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תוכן מסופק על ידי Demetrios. כל תוכן הפודקאסטים כולל פרקים, גרפיקה ותיאורי פודקאסטים מועלים ומסופקים ישירות על ידי Demetrios או שותף פלטפורמת הפודקאסט שלהם. אם אתה מאמין שמישהו משתמש ביצירה שלך המוגנת בזכויות יוצרים ללא רשותך, אתה יכול לעקוב אחר התהליך המתואר כאן https://he.player.fm/legal.
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Challenges Operationalizing ML (And Some Solutions) // Nathan Ryan Frank // #199
Manage episode 392291191 series 3241972
תוכן מסופק על ידי Demetrios. כל תוכן הפודקאסטים כולל פרקים, גרפיקה ותיאורי פודקאסטים מועלים ומסופקים ישירות על ידי Demetrios או שותף פלטפורמת הפודקאסט שלהם. אם אתה מאמין שמישהו משתמש ביצירה שלך המוגנת בזכויות יוצרים ללא רשותך, אתה יכול לעקוב אחר התהליך המתואר כאן https://he.player.fm/legal.
Nathan Ryan Frank is the Machine Learning Operations and platform Director of Grainger. Former Astrophysicist turned data scientist and machine learning engineer with a proven history of delivering results into production across a wide variety of domains while leading projects with international, cross-functional teams. MLOps podcast #199 with Nathan Ryan Frank, Director, Machine Learning Platform & Operations at WW Grainger, Challenges Operationalizing Machine Learning (And Some Solutions). // Abstract This talk details some common challenges and pitfalls when attempting to operationalize machine learning systems and discusses some simple solutions. We dive into the machine learning development workflow and cover topics such as team dynamics, communication issues between roles that don't share a common language, and approaching MLOps from an SRE/DevOps perspective. Similarly, the talk highlights some challenges unique to operationalizing machine learning, drawing distinctions where necessary to highlight a large amount of similarity. Finally, the talk offers some simple and practical guidance for those new to MLOps who want to understand where to start and how to adopt best practices in an evolving field. // Bio Nathan Frank is currently the Director of Machine Learning Platform and Operations at Grainger where he is building a team to support the Technology Group's expanding machine learning efforts. Prior to joining Grainger, Nathan led machine learning engineering efforts at Strong Analytics, a boutique data science and machine learning consulting firm, as well as machine learning platform and development teams at Stats Perform, a leader in sports data and technology. Nathan holds bachelor's and master's degrees in Astrophysics from UC - Santa Cruz and UNC-Chapel Hill, respectively. When not building machine learning systems, Nathan spends as much time as possible with his favorite person in the world, his wife, as well as their four kids and two dogs, and enjoys getting outside to hike or garden and baking bread. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://nrfrank.github.io/ Bisi: https://bisi.gitbook.io/bisi/ --------------- ✌️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 Nathan on LinkedIn: https://www.linkedin.com/in/nrfrank Timestamps: [00:00] Nathan's preferred coffee [00:40] Takeaways [02:00] Please leave a review in our comment sections! Please like, share, and subscribe to our MLOps channels! [03:00] Telescope for gamma-ray burst [07:31] Transition into ML [11:23] Stats-heavy US sports commentary [14:25] Building machine learning systems approach [20:02] ML Workflow Must-Haves [26:50] Love for tests [33:10] Test Writing Importance [34:37] Bridging Stakeholder Language Gap [43:04] Shared Language, Team Collaboration [47:28] Rapid fire questions [51:20] Wrap up
446 פרקים
שתפוManage episode 392291191 series 3241972
תוכן מסופק על ידי Demetrios. כל תוכן הפודקאסטים כולל פרקים, גרפיקה ותיאורי פודקאסטים מועלים ומסופקים ישירות על ידי Demetrios או שותף פלטפורמת הפודקאסט שלהם. אם אתה מאמין שמישהו משתמש ביצירה שלך המוגנת בזכויות יוצרים ללא רשותך, אתה יכול לעקוב אחר התהליך המתואר כאן https://he.player.fm/legal.
