Who's MLOps for Anyway? // Jonathan Rioux // #261

Stefano Bosisio is an accomplished MLOps Engineer with a solid background in Biomedical Engineering, focusing on cellular biology, genetics, and molecular simulations. Reinvent Yourself and Be Curious // MLOps Podcast #264 with Stefano Bosisio, MLOps Engineer at Synthesia. // Abstract This talk goes through Stefano's experience, to be an inspirational source for whoever wants to jump on a career in the MLOps sector. Moreover, Stefano will also introduce his MLOps Course on the MLOps community platform. // Bio Sai Bharath Gottam Stefano Bosisio is an MLOps Engineer, with a versatile background that ranges from biomedical engineering to computational chemistry and data science. Stefano got an MSc in biomedical engineering from the Polytechnic of Milan, focusing on cellular biology, genetics, and molecular simulations. Then, he landed in Scotland, in Edinburgh, to earn a PhD in chemistry from the University of Edinburgh, where he developed robust physical theories and simulation methods, to understand and unlock the drug discovery problem. After completing his PhD, Stefano transitioned into Data Science, where he began his career as a data scientist. His interest in machine learning engineering grew, leading him to specialize in building ML platforms that drive business success. Stefano's expertise bridges the gap between complex scientific research and practical machine learning applications, making him a key figure in the MLOps field. Bonus points beyond data: Stefano, as a proper Italian, loves cooking and (mainly) baking, playing the piano, crocheting and running half-marathons. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://medium.com/@stefanobosisio1First MLOps Stack Course: https://learn.mlops.community/courses/languages/your-first-mlops-stack/ --------------- ✌️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 Stefano on LinkedIn: https://www.linkedin.com/in/stefano-bosisio1/ Timestamps: [00:00] Stephano's preferred coffee [00:12] Takeaways [01:06] Stephano's MLOps Course [01:47] From Academia to AI Industry [09:10] Data science and platforms [16:53] Persistent MLOps challenges [21:23] Internal evangelization for success [24:21] Adapt communication skills to diverse individual needs [29:43] Key components of ML pipelines are essentia l[33:47] Create a generalizable AI training pipeline with Kubeflow [35:44] Consider cost-effective algorithms and deployment methods [39:02] Agree with dream platform; LLMs require simple microservice [42:48] Auto scaling: crucial, tricky, prone to issues [46:28] Auto-scaling issues with Apache Beam data pipelines [49:49] Guiding students through MLOps with practical experience [53:16] Bulletproof Problem Solving: Decision trees for problem analysis [55:03] Evaluate tools critically; appreciate educational opportunities [57:01] Wrap up

Global Feature Store: Optimizing Locally and Scaling Globally at Delivery Hero // MLOps Podcast #263 with Delivery Hero's Gottam Sai Bharath, Senior Machine Learning Engineer & Cole Bailey, ML Platform Engineering Manager. // Abstract Delivery Hero innovates locally within each department to develop MLOps practices most effective in that particular context. We also discuss our efforts to reduce redundancy and inefficiency across the company. Hear about our experiences in creating multiple micro feature stores within our departments, and our goal to unify these into a Global Feature Store that is more powerful when combined. // Bio Sai Bharath Gottam With a passion for translating complex technical concepts into practical solutions, Sai excels at making intricate topics accessible and engaging. As a Senior Machine Learning Engineer at Delivery Hero, Sai works on cutting-edge machine learning platforms that guarantee seamless delivery experiences. Always eager to share insights and innovations, Sai is committed to making technology understandable and enjoyable for all. Cole Bailey Bridging data science and production-grade software engineering. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://www.deliveryhero.com/ --------------- ✌️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 Sai on LinkedIn: https://www.linkedin.com/in/sai-bharath-gottam/ Connect with Cole on LinkedIn: www.linkedin.com/in/cole-bailey Timestamps: [00:00] Sai and Cole's preferred coffee [00:42] Takeaways [01:51] Please like, share, leave a review, and subscribe to our MLOps channels! [02:08] Life changes in Delivery Hero [05:21] Global Feature Store and Pandora [12:21] Tech integration strategies [20:08] Defining Feature and Feature Store [22:46] Feature Store vs Data Platform [26:26] Features are discoverable [32:56] Onboarding and Feature Testing [36:00] Data consistency [41:07] Future Vision Feature Store [44:17] Multi-cloud strategies [46:33] Wrap up

