBM, Raj has been working in product management for several years - at Bitnami, Appdirect, and Salesforce.


    // MLOps Swag/Merch

    https://mlops-community.myshopify.com/


    // Related Links

    Website: https://www.microsoft.com/en-us/research/focus-area/ai-and-microsoft-research/


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

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

    Follow us on Twitter: @mlopscommunity

    Sign up for the next meetup: https://go.mlops.community/register

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


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

    Connect with Raj on LinkedIn: https://www.linkedin.com/in/rajrikhy/


    Timestamps:

    [00:00] Raj's preferred coffee

    [00:16] Takeaways

    [00:23] Join the AI Agents in Production Conference on November 13th!

    [01:25] Categorizing different agents

    [06:59] Agent environment frameworks

    [15:52] Debugging Strategies for Complex Systems

    [22:26] Evaluating Agent Frameworks Effectively

    [28:30] Defining success in projects

    [31:45] Process simplification benefits

    [35:32] Agent workflow use cases

    [39:29] Tinder for clothing recommendation

    [44:20] Speed Reliability Trade-offs in ML

    [48:06] Brilliant minds and doubts

    [48:50] Wrap up


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