Nathan Ryan Frank is the Machine Learning Operations and platform Director of Grainger. Former Astrophysicist turned data scientist and machine learning engineer with a proven history of delivering results into production across a wide variety of domains while leading projects with international, cross-functional teams. MLOps podcast #199 with Nathan Ryan Frank, Director, Machine Learning Platform & Operations at WW Grainger, Challenges Operationalizing Machine Learning (And Some Solutions). // Abstract This talk details some common challenges and pitfalls when attempting to operationalize machine learning systems and discusses some simple solutions. We dive into the machine learning development workflow and cover topics such as team dynamics, communication issues between roles that don't share a common language, and approaching MLOps from an SRE/DevOps perspective. Similarly, the talk highlights some challenges unique to operationalizing machine learning, drawing distinctions where necessary to highlight a large amount of similarity. Finally, the talk offers some simple and practical guidance for those new to MLOps who want to understand where to start and how to adopt best practices in an evolving field. // Bio Nathan Frank is currently the Director of Machine Learning Platform and Operations at Grainger where he is building a team to support the Technology Group's expanding machine learning efforts. Prior to joining Grainger, Nathan led machine learning engineering efforts at Strong Analytics, a boutique data science and machine learning consulting firm, as well as machine learning platform and development teams at Stats Perform, a leader in sports data and technology. Nathan holds bachelor's and master's degrees in Astrophysics from UC - Santa Cruz and UNC-Chapel Hill, respectively. When not building machine learning systems, Nathan spends as much time as possible with his favorite person in the world, his wife, as well as their four kids and two dogs, and enjoys getting outside to hike or garden and baking bread. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://nrfrank.github.io/ Bisi: https://bisi.gitbook.io/bisi/ --------------- ✌️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 Nathan on LinkedIn: https://www.linkedin.com/in/nrfrank Timestamps: [00:00] Nathan's preferred coffee [00:40] Takeaways [02:00] Please leave a review in our comment sections! Please like, share, and subscribe to our MLOps channels! [03:00] Telescope for gamma-ray burst [07:31] Transition into ML [11:23] Stats-heavy US sports commentary [14:25] Building machine learning systems approach [20:02] ML Workflow Must-Haves [26:50] Love for tests [33:10] Test Writing Importance [34:37] Bridging Stakeholder Language Gap [43:04] Shared Language, Team Collaboration [47:28] Rapid fire questions [51:20] Wrap up
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MLOps.community
1 Greg Kamradt: Benchmarking Intelligence | ARC Prize 48:30
48:30 
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הפעל מאוחר יותר 
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לייק 
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הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיWhat makes a good AI benchmark? Greg Kamradt joins Demetrios to break it down—from human-easy, AI-hard puzzles to wild new games that test how fast models can truly learn. They talk hidden datasets, compute tradeoffs, and why benchmarks might be our best bet for tracking progress toward AGI. It’s nerdy, strategic, and surprisingly philosophical. // Bio Greg has mentored thousands of developers and founders, empowering them to build AI-centric applications.By crafting tutorial-based content, Greg aims to guide everyone from seasoned builders to ambitious indie hackers.Greg partners with companies during their product launches, feature enhancements, and funding rounds. His objective is to cultivate not just awareness, but also a practical understanding of how to optimally utilize a company's tools.He previously led Growth @ Salesforce for Sales & Service Clouds in addition to being early on at Digits, a FinTech Series-C company. // Related Links Website: https://gregkamradt.com/ YouTube channel: https://www.youtube.com/@DataIndependent ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore MLOps Swag/Merch: [https://shop.mlops.community/] Connect with Demetrios on LinkedIn: /dpbrinkm Connect with Greg on LinkedIn: /gregkamradt/ Timestamps: [00:00] Human-Easy, AI-Hard [05:25] When the Model Shocks Everyone [06:39] “Let’s Circle Back on That Benchmark…” [09:50] Want Better AI? Pay the Compute Bill [14:10] Can We Define Intelligence by How Fast You Learn? [16:42] Still Waiting on That Algorithmic Breakthrough [20:00] LangChain Was Just the Beginning [24:23] Start With Humans, End With AGI [29:01] What If Reality’s Just... What It Seems? [32:21] AI Needs Fewer Vibes, More Predictions [36:02] Defining Intelligence (No Pressure) [36:41] AI Building AI? Yep, We're Going There [40:13] Open Source vs. Prize Money Drama [43:05] Architecting the ARC Challenge [46:38] Agent 57 and the Atari Gauntlet…
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MLOps.community
1 Bridging the Gap Between AI and Business Data // Deepti Srivastava // #325 57:13
57:13 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי57:13