Adam Kamor is the Co-founder of Tonic, a company that specializes in creating mock data that preserves secure datasets. RAG Quality Starts with Data Quality // MLOps Podcast #262 with Adam Kamor, Co-Founder & Head of Engineering of Tonic.ai. // Abstract Dive into what makes Retrieval-Augmented Generation (RAG) systems tick—and it all starts with the data. We’ll be talking with an expert in the field who knows exactly how to transform messy, unstructured enterprise data into high-quality fuel for RAG systems. Expect to learn the essentials of data prep, uncover the common challenges that can derail even the best-laid plans, and discover some insider tips on how to boost your RAG system’s performance. We’ll also touch on the critical aspects of data privacy and governance, ensuring your data stays secure while maximizing its utility. If you’re aiming to get the most out of your RAG systems or just curious about the behind-the-scenes work that makes them effective, this episode is packed with insights that can help you level up your game. // Bio Adam Kamor, PhD, is the Co-founder and Head of Engineering of Tonic.ai. Since completing his PhD in Physics at Georgia Tech, Adam has committed himself to enabling the work of others through the programs he develops. In his roles at Microsoft and Kabbage, he handled UI design and led the development of new features to anticipate customer needs. At Tableau, he played a role in developing the platform’s analytics/calculation capabilities. As a founder of Tonic.ai, he is leading the development of unstructured data solutions that are transforming the work of fellow developers, analysts, and data engineers alike. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://www.tonic.ai Various topics about RAG and LLM security are available on Tonic.ai's blogs: https://www.tonic.ai/blog https://www.tonic.ai/blog/how-to-prevent-data-leakage-in-your-ai-applications-with-tonic-textual-and-snowpark-container-services https://www.tonic.ai/blog/rag-evaluation-series-validating-the-rag-performance-of-the-openais-rag-assistant-vs-googles-vertex-search-and-conversation https://www.youtube.com/watch?v=5xdyt4oRONU https://www.tonic.ai/blog/what-is-retrieval-augmented-generation-the-benefits-of-implementing-rag-in-using-llms --------------- ✌️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 Adam on LinkedIn: https://www.linkedin.com/in/adam-kamor-85720b48/ Timestamps: [00:00] Adam's preferred coffee [00:24] Takeaways [00:59] Huge shout out to Tonic.ai for supporting the community! [01:03] Please like, share, leave a review, and subscribe to our MLOps channels! [01:18] Naming a product [03:38] Tonic Textual [08:00] Managing PII and Data Safety [10:16] Chunking strategies for context [14:19] Data prep for RAG [17:20] Data quality in AI systems [20:58] Data integrity in PDFs [27:12] Ensuring chatbot data freshness [33:02] Managed PostgreSQL and Vector DB [34:49] RBAC database vs file access [37:35] Slack AI data leakage solutions [42:26] Hot swapping [46:06] LLM security concerns [47:03] Privacy management best practices [49:02] Chatbot design patterns [50:39] RAG growth and impact [52:40] Retrieval Evaluation best practices [59:20] Wrap up

