In this episode, we uncover the grim details of the Bag Murders, the systemic failures that allowed the killer to evade justice for years.
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محتوای ارائه شده توسط Demetrios. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط Demetrios یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal
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Building Better Data Teams // Leanne Fitzpatrick // Coffee Sessions #113
Manage episode 336873505 series 3241972
محتوای ارائه شده توسط Demetrios. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط Demetrios یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal
MLOps Coffee Sessions #113 with Leanne Fitzpatrick, Director of Data Science of Financial Times, Building Better Data Teams co-hosted by Mihail Eric.
// Abstract
We spent a lot of time talking about data tooling but we maybe spent not as much time talking about data organizations and efficiently running and organizing data teams.
What about starting with limitations instead of aspirations? Right constraints instead of the north star? In this session, let's learn more about a realistic take on the state of data organizations of today.
// Bio
Leanne is Director of Data Science at the Financial Times and is a passionate data leader with experience building and developing empowered data science and analytics teams in a variety of businesses. Leanne is in her element when developing and implementing strategic, technical, and cultural solutions to getting machine learning and data science into the operational ecosystem.
Leanne is an active part of the data and technology community, sharing innovation and insights to encourage best practices, from Manchester, UK to Austin, TX, and is an Advisory Panel Board Member. Outside of all things data you can ask Leanne about her golf swing (it’s not good - yet), her passion for American Football (specifically the Cincinnati Bengals), her latest sewing project, and her love for good music, food, and whisky.
// MLOps Jobs board
https://mlops.pallet.xyz/jobs
MLOps Swag/Merch
https://mlops-community.myshopify.com/
// Related
Links Children of Time book by Adrian Tchaikovsky:
https://www.amazon.com/Children-Time-Adrian-Tchaikovsky/dp/0316452505
--------------- ✌️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 Mihail on LinkedIn: https://www.linkedin.com/in/mihaileric/
Connect with Leanne on LinkedIn: https://www.linkedin.com/in/leanne-kim-fitzpatrick-29204341/
Timestamps:
[00:00] Introduction to Leanne Fitzpatrick
[04:23] Write us your suggestions!
[05:43] Tri-pawed dog called Seaweed!
[08:43] How to architect data teams
[14:44] Organizational deficiencies
[19:19] Tensions and conflicts for starters
[24:07] Misunderstandings from marketing
[25:59] The Middle Layer
[28:48] Data science work at publications
[31:11] Mystique of going to real-time
[35:29] Third parties with fraud
[37:40] Augmenting data practitioners with third-party tools
[41:00] Principle of reinventing the wheel and avoiding undifferentiated heavy lifting
[46:29] Different Abstraction Layer recommendations
[48:42] RN Production
[51:56] Will Python eats RN Production away?
[56:05] Julia as a dark horse
[56:39] Future of RN Production
[58:00] Rapid fire questions
443 قسمت
اشتراک گذاریManage episode 336873505 series 3241972
محتوای ارائه شده توسط Demetrios. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط Demetrios یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal
MLOps Coffee Sessions #113 with Leanne Fitzpatrick, Director of Data Science of Financial Times, Building Better Data Teams co-hosted by Mihail Eric.
// Abstract
We spent a lot of time talking about data tooling but we maybe spent not as much time talking about data organizations and efficiently running and organizing data teams.
What about starting with limitations instead of aspirations? Right constraints instead of the north star? In this session, let's learn more about a realistic take on the state of data organizations of today.
// Bio
Leanne is Director of Data Science at the Financial Times and is a passionate data leader with experience building and developing empowered data science and analytics teams in a variety of businesses. Leanne is in her element when developing and implementing strategic, technical, and cultural solutions to getting machine learning and data science into the operational ecosystem.
Leanne is an active part of the data and technology community, sharing innovation and insights to encourage best practices, from Manchester, UK to Austin, TX, and is an Advisory Panel Board Member. Outside of all things data you can ask Leanne about her golf swing (it’s not good - yet), her passion for American Football (specifically the Cincinnati Bengals), her latest sewing project, and her love for good music, food, and whisky.