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיBridging the Gap Between AI and Business Data // MLOps Podcast #325 with Deepti Srivastava, Founder and CEO at Snow Leopard. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract I’m sure the MLOps community is probably aware – it's tough to make AI work in enterprises for many reasons, from data silos, data privacy and security concerns, to going from POCs to production applications. But one of the biggest challenges facing businesses today, that I particularly care about, is how to unlock the true potential of AI by leveraging a company’s operational business data. At Snow Leopard, we aim to bridge the gap between AI systems and critical business data that is locked away in databases, data warehouses, and other API-based systems, so enterprises can use live business data from any data source – whether it's database, warehouse, or APIs – in real time and on demand, natively. In this interview, I'd like to cover Snow Leopard’s intelligent data retrieval approach that can leverage business data directly and on-demand to make AI work. // Bio Deepti is the founder and CEO of Snow Leopard AI, a platform that helps teams build AI apps using their live business data, on-demand. She has nearly 2 decades of experience in data platforms and infrastructure. As Head of Product at Observable, Deepti led the 0→1 product and GTM strategy in the crowded data analytics market. Before that, Deepti was the founding PM for Google Spanner, growing it to thousands of internal customers (Ads, PlayStore, Gmail, etc.), before launching it externally as a seminal cloud database service. Deepti started her career as a distributed systems engineer in the RAC database kernel at Oracle. // Related Links Website: https://www.snowleopard.ai/ AI SQL Data Analyst // Donné Stevenson - https://youtu.be/hwgoNmyCGhQ ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore Join our Slack community [https://go.mlops.community/slack] Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register] MLOps Swag/Merch: [https://shop.mlops.community/] Connect with Demetrios on LinkedIn: /dpbrinkm Connect with Deepti on LinkedIn: /thedeepti/ Timestamps: [00:00] Deepti's preferred coffee [00:49] MLflow vs Kubeflow Debate [04:58] GenAI Data Integration Challenges [09:02] GenAI Sidecar Spicy Takes [14:07] Troubleshooting LLM Hallucinations [19:03] AI Overengineering and Hype [25:06] Self-Serve Analytics Governance [33:29] Dashboards vs Data Quality [37:06] Agent Database Context Control [43:00] LLM as Orchestrator [47:34] Tool Call Ownership Clarification [51:45] MCP Server Challenges [56:52] Wrap up…
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MLOps.community
1 The Creator of FastAPI’s Next Chapter // Sebastián Ramírez // #324 1:09:37
1:09:37 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי1:09:37
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיThe Creator of FastAPI’s Next Chapter // MLOps Podcast #324 with Sebastián Ramírez, Developer at FastAPI Labs. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract The creator of FastAPI is back with a new chapter—FastAPI Cloud. From building one of the most loved dev tools to launching a company, Sebastián Ramírez shares how open source, developer experience, and a dash of humor are shaping the future of APIs. // Bio Sebastián Ramírez (also known as Tiangolo) is the creator of FastAPI, Typer, SQLModel, Asyncer, and several other widely used open source tools.He has collaborated with companies and teams around the world—from Latin America to the Middle East, Europe, and the United States—building a range of products and custom solutions focused on APIs, data processing, distributed systems, and machine learning. Today, he works full time on FastAPI and its growing ecosystem. // Related Links Website: https://tiangolo.com/ FastAPI: https://fastapi.tiangolo.com/ FastAPI Cloud: https://fastapicloud.com/ FastAPI for Machine Learning // Sebastián Ramírez // MLOps Coffee Sessions #96 - https://youtu.be/NpvRhZnkEFg ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore Join our Slack community [https://go.mlops.community/slack] Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register] MLOps Swag/Merch: [https://shop.mlops.community/] Connect with Demetrios on LinkedIn: /dpbrinkm Connect with Tiangolo on LinkedIn: /tiangolo Timestamps: [00:00] Sebastián's preferred coffee [00:15] Takeaways [01:43] Why Pydantic is Awesome [06:47] ML Background and FastAPI [10:44] NASA FastAPI Emojis [15:21] FastAPI Cloud Journey [26:07] FastAPI Cloud Open-Source Balance [31:45] Basecamp Design Philosophy [35:30] AI Abstraction Strategies [42:56] Engineering vs Developer Experience [51:40] Dogfooding and Docs Strategy [59:44] Code Simplicity and Trust [1:04:26] Scaling Without Losing Vision [1:08:20] FastAPI Cloud Signup [1:09:23] Wrap up…
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MLOps.community
1 Everything Hard About Building AI Agents Today 47:02
47:02 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי47:02