Jonathan Rioux is a Managing Principal of AI Consulting for EPAM Systems, where he advises clients on how to get from idea to realized AI products with the minimum of fuss and friction. Who's MLOps for Anyway? // MLOps Podcast #261 with Jonathan Rioux, Managing Principal, AI Consulting at EPAM Systems. // Abstract The year is 2024 and we are all staring into the cliff towards the abyss of disillusionment for Generative AI. Every organization, developer, and AI-adjacent individual is now talking about "making AI real" and "turning a ROI on AI initiatives". MLOps and LLMOps are taking the stage as the solution; equip your AI teams with the best tools money can buy, grab tokens by the fistful, and look at value raking in. Sounds familiar and eerily similar to the previous ML hype cycles? From solo devs to large organizations, how can we avoid the same pitfalls as last time and get out of the endless hamster wheel? // Bio Jonathan is a Managing Principal of AI Consulting for EPAM, where he advises client on how to get from idea to realized AI products with the minimum of fuss and friction. He's obsessed with the mental models of ML and how to organize harmonious AI practices. Jonathan published "Data Analysis with Python and PySpark" (Manning, 2022). // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: raiks.ca --------------- ✌️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 Jonathan on LinkedIn: https://www.linkedin.com/in/jonathanrx/ Timestamps: [00:00] Jonathan's preferred coffee [00:25] Takeaways [01:44] MLOps as not being sexy [03:49] Do not conflate MLOps with ROI [06:21] ML Certification Business Idea [11:02] AI Adoption Missteps [15:40] Slack AI Privacy Risks [18:17] Decentralized AI success [22:00] Michelangelo Hub-Spoke Model [27:45] Engineering tools for everyone [33:38 - 35:20] SAS Ad [35:21] POC to ROI transition [42:08] Repurposing project learnings [46:24] Balancing Innovation and ROI [55:35] Using classification model [1:00:24] Chatbot evolution comparison [1:01:20] Balancing Automation and Trust [1:06:30] Manual to AI transition [1:09:57] Wrap up

Shiva Bhattacharjee is the Co-founder and CTO of TrueLaw, where we are building bespoke models for law firms for a wide variety of tasks. Alignment is Real // MLOps Podcast #260 with Shiva Bhattacharjee, CTO of TrueLaw Inc. // Abstract If the off-the-shelf model can understand and solve a domain-specific task well enough, either your task isn't that nuanced or you have achieved AGI. We discuss when is fine-tuning necessary over prompting and how we have created a loop of sampling - collecting feedback - fine-tuning to create models that seem to perform exceedingly well in domain-specific tasks. // Bio 20 years of experience in distributed and data-intensive systems spanning work at Apple, Arista Networks, Databricks, and Confluent. Currently CTO at TrueLaw where we provide a framework to fold in user feedback, such as lawyer critiques of a given task, and fold them into proprietary LLM models through fine-tuning mechanics, resulting in 7-10x improvements over the base model. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: www.truelaw.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 Shiva on LinkedIn: https://www.linkedin.com/in/shivabhattacharjee/ Timestamps: [00:00] Shiva's preferred coffee [00:58] Takeaways [01:17] DSPy Implementation [04:57] Evaluating DSPy risks [08:13] Community-driven DSPy tool [12:19] RAG implementation strategies [17:02] Cost-effective embedding fine-tuning [18:51] AI infrastructure decision-making [24:13] Prompt data flow evolution [26:32] Buy vs build decision [30:45] Tech stack insights [38:20] Wrap up

Vikram Rangnekar is an open-source software developer focused on simplifying LLM integration. He created LLMClient, a TypeScript library inspired by Stanford's DSP paper. With years of experience building complex LLM workflows, he previously worked as a senior software engineer at LinkedIn on Ad Serving. Ax a New Way to Build Complex Workflows with LLMs // MLOps Podcast #259 with Vikram Rangnekar, Software Engineer at Stealth. // Abstract Ax is a new way to build complex workflows with LLMs. It's a typescript library based on research done in the Stanford DSP paper. Concepts such as prompt signatures, prompt tuning, and composable prompts help you build RAG and agent-powered ideas that have till now been hard to build and maintain. Ax is designed for production usage. // Bio Vikram builds open-source software. Currently working on making it easy to build with LLMs. Created Ax a typescript library that abstracts over all the complexity of LLMs, it is based on the research done in the Stanford DSP paper. Worked extensively with LLMs over the last few years to build complex workflows. Previously worked as a senior software engineer with LinkedIn on Ad Serving. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links The unofficial DSPy framework. Build LLM-powered Agents and "Agentic workflows" based on the Stanford DSP paper: https://axllm.dev All the Hard Stuff with LLMs in Product Development // Phillip Carter // MLOps Podcast #170: https://youtu.be/DZgXln3v85s --------------- ✌️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 Vikram on LinkedIn: https://www.linkedin.com/in/vikramr Timestamps: [00:00] Vikram preferred coffee [00:41] Takeaways [01:05] Data Engineering for AI/ML Conference Ad [01:41] Vikram's work these days [04:54] Fine-tuned Model insights [06:22] Java Script tool evolution [16:14] DSP knowledge distillation [17:34] DSP vs Manual examples [22:53] Optimizing task context [27:58] API type validation explained [30:25] LLM value and innovation [34:22] Navigating complex systems [37:30] DSP code generators explained [40:56] Exploring LLM personas [42:45] Optimizing small agents [43:32] Complex task assistance [49:53] Wrap up

