Weekly talks and fireside chats about everything that has to do with the new space emerging around DevOps for Machine Learning aka MLOps aka Machine Learning Operations.... more
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MLOps.community
By Demetrios Brinkmann
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The podcast currently has 380 episodes available.
MLOps.community episodes:
September 20, 2024RAG Quality Starts with Data Quality // Adam Kamor // #262 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.// AbstractDive 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.// BioAdam 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 boardhttps://mlops.pallet.xyz/jobs// MLOps Swag/Merchhttps://mlops-community.myshopify.com/// Related LinksWebsite: https://www.tonic.aiVarious topics about RAG and LLM security are available on Tonic.ai's blogs: https://www.tonic.ai/bloghttps://www.tonic.ai/blog/how-to-prevent-data-leakage-in-your-ai-applications-with-tonic-textual-and-snowpark-container-serviceshttps://www.tonic.ai/blog/rag-evaluation-series-validating-the-rag-performance-of-the-openais-rag-assistant-vs-googles-vertex-search-and-conversationhttps://www.youtube.com/watch?v=5xdyt4oRONUhttps://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/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerCatch 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...more September 17, 2024Who's MLOps for Anyway? // Jonathan Rioux // #261 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.// AbstractThe 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?// BioJonathan 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 boardhttps://mlops.pallet.xyz/jobs// MLOps Swag/Merchhttps://mlops-community.myshopify.com/// Related LinksWebsite: raiks.ca--------------- ✌️Connect With Us ✌️ -------------Join our slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerCatch 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...more September 13, 2024Alignment is Real // Shiva Bhattacharjee // #260 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.// AbstractIf 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.// Bio20 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 boardhttps://mlops.pallet.xyz/jobs// MLOps Swag/Merchhttps://mlops-community.myshopify.com/// Related LinksWebsite: www.truelaw.ai--------------- ✌️Connect With Us ✌️ -------------Join our slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerCatch 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...more September 11, 2024Ax a New Way to Build Complex Workflows with LLMs // Vikram Rangnekar // #259 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.// AbstractAx 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.// BioVikram 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 boardhttps://mlops.pallet.xyz/jobs// MLOps Swag/Merchhttps://mlops-community.myshopify.com/// Related LinksThe unofficial DSPy framework. Build LLM-powered Agents and "Agentic workflows" based on the Stanford DSP paper: https://axllm.devAll 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/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerCatch 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/vikramrTimestamps:[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...more September 05, 2024Building in Production Human-centred GenAI Solutions // Mohamed Abusaid & Mara Pometti// #177 MLOps Coffee Sessions #177 with Mohamed Abusaid and Mara Pometti, Building in Production Human-centred GenAI Solutions sponsored by QuantumBlack, AI by McKinsey.
// AbstractTrust 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.// BioMara PomettiMara 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 AbusaidAm 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 boardhttps://mlops.pallet.xyz/jobs// MLOps Swag/Merchhttps://mlops-community.myshopify.com/// Related LinksQuantumBlack, AI by McKinsey: https://www.mckinsey.com/capabilities/quantumblack/how-we-help-clients--------------- ✌️Connect With Us ✌️ -------------Join our slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerCatch 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-pomettiConnect with Mohamed on LinkedIn: https://www.linkedin.com/in/mabusaid/...more September 03, 2024Visualize - Bringing Structure to Unstructured Data // Markus Stoll // #258 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 Manufacturing
Interactive ML Data Visualization
ML Data ExplorationFollow 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!// AbstractThis 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.// BioMarkus 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 boardhttps://mlops.pallet.xyz/jobs// MLOps Swag/Merchhttps://mlops-community.myshopify.com/// Related LinksWebsite: 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/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerCatch 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/...more September 01, 2024AI Testing Highlights // Special MLOps Podcast Episode MLOps for GenAI Applications // Special MLOps Podcast episode with Demetrios Brinkmann, Chief Happiness Engineer at MLOps Community.
// AbstractDemetrios 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.// BioAt 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 boardhttps://mlops.pallet.xyz/jobs// MLOps Swag/Merchhttps://mlops-community.myshopify.com/// Related LinksBalancing Speed and Safety // Panel // AIQCON - https://youtu.be/c81puRgu3KwAI For Good - Detecting Harmful Content at Scale // Matar Haller // MLOps Podcast #246 - https://youtu.be/wLKlZ6yHg1kWhat is AI Quality? // Mohamed Elgendy // MLOps Podcast #229 - https://youtu.be/-Jdmq4DiOewAll 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/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerCatch 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!...more August 30, 2024MLSecOps is Fundamental to Robust AISPM // Sean Morgan // #257 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.// AbstractMLSecOps, 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.// BioSean 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 boardhttps://mlops.pallet.xyz/jobs// MLOps Swag/Merchhttps://mlops-community.myshopify.com/// Related LinksSean's GitHub: https://github.com/seanpmorganMLSecOps Community: https://community.mlsecops.com/--------------- ✌️Connect With Us ✌️ -------------Join our slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerCatch 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...more August 27, 2024MLOps for GenAI Applications // Harcharan Kabbay // #256 Harcharan Kabbay is a Data Scientist & AI/ML Engineer with Expertise in MLOps, Kubernetes, and DevOps, Driving End-to-End Automation and Transforming Data into Actionable Insights.