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיWillem Pienaar and Shreya Shankar discuss the challenge of evaluating agents in production where "ground truth" is ambiguous and subjective user feedback isn't enough to improve performance. The discussion breaks down the three "gulfs" of human-AI interaction—Specification, Generalization, and Comprehension—and their impact on agent success. Willem and Shreya cover the necessity of moving the human "out of the loop" for feedback, creating faster learning cycles through implicit signals rather than direct, manual review.The conversation details practical evaluation techniques, including analyzing task failures with heat maps and the trade-offs of using simulated environments for testing. Willem and Shreya address the reality of a "performance ceiling" for AI and the importance of categorizing problems your agent can, can learn to, or will likely never be able to solve. // Bio Shreya Shankar PhD student in data management for machine learning. Willem Pienaar Willem Pienaar, CTO of Cleric, is a builder with a focus on LLM agents, MLOps, and open source tooling. He is the creator of Feast, an open source feature store, and contributed to the creation of both the feature store and MLOps categories. Before starting Cleric, Willem led the open source engineering team at Tecton and established the ML platform team at Gojek, where he built high scale ML systems for the Southeast Asian decacorn. // Related Links https://www.google.com/about/careers/applications/?utm_campaign=profilepage&utm_medium=profilepage&utm_source=linkedin&src=Online/LinkedIn/linkedin_pagehttps://cleric.ai/ ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore MLOps Swag/Merch: [https://shop.mlops.community/] Connect with Demetrios on LinkedIn: /dpbrinkm Connect with Shreya on LinkedIn: /shrshnk Connect with Willem on LinkedIn: /willempienaar Timestamps: [00:00] Trust Issues in AI Data [04:49] Cloud Clarity Meets Retrieval [09:37] Why Fast AI Is Hard [11:10] Fixing AI Communication Gaps [14:53] Smarter Feedback for Prompts [19:23] Creativity Through Data Exploration [23:46] Helping Engineers Solve Faster [26:03] The Three Gaps in AI [28:08] Alerts Without the Noise [33:22] Custom vs General AI [34:14] Sharpening Agent Skills [40:01] Catching Repeat Failures [43:38] Rise of Self-Healing Software [44:12] The Chaos of Monitoring AI…
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MLOps.community
1 Tricks to Fine Tuning // Prithviraj Ammanabrolu // #318 54:01
54:01 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי54:01
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיTricks to Fine Tuning // MLOps Podcast #318 with Prithviraj Ammanabrolu, Research Scientist at Databricks . Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract Prithviraj Ammanabrolu drops by to break down Tao fine-tuning—a clever way to train models without labeled data. Using reinforcement learning and synthetic data, Tao teaches models to evaluate and improve themselves. Raj explains how this works, where it shines (think small models punching above their weight), and why it could be a game-changer for efficient deployment. // Bio Raj is an Assistant Professor of Computer Science at the University of California, San Diego, leading the PEARLS Lab in the Department of Computer Science and Engineering (CSE). He is also a Research Scientist at Mosaic AI, Databricks, where his team is actively recruiting research scientists and engineers with expertise in reinforcement learning and distributed systems. Previously, he was part of the Mosaic team at the Allen Institute for AI. He earned his PhD in Computer Science from the School of Interactive Computing at Georgia Tech, advised by Professor Mark Riedl in the Entertainment Intelligence Lab. // Related Links Website: https://www.databricks.com/ ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore Join our Slack community [https://go.mlops.community/slack] Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register] MLOps Swag/Merch: [https://shop.mlops.community/] Connect with Demetrios on LinkedIn: /dpbrinkm Connect with Raj on LinkedIn: /rajammanabrolu Timestamps: [00:00] Raj's preferred coffee [00:36] Takeaways [01:02] Tao Naming Decision [04:19] No Labels Machine Learning [08:09] Tao and TAO breakdown [13:20] Reward Model Fine-Tuning [18:15] Training vs Inference Compute [22:32] Retraining and Model Drift [29:06] Prompt Tuning vs Fine-Tuning [34:32] Small Model Optimization Strategies [37:10] Small Model Potential [43:08] Fine-tuning Model Differences [46:02] Mistral Model Freedom [53:46] Wrap up…
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MLOps.community
1 Packaging MLOps Tech Neatly for Engineers and Non-engineers // Jukka Remes // #322 55:30
55:30 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי55:30
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיPackaging MLOps Tech Neatly for Engineers and Non-engineers // MLOps Podcast #322 with Jukka Remes, Senior Lecturer (SW dev & AI), AI Architect at Haaga-Helia UAS, Founder & CTO at 8wave AI. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract AI is already complex—adding the need for deep engineering expertise to use MLOps tools only makes it harder, especially for SMEs and research teams with limited resources. Yet, good MLOps is essential for managing experiments, sharing GPU compute, tracking models, and meeting AI regulations. While cloud providers offer MLOps tools, many organizations need flexible, open-source setups that work anywhere—from laptops to supercomputers. Shared setups can boost collaboration, productivity, and compute efficiency.In this session, Jukka introduces an open-source MLOps platform from Silo AI, now packaged for easy deployment across environments. With Git-based workflows and CI/CD automation, users can focus on building models while the platform handles the MLOps.