// MLOps Jobs board
https://mlops.pallet.xyz/jobs
MLOps Swag/Merch
https://mlops-community.myshopify.com/
// Related
Links Children of Time book by Adrian Tchaikovsky:
https://www.amazon.com/Children-Time-Adrian-Tchaikovsky/dp/0316452505
--------------- ✌️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 Mihail on LinkedIn: https://www.linkedin.com/in/mihaileric/
Connect with Leanne on LinkedIn: https://www.linkedin.com/in/leanne-kim-fitzpatrick-29204341/
Timestamps:
[00:00] Introduction to Leanne Fitzpatrick
[04:23] Write us your suggestions!
[05:43] Tri-pawed dog called Seaweed!
[08:43] How to architect data teams
[14:44] Organizational deficiencies
[19:19] Tensions and conflicts for starters
[24:07] Misunderstandings from marketing
[25:59] The Middle Layer
[28:48] Data science work at publications
[31:11] Mystique of going to real-time
[35:29] Third parties with fraud
[37:40] Augmenting data practitioners with third-party tools
[41:00] Principle of reinventing the wheel and avoiding undifferentiated heavy lifting
[46:29] Different Abstraction Layer recommendations
[48:42] RN Production
[51:56] Will Python eats RN Production away?
[56:05] Julia as a dark horse
[56:39] Future of RN Production
[58:00] Rapid fire questions
443 قسمت
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Willem 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…
Tricks 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
Packaging 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…
Tecton 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?…
Raza 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…
Getting 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…
Demetrios 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 
پخش در آینده 
پخش در آینده 
لیست ها 
پسندیدن 
دوست داشته شد1:04:42
پخش در آینده پخش در آینده لیست ها پسندیدن دوست داشته شدDemetrios, 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
AI 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…
AI, 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…
MLOps 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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MLOps.community
1 Making AI Reliable is the Greatest Challenge of the 2020s // Alon Bochman // #312 1:01:37
1:01:37 پخش در آینده پخش در آینده لیست ها پسندیدن دوست داشته شد1:01:37
پخش در آینده پخش در آینده لیست ها پسندیدن دوست داشته شدMaking 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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MLOps.community
1 Behavior Modeling, Secondary AI Effects, Bias Reduction & Synthetic Data // Devansh Devansh // #311 1:01:35
1:01:35 پخش در آینده پخش در آینده لیست ها پسندیدن دوست داشته شد1:01:35
پخش در آینده پخش در آینده لیست ها پسندیدن دوست داشته شدBehavior Modeling, Secondary AI Effects, Bias Reduction & Synthetic Data // MLOps Podcast #311 with Devansh Devansh, Head of AI at Stealth AI Startup. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // AbstractOpen-source AI researcher Devansh Devansh joins Demetrios to discuss grounded AI research, jailbreaking risks, Nvidia’s Gretel AI acquisition, and the role of synthetic data in reducing bias. They explore why deterministic systems may outperform autonomous agents and urge listeners to challenge power structures and rethink how intelligence is built into data infrastructure. // BioThe best meme-maker in Tech. Writer on AI, Software, and the Tech Industry. // Related Links Subscribe to Artificial Intelligence Made Simple: https://artificialintelligencemadesimple.substack.com/https://www.linkedin.com/pulse/alternative-ways-build-ai-models-taoist-devansh-devansh-z9iff/?trackingId=TKvUBldml6rOQUjqam%2B7lA%3D%3D ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin 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: /dpbrinkmConnect with Devansh on LinkedIn: /devansh-devansh-516004168 Timestamps:[00:00] Devansh's preferred coffee[01:23] Jailbreaking DeepSeek[02:24] AI Made Simple [07:16] Leveraging AI for Data Insights[10:42] Synthetic Data and LLMs[19:29] AI Experience Design[22:20] Synthetic Data Bias Reduction[26:33] Data Ecosystem Insights[29:50] Moving Intelligence to Data Layer[36:37] Minimizing Model Responsibility[40:04] Workflow vs Generalized Agents[49:24] AI Second-Order Effects[55:26] AI Experience vs Efficiency[1:01:10] Wrap up…