MLOps Coffee Sessions #177 with Mohamed Abusaid and Mara Pometti, Building in Production Human-centred GenAI Solutions sponsored by QuantumBlack, AI by McKinsey. // Abstract Trust is paramount in the adoption of new technologies, especially in the realm of education. Mohamed and Mara shed light on the importance of AI governance programs and establishing AI governance boards to ensure safe and ethical use of technology while managing associated risks. They discuss the impact on customers, potential risks, and mitigation strategies that organizations must consider to protect their brand reputation and comply with regulations. // Bio Mara Pometti Mara is an Associate Design Director at McKinsey & Company, where she helps organisations drive AI adoption through human-centered methods. She defines herself as a data-savvy humanist. Her practice spans across AI, data journalism, and design with the overarching objective of finding the strategic intersection between AI models and human intents to implement responsible AI systems that move organisations forward. Previously, she led the AI Strategy practice at IBM, where she also developed the company’s first-ever data storytelling program. Yet, by background, she is a data journalist. She worked as a data journalist for agencies and newsrooms like Aljazeera. Mara lectured at many universities about how to humanize AI, including the London School of Economics. Her books and writing explore how to weave a humanistic approach to AI development. Mohamed Abusaid Am Mohamed, a tech enthusiast, hacker, avid traveler, and foodie all rolled into one individual. Built his first website when he was 9 and fell in love with computers and the internet ever since. Graduated with computer science from university although dabbled in electrical, electronic, and network engineering before that. When he's not reading up on the latest tech conversations and products on Hacker News, Mohamed spends his time traveling to new destinations and exploring their cuisine and culture. Mohamed works with different companies helping them tackle challenges in developing, deploying, and scaling their analytics to reach its potential. Some topics he's enthusiastic about include MLOps, DataOps, GenerativeAI, Product thinking, and building cross-functional teams to deliver user-first products. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links QuantumBlack, AI by McKinsey: https://www.mckinsey.com/capabilities/quantumblack/how-we-help-clients --------------- ✌️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 Stephen on LinkedIn: https://www.linkedin.com/in/stephen-batifol/ Connect with Mara on LinkedIn: https://www.linkedin.com/in/mara-pometti Connect with Mohamed on LinkedIn: https://www.linkedin.com/in/mabusaid/

Markus Stoll is the Co-Founder of Renumics and the developer behind the open-source interactive ML dataset exploration tool, Spotlight. He shares insights on: AI in Engineering and ManufacturingInteractive ML Data VisualizationML Data Exploration Follow Markus for hands-on articles about leveraging ML while keeping a strong focus on data. Visualize - Bringing Structure to Unstructured Data // MLOps Podcast #258 with Markus Stoll, CTO of Renumics. A huge thank you to SAS for their generous support! // Abstract This talk is about how data visualization and embeddings can support you in understanding your machine-learning data. We explore methods to structure and visualize unstructured data like text, images, and audio for applications ranging from classification and detection to Retrieval-Augmented Generation. By using tools and techniques like UMAP to reduce data dimensions and visualization tools like Renumics Spotlight, we aim to make data analysis for ML easier. Whether you're dealing with interpretable features, metadata, or embeddings, we'll show you how to use them all together to uncover hidden patterns in multimodal data, evaluate the model performance for data subgroups, and find failure modes of your ML models. // Bio Markus Stoll began his career in the industry at Siemens Healthineers, developing software for the Heavy Ion Therapy Center in Heidelberg. He learned about software quality while developing a treatment machine weighing over 600 tons. He earned a Ph.D., focusing on combining biomechanical models with statistical models, through which he learned how challenging it is to bridge the gap between research and practical application in the healthcare domain. Since co-founding Renumics, he has been active in the field of AI for Engineering, e.g., AI for Computer Aided Engineering (CAE), implementing projects, contributing to their open-source library for data exploration for ML datasets (Renumics Spotlight) and writing articles about data visualization. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://renumics.com/ MLSecOps Community: https://community.mlsecops.com/ Blogs: https://towardsdatascience.com/visualize-your-rag-data-evaluate-your-retrieval-augmented-generation-system-with-ragas-fc2486308557 : https://medium.com/itnext/how-to-explore-and-visualize-ml-data-for-object-detection-in-images-88e074f46361 --------------- ✌️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 Markus on LinkedIn: https://www.linkedin.com/in/markus-stoll-b39a42138/