// BioFounder & CTO, 8wave AI | Senior Lecturer, Haaga-Helia University of Applied SciencesJukka Remes has 28+ years of experience in software, machine learning, and infrastructure. Starting with SW dev in the late 1990s and analytics pipelines of fMRI research in early 2000s, he’s worked across deep learning (Nokia Technologies), GPU and cloud infrastructure (IBM), and AI consulting (Silo AI), where he also led MLOps platform development. Now a senior lecturer at Haaga-Helia, Jukka continues evolving that open-source MLOps platform with partners like the University of Helsinki. He leads R&D on GenAI and AI-enabled software, and is the founder of 8wave AI, which develops AI Business Operations software for next-gen AI enablement, including regulatory compliance of AI. // Related Links Open source -based MLOps k8s platform setup originally developed by Jukka's team at Silo AI - free for any use and installable in any environment from laptops to supercomputing: https://github.com/OSS-MLOPS-PLATFORM/oss-mlops-platform Jukka's new company:https://8wave.ai ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore Join our Slack community [https://go.mlops.community/slack] Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register] MLOps Swag/Merch: [https://shop.mlops.community/] Connect with Demetrios on LinkedIn: /dpbrinkm Connect with Jukka on LinkedIn: /jukka-remes Timestamps: [00:00] Jukka's preferred coffee [00:39] Open-Source Platform Benefits [01:56] Silo MLOps Platform Explanation [05:18] AI Model Production Processes [10:42] AI Platform Use Cases [16:54] Reproducibility in Research Models [26:51] Pipeline setup automation [33:26] MLOps Adoption Journey [38:31] EU AI Act and Open Source [41:38] MLOps and 8wave AI [45:46] Optimizing Cross-Stakeholder Collaboration [52:15] Open Source ML Platform [55:06] Wrap up…
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MLOps.community
1 Hard Learned Lessons from Over a Decade in AI 48:42
48:42 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי48:42
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיTecton Founder and CEO Mike Del Balso talks about what ML/AI use cases are core components generating Millions in revenue. Demetrios and Mike go through the maturity curve that predictive Machine Learning use cases have gone through over the past 5 years, and why a feature store is a primary component of an ML stack. // Bio Mike Del Balso is the CEO and co-founder of Tecton, where he’s building the industry’s first feature platform for real-time ML. Before Tecton, Mike co-created the Uber Michelangelo ML platform. He was also a product manager at Google where he managed the core ML systems that power Google’s Search Ads business. He studied Applied Science, Electrical & Computer Engineering at the University of Toronto. // Related Links Website: www.tecton.ai ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore MLOps Swag/Merch: [https://shop.mlops.community/] Connect with Demetrios on LinkedIn: /dpbrinkm Connect with Mike on LinkedIn: /michaeldelbalso Timestamps: [00:00] Smarter decisions, less manual work [03:52] Data pipelines: pain and fixes [08:45] Why Tecton was born [11:30] ML use cases shift [14:14] Models for big bets [18:39] Build or buy drama [20:20] Fintech's data playbook [23:52] What really needs real-time [28:07] Speeding up ML delivery [32:09] Valuing ML is tricky [35:29] Simplifying ML toolkits [37:18] AI copilots in action [42:13] AI that fights fraud [45:07] Teaming up across coasts [46:43] Tecton + Generative AI?…
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MLOps.community
1 Product Metrics are LLM Evals // Raza Habib CEO of Humanloop // #320 53:06
53:06 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי53:06
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיRaza Habib, the CEO of LLM Eval platform Humanloop , talks to us about how to make your AI products more accurate and reliable by shortening the feedback loop of your evals. Quickly iterating on prompts and testing what works, along with some of his favorite Dario from Anthropic AI Quotes. // Bio Raza is the CEO and Co-founder at Humanloop. He has a PhD in Machine Learning from UCL, was the founding engineer of Monolith AI, and has built speech systems at Google. For the last 4 years, he has led Humanloop and supported leading technology companies such as Duolingo, Vanta, and Gusto to build products with large language models. Raza was featured in the Forbes 30 Under 30 technology list in 2022, and Sifted recently named him one of the most influential Gen AI founders in Europe. // Related Links Websites: https://humanloop.com ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore MLOps Swag/Merch: [https://shop.mlops.community/] Connect with Demetrios on LinkedIn: /dpbrinkm Connect with Raza on LinkedIn: /humanloop-raza Timestamps: [00:00] Cracking Open System Failures and How We Fix Them [05:44] LLMs in the Wild — First Steps and Growing Pains [08:28] Building the Backbone of Tracing and Observability [13:02] Tuning the Dials for Peak Model Performance [13:51] From Growing Pains to Glowing Gains in AI Systems [17:26] Where Prompts Meet Psychology and Code [22:40] Why Data Experts Deserve a Seat at the Table [24:59] Humanloop and the Art of Configuration Taming [28:23] What Actually Matters in Customer-Facing AI [33:43] Starting Fresh with Private Models That Deliver [34:58] How LLM Agents Are Changing the Way We Talk [39:23] The Secret Lives of Prompts Inside Frameworks [42:58] Streaming Showdowns — Creativity vs. Convenience [46:26] Meet Our Auto-Tuning AI Prototype [49:25] Building the Blueprint for Smarter AI [51:24] Feedback Isn’t Optional — It’s Everything…