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MLOps.community
1 GraphBI: Expanding Analytics to All Data Through the Combination of GenAI, Graph, & Visual Analytics // Paco Nathan & Weidong Yang // #310 1:14:01
1:14:01 پخش در آینده پخش در آینده لیست ها پسندیدن دوست داشته شد1:14:01
پخش در آینده پخش در آینده لیست ها پسندیدن دوست داشته شدGraphBI: Expanding Analytics to All Data Through the Combination of GenAI, Graph, & Visual Analytics // MLOps Podcast #310 with Paco Nathan, Principal DevRel Engineer at Senzing & Weidong Yang, CEO of Kineviz. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // AbstractExisting BI and big data solutions depend largely on structured data, which makes up only about 20% of all available information, leaving the vast majority untapped. In this talk, we introduce GraphBI, which aims to address this challenge by combining GenAI, graph technology, and visual analytics to unlock the full potential of enterprise data. Recent technologies like RAG (Retrieval-Augmented Generation) and GraphRAG leverage GenAI for tasks such as summarization and Q&A, but they often function as black boxes, making verification challenging. In contrast, GraphBI uses GenAI for data pre-processing—converting unstructured data into a graph-based format—enabling a transparent, step-by-step analytics process that ensures reliability. We will walk through the GraphBI workflow, exploring best practices and challenges in each step of the process: managing both structured and unstructured data, data pre-processing with GenAI, iterative analytics using a BI-focused graph grammar, and final insight presentation. This approach uniquely surfaces business insights by effectively incorporating all types of data. // BioPaco NathanPaco Nathan is a "player/coach" who excels in data science, machine learning, and natural language, with 40 years of industry experience. He leads DevRel for the Entity Resolved Knowledge Graph practice area at Senzing.com and advises Argilla.io, Kurve.ai, KungFu.ai, and DataSpartan.co.uk, and is lead committer for the pytextrank and kglab open source projects. Formerly: Director of Learning Group at O'Reilly Media; and Director of Community Evangelism at Databricks. Weidong YangWeidong Yang, Ph.D., is the founder and CEO of Kineviz, a San Francisco-based company that develops interactive visual analytics based solutions to address complex big data problems. His expertise spans Physics, Computer Science and Performing Art, with significant contributions to the semiconductor industry and quantum dot research at UC, Berkeley and Silicon Valley. Yang also leads Kinetech Arts, a 501(c) non-profit blending dance, science, and technology. An eloquent public speaker and performer, he holds 11 US patents, including the groundbreaking Diffraction-based Overlay technology, vital for sub-10-nm semiconductor production. // Related LinksWebsite: https://www.kineviz.com/Blog: https://medium.com/kinevizWebsite: https://derwen.ai/pacohttps://huggingface.co/pacoidhttps://github.com/ceterihttps://neo4j.com/developer-blog/entity-resolved-knowledge-graphs/ ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin 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: /dpbrinkmConnect with Weidong on LinkedIn: /yangweidong/Connect with Paco on LinkedIn: /ceteri/…
AI Data Engineers - Data Engineering after AI // MLOps Podcast #309 with Vikram Chennai, Founder/CEO of Ardent AI. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // AbstractA discussion of Agentic approaches to Data Engineering. Exploring the benefits and pitfalls of AI solutions and how to design product-grade AI agents, especially in data. // BioSecond Time Founder. 5 years building Deep learning models. Currently, AI Data Engineers // Related LinksWebsite: tryardent.com ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin 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: /dpbrinkmConnect with Vikram on LinkedIn: /vikram-chennai/…
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