MLOps for GenAI Applications // Special MLOps Podcast episode with Demetrios Brinkmann, Chief Happiness Engineer at MLOps Community. // Abstract Demetrios explores common themes in ML model testing with insights from Erica Greene (Yahoo News), Matar Haller (ActiveFence), Mohamed Elgendy (Kolena), and Catherine Nelson (Freelance Data Scientist). They discuss tiered test cases, functional testing for hate speech, differences between AI and traditional software testing, and the complexities of evaluating LLMs. Demetrios wraps up by inviting feedback and promoting an upcoming virtual conference on data engineering for AI and ML. // Bio At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps Community Podcasts. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building lego houses with his daughter. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Balancing Speed and Safety // Panel // AIQCON - https://youtu.be/c81puRgu3Kw AI For Good - Detecting Harmful Content at Scale // Matar Haller // MLOps Podcast #246 - https://youtu.be/wLKlZ6yHg1k What is AI Quality? // Mohamed Elgendy // MLOps Podcast #229 - https://youtu.be/-Jdmq4DiOew All Data Scientists Should Learn Software Engineering Principles // Catherine Nelson // Podcast #245 - https://youtu.be/yP6Eyny7p20 --------------- ✌️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/ Timestamps: [00:00] Exploring common themes in MLOps community [00:49] Common patterns about model output and testing [01:34] Tiered test case strategy [03:05] Functional testing for models [05:24] Testing coverage and quality [07:47] Evaluating LLMs challenges [08:35] Please like, share, leave a review, and subscribe to our MLOps channels!

Sean Morgan is an active open-source contributor and maintainer and is the special interest group lead for TensorFlow Addons. Learn more about the platform for end-to-end AI Security at https://protectai.com/. MLSecOps is Fundamental to Robust AI Security Posture Management (AISPM) // MLOps Podcast #257 with Sean Morgan, Chief Architect at Protect AI. // Abstract MLSecOps, which is the practice of integrating security practices into the AIML lifecycle (think infusing MLOps with DevSecOps practices), is a critical part of any team’s AI Security Posture Management. In this talk, we’ll discuss how to threat model realistic AIML security risks, how you can measure your organization’s AI Security Posture, and most importantly how you can improve that security posture through the use of MLSecOps. // Bio Sean Morgan is the Chief Architect at Protect AI. In prior roles he's led production AIML deployments in the semiconductor industry, evaluated adversarial machine learning defenses for DARPA research programs, and most recently scaled customers on interactive machine learning solutions at AWS. In his free time, Sean is an active open-source contributor and maintainer, and is the special interest group lead for TensorFlow Addons. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Sean's GitHub: https://github.com/seanpmorgan MLSecOps Community: https://community.mlsecops.com/ --------------- ✌️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 Sean on LinkedIn: https://www.linkedin.com/in/seanmorgan/ Timestamps: [00:00] Sean's preferred coffee [00:10] Takeaways [01:39] Register for the Data Engineering for AI/ML Conference now! [02:21] KubeCon Paris: Emphasis on security and AI [05:00] Concern about malicious data during training process [09:29] Model builders, security, pulling foundational models, nuances [12:13] Hugging Face research on security issues [15:00] Inference servers exposed; potential for attack [19:45] Balancing ML and security processes for ease [23:23] Model artifact security in enterprise machine learning [25:04] Scanning models and datasets for vulnerabilities [29:23] Ray's user interface vulnerabilities lead to attacks [32:07] ML Flow vulnerabilities present significant server risks [36:04] Data ops essential for machine learning security [37:32] Prioritized security in model and data deployment [40:46] Automated scanning tool for improved antivirus protection [42:00] Wrap up