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MLOps.community
1 Getting AI Apps Past the Demo // Vaibhav Gupta // #319 50:29
50:29 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי50:29
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיGetting AI Apps Past the Demo // MLOps Podcast #319 with Vaibhav Gupta, CEO of BoundaryML. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract It's been two years, and we still seem to see AI disproportionately more in demos than production features. Why? And how can we apply engineering practices we've all learned in the past decades to our advantage here? // Bio Vaibhav is one of the creators of BAML and a YC alum. He spent 10 years in AI performance optimization at places like Google, Microsoft, and D.E. Shaw. He loves diving deep and chatting about anything related to Gen AI and Computer Vision! // Related Links Website: https://www.boundaryml.com/ ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore Join our Slack community [https://go.mlops.community/slack] Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register] MLOps Swag/Merch: [https://shop.mlops.community/] Connect with Demetrios on LinkedIn: /dpbrinkm Connect with Vaibhav on LinkedIn: /vaigup Timestamps: [00:00] Vaibhav's preferred coffee [00:38] What is BAML [03:07] LangChain Overengineering Issues [06:46] Verifiable English Explained [11:45] Python AI Integration Challenges [15:16] Strings as First-Class Code [21:45] Platform Gap in Development [30:06] Workflow Efficiency Tools [33:10] Surprising BAML Insights [40:43] BAML Cool Projects [45:54] BAML Developer Conversations [48:39] Wrap up…
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MLOps.community
1 Building Out GPU Clouds // Mohan Atreya // #317 47:57
47:57 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי47:57
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיDemetrios and Mohan Atreya break down the GPU madness behind AI — from supply headaches and sky-high prices to the rise of nimble GPU clouds trying to outsmart the giants. They cover power-hungry hardware, failed experiments, and how new cloud models are shaking things up with smarter provisioning, tokenized access, and a whole lotta hustle. It's a wild ride through the guts of AI infrastructure — fun, fast, and full of sparks! Big thanks to the folks at Rafay for backing this episode — appreciate the support in making these conversations happen! // BioMohan is a seasoned and innovative product leader currently serving as the Chief Product Officer at Rafay Systems. He has led multi-site teams and driven product strategy at companies like Okta, Neustar, and McAfee. // Related LinksWebsites: https://rafay.co/ ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore MLOps Swag/Merch: [https://shop.mlops.community/] Connect with Demetrios on LinkedIn: /dpbrinkm Connect with Mohan on LinkedIn: /mohanatreya Timestamps: [00:00] AI/ML Customer Challenges [04:21] Dependency on Microsoft for Revenue [09:08] Challenges of Hypothesis in AI/ML [12:17] Neo Cloud Onboarding Challenges [15:02] Elastic GPU Cloud Automation [19:11] Dynamic GPU Inventory Management [20:25] Terraform Lacks Inventory Awareness [26:42] Onboarding and End-User Experience Strategies [29:30] Optimizing Storage for Data Efficiency [33:38] Pizza Analogy: User Preferences [35:18] Token-Based GPU Cloud Monetization [39:01] Empowering Citizen Scientists with AI [42:31] Innovative CFO Chatbot Solutions [47:09] Cloud Services Need Spectrum…
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MLOps.community
1 A Candid Conversation Around MCP and A2A // Rahul Parundekar and Sam Partee // #316 SF Live 1:04:42
1:04:42 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי1:04:42
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיDemetrios, Sam Partee, and Rahul Parundekar unpack the chaos of AI agent tools and the evolving world of MCP (Model Context Protocol). With sharp insights and plenty of laughs, they dig into tool permissions, security quirks, agent memory, and the messy path to making agents actually useful. // Bio Sam Partee Sam Partee is the CTO and Co-Founder of Arcade AI. Previously a Principal Engineer leading the Applied AI team at Redis, Sam led the effort in creating the ecosystem around Redis as a vector database. He is a contributor to multiple OSS projects including Langchain, DeterminedAI, LlamaIndex and Chapel amongst others. While at Cray/HPE he created the SmartSim AI framework which is now used at national labs around the country to integrate HPC simulations like climate models with AI. Rahul Parundekar Rahul Parundekar is the founder of AI Hero. He graduated with a Master's in Computer Science from USC Los Angeles in 2010, and embarked on a career focused on Artificial Intelligence. From 2010-2017, he worked as a Senior Researcher at Toyota ITC working on agent autonomy within vehicles. His journey continued as the Director of Data Science at FigureEight (later acquired by Appen), where he and his team developed an architecture supporting