MLOps for GenAI Applications // MLOps Podcast #256 with Harcharan Kabbay, Lead Machine Learning Engineer at World Wide Technology.// AbstractThe discussion begins with a brief overview of the Retrieval-Augmented Generation (RAG) framework, highlighting its significance in enhancing AI capabilities by combining retrieval mechanisms with generative models.The podcast further explores the integration of MLOps, focusing on best practices for embedding the RAG framework into a CI/CD pipeline. This includes ensuring robust monitoring, effective version control, and automated deployment processes that maintain the agility and efficiency of AI applications.A significant portion of the conversation is dedicated to the importance of automation in platform provisioning, emphasizing tools like Terraform. The discussion extends to application design, covering essential elements such as key vaults, configurations, and strategies for seamless promotion across different environments (development, testing, and production). We'll also address how to enhance the security posture of applications through network firewalls, key rotation, and other measures.Let's talk about the power of Kubernetes and related tools to aid a good application design.The podcast highlights the principles of good application design, including proper observability and eliminating single points of failure. I would share strategies to reduce development time by creating templates for GitHub repositories by application types to be re-used, also templates for pull requests, thereby minimizing human errors and streamlining the development process.// BioHarcharan is an AI and machine learning expert with a robust background in Kubernetes, DevOps, and automation. He specializes in MLOps, facilitating the adoption of industry best practices and platform provisioning automation. With extensive experience in developing and optimizing ML and data engineering pipelines, Harcharan excels at integrating RAG-based applications into production environments. His expertise in building scalable, automated AI systems has empowered the organization to enhance decision-making and problem-solving capabilities through advanced machine-learning techniques.// MLOps Jobs boardhttps://mlops.pallet.xyz/jobs// MLOps Swag/Merchhttps://mlops-community.myshopify.com/// Related LinksHarcharan's Medium - https://medium.com/@harcharan-kabbayData Engineering for AI/ML Conference: https://home.mlops.community/home/events/dataengforai--------------- ✌️Connect With Us ✌️ -------------Join our slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerCatch all episodes, blogs, newsletters, and more: https://mlops.community/Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/Connect with Harcharan on LinkedIn: https://www.linkedin.com/in/harcharankabbay/locale=en_US...more August 23, 2024BigQuery Feature Store // Nicolas Mauti // #255 Nicolas Mauti is an MLOps Engineer from Lyon (France), Working at Malt.
BigQuery Feature Store // MLOps Podcast #255 with Nicolas Mauti, Lead MLOps at Malt.// AbstractNeed a feature store for your AI/ML applications but overwhelmed by the multitude of options? Think again. In this talk, Nicolas shares how they solved this issue at Malt by leveraging the tools they already had in place. From ingestion to training, Nicolas provides insights on how to transform BigQuery into an effective feature management system.We cover how Nicolas' team designed their feature tables and addressed challenges such as monitoring, alerting, data quality, point-in-time lookups, and backfilling. If you’re looking for a simpler way to manage your features without the overhead of additional software, this talk is for you. Discover how BigQuery can handle it all!// BioNicolas Mauti is the go-to guy for all things related to MLOps at Malt. With a knack for turning complex problems into streamlined solutions and over a decade of experience in code, data, and ops, he is a driving force in developing and deploying machine learning models that actually work in production.When he's not busy optimizing AI workflows, you can find him sharing his knowledge at the university. Whether it's cracking a tough data challenge or cracking a joke, Nicolas knows how to keep things interesting.// MLOps Jobs boardhttps://mlops.pallet.xyz/jobs// MLOps Swag/Merchhttps://mlops-community.myshopify.com/// Related LinksNicolas' Medium - https://medium.com/@nmautiData Engineering for AI/ML Conference: https://home.mlops.community/home/events/dataengforai--------------- ✌️Connect With Us ✌️ -------------Join our slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerCatch all episodes, blogs, newsletters, and more: https://mlops.community/Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/Connect with Nicolas on LinkedIn: https://www.linkedin.com/in/nicolasmauti/?locale=en_USTimestamps:[00:00] Nicolas' preferred beverage[00:35] Takeaways[02:25] Please like, share, leave a review, and subscribe to our MLOps channels![02:57] BigQuery end goal[05:00] BigQuery pain points[10:14] BigQuery vs Feature Stores[12:54] Freelancing Rate Matching issues[16:43] Post-implementation pain points[19:39] Feature Request Process[20:45] Feature Naming Consistency[23:42] Feature Usage Analysis[26:59] Anomaly detection in data[28:25] Continuous Model Retraining Process[30:26] Model misbehavior detection[33:01] Handling model latency issues[36:28] Accuracy vs The Business[38:59] BigQuery cist-benefit analysis[42:06] Feature stores cost savings[44:09] When not to use BigQuery[46:20] Real-time vs Batch Processing[49:11] Register for the Data Engineering for AI/ML Conference now![50:14] Wrap up...more
FAQs about MLOps.community:
How many episodes does MLOps.community have?
The podcast currently has 380 episodes available.
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