over 36 ML models and managing over a million predictions daily. Since 2021, he has been working on AI Hero, aiming to democratize AI access, while also consulting on LLMOps(Large Language Model Operations), and AI system scalability. Other than his full time role as a founder, he is also passionate about community engagement, and actively organizes MLOps events in SF, and contributes educational content on RAG and LLMOps at learn.mlops.community. // Related Links Websites: arcade.dev aihero.studio~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore MLOps Swag/Merch: [https://shop.mlops.community/] Connect with Demetrios on LinkedIn: /dpbrinkm Connect with Rahul on LinkedIn: /rparundekar Connect with Sam on LinkedIn: /samparteeTimestamps:[00:00] Agents & Tools, Explained (Without Melting Your Brain) [09:51] MVP Servers: Why Everything’s on Fire (and How to Fix It) [13:18] Can We Actually Trust the Protocol? [18:13] KYC, But Make It AI (and Less Painful) [25:25] Web Automation Tests: The Bugs Strike Back [28:18] MCP Dev: What Went Wrong (and What Saved Us) [33:53] Social Login: One Button to Rule Them All [39:33] What Even Is an AI-Native Developer? [42:21] Betting Big on Smarter Models (High Risk, High Reward) [51:40] Harrison’s Bold New Tactic (With Real-Life Magic Tricks) [55:31] Async Task Handoffs: Herding Cats, But Digitally [1:00:37] Getting AI to Actually Help Your Workflow [1:03:53] The Infamous Varma System Error (And How We Dodge It)…
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MLOps.community
1 AI in M&A: Building, Buying, and the Future of Dealmaking // Kison Patel // #315 55:32
55:32 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי55:32
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיAI in M&A: Building, Buying, and the Future of Dealmaking // MLOps Podcast #315 with Kison Patel, CEO and M&A Science at DealRoom . Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // AbstractThe intersection of M&A and AI, exploring how the DealRoom team developed AI capabilities and the practical use cases of AI in dealmaking. Discuss the evolving landscape of AI-driven M&A, the factors that make AI companies attractive acquisition targets, and the key indicators of success in this space. // Bio Kison Patel is the Founder and CEO of DealRoom, an M&A lifecycle management platform designed for buyer-led M&A and recognized twice on the Inc. 5000 Fastest Growing Companies list. He also founded M&A Science, a global community offering courses, events, and the top-rated M&A Science podcast with over 2.25 million downloads. Through the podcast, Kison shares actionable insights from top M&A experts, helping professionals modernize their approach to deal-making. He is also the author of *Agile M&A: Proven Techniques to Close Deals Faster and Maximize Value*, a guide to tech-enabled, adaptive M&A practices. Kison is dedicated to disrupting traditional M&A with innovative tools and education, empowering teams to drive greater efficiency and value. // Related LinksWebsite: https://dealroom.nethttps://www.mascience.com ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore Join our Slack community [https://go.mlops.community/slack] Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register] MLOps Swag/Merch: [https://shop.mlops.community/] Connect with Demetrios on LinkedIn: /dpbrinkm Connect with Kison on LinkedIn: /kisonpatel…
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MLOps.community
1 AI, Marketing, and Human Decision Making // Fausto Albers // #313 49:40
49:40 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי49:40
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיAI, Marketing, and Human Decision Making // MLOps Podcast #313 with Fausto Albers, AI Engineer & Community Lead at AI Builders Club. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract Demetrios and Fausto Albers explore how generative AI transforms creative work, decision-making, and human connection, highlighting both the promise of automation and the risks of losing critical thinking and social nuance. // Bio Fausto Albers is a relentless explorer of the unconventional—a techno-optimist with a foundation in sociology and behavioral economics, always connecting seemingly absurd ideas that, upon closer inspection, turn out to be the missing pieces of a bigger puzzle. He thrives in paradox: he overcomplicates the simple, oversimplifies the complex, and yet somehow lands on solutions that feel inevitable in hindsight. He believes that true innovation exists in the tension between chaos and structure—too much of either, and you’re stuck. His career has been anything but linear. He’s owned and operated successful restaurants, served high-stakes cocktails while juggling bottles on London’s bar tops, and later traded spirits for code—designing digital waiters, recommender systems, and AI-driven accounting tools. Now, he leads the AI Builders Club Amsterdam, a fast-growing community where AI engineers, researchers, and founders push the boundaries of intelligent systems. Ask him about RAG, and he’ll insist on specificity—because, as he puts it, discussing retrieval-augmented generation without clear definitions is as useful as declaring that “AI will have an impact on the world.” An engaging communicator, a sharp systems thinker, and a builder of both technology and communities, Fausto is here to challenge perspectives, deconstruct assumptions, and remix the future of AI. // Related Links Website: aibuilders.club Moravec's paradox: https://en.wikipedia.org/wiki/Moravec%27s_paradox?utm_source=chatgpt.com Behavior Modeling, Secondary AI Effects, Bias Reduction & Synthetic Data // Devansh Devansh // #311: https://youtu.be/jJXee5rMtHI ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore Join our Slack community [https://go.mlops.community/slack] Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register] MLOps Swag/Merch: [https://shop.mlops.community/] Connect with Demetrios on LinkedIn: /dpbrinkm Connect with Fausto on LinkedIn: /stepintoliquid Timestamps:[00:00] Fausto's preferred coffee[00:26] Takeaways[01:18] Automated Ad Creative Generation[07:14] AI in Marketing Workflows[13:23] MCP and System Bottlenecks[21:45] Forward Compatibility vs Optimization[29:57] Unlocking Workflow Speed[33:48] AI Dependency vs Critical Thinking[37:44] AI Realism and Paradoxes[42:30] Outsourcing Decision-Making Risks[46:22] Human Value in Automation[49:02] Wrap up…
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MLOps.community
1 MLOps with Databricks // Maria Vechtomova // #314 52:43
52:43 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי52:43
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיMLOps with Databricks // MLOps Podcast #314 with Maria Vechtomova, MLOps Tech Lead | Founder at Ahold Delhaize | Marvelous MLOps. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract The world of MLOps is very complex as there is an endless amount of tools serving its purpose, and it is very hard to get your head around it. Instead of combining various tools and managing them, it may make sense to opt for a platform instead. Databricks is a leading platform for MLOps. In this discussion, I will explain why it is the case, and walk you through Databricks MLOps features. // Bio Maria is an MLOps Tech lead with over 10 years of experience in Data and AI. For the last 8 years, Maria has focused on MLOps and helped to establish MLOps best practices at large corporations. Together with her colleague, she co-founded Marvelous MLOps to share knowledge on MLOps via training, social media posts, and blogs. // Related Links Website: marvelousmlops.io MLOps Course discount code: MLOPS100 for the podcast listeners - https://maven.com/marvelousmlops/mlops-with-databricks?promoCode=MLOPS100 ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore Join our slack community [https://go.mlops.community/slack] Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register] MLOps Swag/Merch: [https://shop.mlops.community/] Connect with Demetrios on LinkedIn: /dpbrinkm Connect with Maria on LinkedIn: /maria-vechtomovaTimestamps: [00:00] Maria's preferred coffee[00:42] Takeaways[02:48] Why Databricks for MLOps[09:56] Platform Adoption vs Procurement Pain[12:56] Databricks Best Practices[16:57] Feature Store Overview[22:00] Managed system trade-offs[29:15] Databricks Developments and Trends[44:31] Insider Info and Summit[45:47] Data Ownership Pros and Cons[48:08] Data Contracts and Challenges[51:25] MLOps Databricks Book Guide[52:19] Wrap up…
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1 Making AI Reliable is the Greatest Challenge of the 2020s // Alon Bochman // #312 1:01:37
1:01:37 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי1:01:37
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיMaking AI Reliable is the Greatest Challenge of the 2020s // MLOps Podcast #312 with Alon Bochman, CEO of RagMetrics. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter Huge shout-out to @RagMetrics for sponsoring this episode! // Abstract Demetrios talks with Alon Bochman, CEO of RagMetrics, about testing in machine learning systems. Alon stresses the value of empirical evaluation over influencer advice, highlights the need for evolving benchmarks, and shares how to effectively involve subject matter experts without technical barriers. They also discuss using LLMs as judges and measuring their alignment with human evaluators. // Bio Alon is a product leader with a fintech and adtech background, ex-Google, ex-Microsoft. Co-founded and sold a software company to Thomson Reuters for $30M, grew an AI consulting practice from 0 to over $ 1 Bn in 4 years. 20-year AI veteran, winner of three medals in model-building competitions. In a prior life, he was a top-performing hedge fund portfolio manager.Alon lives near NYC with his wife and two daughters. He is an avid reader, runner, and tennis player, an amateur piano player, and a retired chess player. // Related Links Website: ragmetrics.ai ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore Join our slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register] MLOps Swag/Merch: [https://shop.mlops.community/] Connect with Demetrios on LinkedIn: /dpbrinkm Connect with Alon on LinkedIn: /alonbochman Timestamps: [00:00] Alon's preferred coffee[00:15] Takeaways[00:47] Testing Multi-Agent Systems[05:55] Tracking ML Experiments[12:28] AI Eval Redundancy Balance[17:07] Handcrafted vs LLM Eval Tradeoffs[28:15] LLM Judging Mechanisms[36:03] AI and Human Judgment[38:55] Document Evaluation with LLM[42:08] Subject Matter Expertise in Co-Pilots[46:33] LLMs as Judges[51:40] LLM Evaluation Best Practices[55:26] LM Judge Evaluation Criteria[58:15] Visualizing AI Outputs[1:01:16] Wrap up…
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