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Author: Demetrios Brinkmann

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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.
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Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/ Tom Smoker is the cofounder of an early stage tech company empowering developers to create knowledge graphs within their RAG pipelines. Tom is a technical founder, and owns the research and development of knowledge graphs tooling for the company. Managing Small Knowledge Graphs for Multi-agent Systems // MLOps podcast #236 with Tom Smoker, Technical Founder of whyhow.ai. A big thank you to  @latticeflow  for sponsoring this episode! LatticeFlow - https://latticeflow.ai/ // Abstract RAG is one of the more popular use cases for generative models, but there can be issues with repeatability and accuracy. This is especially applicable when it comes to using many agents within a pipeline, as the uncertainty propagates. For some multi-agent use cases, knowledge graphs can be used to structurally ground the agents and selectively improve the system to make it reliable end to end. // Bio Technical Founder of WhyHow.ai. Did Masters and PhD in CS, specializing in knowledge graphs, embeddings, and NLP. Worked as a data scientist to senior machine learning engineer at large resource companies and startups. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models: https://arxiv.org/abs/2401.01313 --------------- ✌️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 Tom on LinkedIn: https://www.linkedin.com/in/thomassmoker/
Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/ David Nunez, based in Santa Barbara, CA, US, is currently a Co-Founder and Partner at Abstract Group, bringing experience from previous roles at First Round Capital, Stripe, and Slab. Just when we Started to Solve Software Docs, AI Blew Everything Up // MLOps Podcast #235 with Dave Nunez, Partner of Abstract Group co-hosted by Jakub Czakon. Huge thank you to Zilliz for sponsoring this episode. Zilliz - https://zilliz.com/. // Abstract Over the previous decade, the recipe for making excellent software docs mostly converged on a set of core goals: Create high-quality, consistent content Use different content types depending on the task Make the docs easy to find For AI-focused software and products, the entire developer education playbook needs to be rewritten. // Bio Dave lives in Santa Barbara, CA with his wife and four kids. He started his tech career at various startups in Santa Barbara before moving to San Francisco to work at Salesforce. After Salesforce, he spent 2+ years at Uber and 5+ years at Stripe leading internal and external developer documentation efforts. In 2021, he co-authored Docs for Developers to help engineers become better writers. He's now a consultant, advisor, and angel investor for fast-growing startups. He typically invests in early-stage startups focusing on developer tools, productivity, and AI. He's a reading nerd, Lakers fan, and golf masochist. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://www.abstractgroup.co/ Book: docsfordevelopers.com About Dave: https://gamma.app/docs/Dave-Nunez-about-me-002doxb23qbblme?mode=doc https://review.firstround.com/investing-in-internal-documentation-a-brick-by-brick-guide-for-startups https://increment.com/documentation/why-investing-in-internal-docs-is-worth-it/ Writing to Learn paper by Peter Elbow: https://peterelbow.com/pdfs/Writing_for_Learning-Not_just_Demonstrating.PDF --------------- ✌️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 Dave on LinkedIn: https://www.linkedin.com/in/djnunez/ Connect with Kuba on LinkedIn: https://www.linkedin.com/in/jakub-czakon/?locale=en_US Timestamps: [00:00] Dave's preferred coffee [00:13] Introducing this episode's co-host, Kuba [00:36] Takeaways [02:55] Please like, share, leave a review, and subscribe to our MLOps channels! [03:23] Good docs, bad docs, and how to feel them [06:51] Inviting Dev docs and checks [10:36] Stripe's writing culture [12:42] Engineering team writing culture [14:15] Bottom-up tech writer change [18:31] Strip docs cult following [24:40] TriDocs Smart API Injection [26:42] User research for documentation [29:51] Design cues [32:15] Empathy-driven docs creation [34:28 - 35:35] Zilliz Ad [35:36] Foundational elements in documentation [38:23] Minimal infrastructure of information in "Read Me" [40:18] Measuring documentation with OKRs [43:58] Improve pages with Analytics [47:33] Google branded doc searches [48:35] Time to First Action [52:52] Dave's day in and day out and what excites him [56:01] Exciting internal documentation [59:55] Wrap up
Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/ Cody Peterson has a diverse work experience in the field of product management and engineering. Cody is currently working as a Technical Product Manager at Voltron Data, starting from May 2023. Previously, they worked as a Product Manager at dbt Labs from July 2022 to March 2023. MLOps podcast #234 with Cody Peterson, Senior Technical Product Manager at Voltron Data | Ibis project // Open Standards Make MLOps Easier and Silos Harder. Huge thank you to Weights & Biases for sponsoring this episode. WandB Free Courses -http://wandb.me/courses_mlops // Abstract MLOps is fundamentally a discipline of people working together on a system with data and machine learning models. These systems are already built on open standards we may not notice -- Linux, git, scikit-learn, etc. -- but are increasingly hitting walls with respect to the size and velocity of data. Pandas, for instance, is the tool of choice for many Python data scientists -- but its scalability is a known issue. Many tools make the assumption of data that fits in memory, but most organizations have data that will never fit in a laptop. What approaches can we take? One emerging approach with the Ibis project (created by the creator of pandas, Wes McKinney) is to leverage existing "big" data systems to do the heavy lifting on a lightweight Python data frame interface. Alongside other open source standards like Apache Arrow, this can allow data systems to communicate with each other and users of these systems to learn a single data frame API that works across any of them. Open standards like Apache Arrow, Ibis, and more in the MLOps tech stack enable freedom for composable data systems, where components can be swapped out allowing engineers to use the right tool for the job to be done. It also helps avoid vendor lock-in and keep costs low. // Bio Cody is a Senior Technical Product Manager at Voltron Data, a next-generation data systems builder that recently launched an accelerator-native GPU query engine for petabyte-scale ETL called Theseus. While Theseus is proprietary, Voltron Data takes an open periphery approach -- it is built on and interfaces through open standards like Apache Arrow, Substrait, and Ibis. Cody focuses on the Ibis project, a portable Python dataframe library that aims to be the standard Python interface for any data system, including Theseus and over 20 other backends. Prior to Voltron Data, Cody was a product manager at dbt Labs focusing on the open source dbt Core and launching Python models (note: models is a confusing term here). Later, he led the Cloud Runtime team and drastically improved the efficiency of engineering execution and product outcomes. Cody started his carrer as a Product Manager at Microsoft working on Azure ML. He spent about 2 years on the dedicated MLOps product team, and 2 more years on various teams across the ML lifecycel including data, training, and inferencing. He is now passionate about using open source standards to break down the silos and challenges facing real world engineering teams, where engineering increasingly involves data and machine learning. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Ibis Project: https://ibis-project.org Apache Arrow and the “10 Things I Hate About pandas”: https://wesmckinney.com/blog/apache-arrow-pandas-internals/ --------------- ✌️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 Cody on LinkedIn: https://linkedin.com/in/codydkdc
Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/ Syed Asad is an Innovator, Generative AI & Machine Learning Engineer, and a Champion for Ethical AI MLOps podcast #233 with Syed Asad, Lead AI/ML Engineer at KiwiTech // Retrieval Augmented Generation. A big thank you to @ for sponsoring this episode! AWS - // Abstract Everything and anything around RAG. // Bio Currently Exploring New Horizons: Syed is diving deep into the exciting world of Semantic Vector Searches and Vector Databases. These innovative technologies are reshaping how we interact with and interpret vast data landscapes, opening new avenues for discovery and innovation. Specializing in Retrieval Augmented Generation (RAG): Syed's current focus also includes mastering Retrieval Augmented Generation Techniques (RAGs). This cutting-edge approach combines the power of information retrieval with generative models, setting new benchmarks in AI's capability and application. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://sanketgupta.substack.com/ Our paper on this topic "Generalized User Representations for Transfer Learning": https://arxiv.org/abs/2403.00584 Sanket's blogs on Medium in the past: https://medium.com/@sanket107 --------------- ✌️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 Syed on LinkedIn: https://www.linkedin.com/in/syed-asad-76815246/
Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/ Sanket works as a Senior Machine Learning Engineer at Spotify working on building end-to-end audio recommender systems. Models built by his team are used across Spotify in many different products including Discover Weekly and Autoplay. MLOps podcast #232 with Sanket Gupta, Senior Machine Learning Engineer at Spotify // RecSys at Spotify. A big thank you to LatticeFlow for sponsoring this episode! LatticeFlow - https://latticeflow.ai/ // Abstract LLMs with foundational embeddings have changed the way we approach AI today. Instead of re-training models from scratch end-to-end, we instead rely on fine-tuning existing foundation models to perform transfer learning. Is there a similar approach we can take with recommender systems? In this episode, we can talk about: a) how Spotify builds and maintains large-scale recommender systems, b) how foundational user and item embeddings can enable transfer learning across multiple products, c) how we evaluate this system d) MLOps challenges with these systems // Bio Sanket works as a Senior Machine Learning Engineer on a team at Spotify building production-grade recommender systems. Models built by my team are being used in Autoplay, Daily Mix, Discover Weekly, etc. Currently, my passion is how to build systems to understand user taste - how do we balance long-term and short-term understanding of users to enable a great personalized experience. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://sanketgupta.substack.com/ Our paper on this topic "Generalized User Representations for Transfer Learning": https://arxiv.org/abs/2403.00584 Sanket's blogs on Medium in the past: https://medium.com/@sanket107 --------------- ✌️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 Sanket on LinkedIn: www.linkedin.com/in/sanketgupta107 Timestamps: [00:00] Sanket's preferred coffee [00:37] Takeaways [02:30] RecSys are RAGs [06:22] Evaluating RecSys parallel to RAGs [07:13] Music RecSys Optimization [09:46] Dealing with cold start problems [12:18] Quantity of models in the recommender systems [13:09] Radio models [16:24] Evaluation system [20:25] Infrastructure support [21:25] Transfer learning [23:53] Vector database features [25:31] Listening History Balance [26:35 - 28:06] LatticeFlow Ad [28:07] The beauty of embeddings [30:13] Shift to real-time recommendation [34:05] Vector Database Architecture Options [35:30] Embeddings drive personalized [40:16] Feature Stores vs Vector Databases [42:33] Spotify product integration strategy [45:38] Staying up to date with new features [47:53] Speed vs Relevance metrics [49:40] Wrap up
Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/ Ryan Carson. CEO Founder for 20 years Built and sold 3 startups Helping build a global community of AI devs with Intel. MLOps podcast #231 with Ryan Carson, Senior AI Dev Community Lead at Intel Huge thank you to Zilliz for sponsoring this episode. Zilliz - https://zilliz.com/ // Abstract Ryan shares his professional journey, tracing his transition from building Treehouse to joining Intel. The conversation evolves into a deep dive into Carson's aspiration to democratize access to AI development. Furthermore, he expounds on the exciting prospects of new technology like Gaudi three, a new ASIC for AI workloads. Ryan emphasizes the need for driving competition in compute to lower prices and increase access, underlining the importance of associating individual work with company-based OKRs or KPIs. There is also a reflection on the essentiality of forging quality relationships in professional settings and aligning work with top-level OKRs. Discussion on the potential benefits of AI in constructing and maintaining professional interactions is explored. Touching upon practical applications of AI, they also delve into smaller projects, the possibility of one-person companies, and the role of AI for daily interactions. The episode concludes with an expression of optimism about technological advances shaping the future and an appreciation for the enlightening conversation. // Bio Ryan has been a founder, entrepreneur, and CEO for 20 years, successfully building, scaling, and selling three companies. He's passionate about empowering people to become developers and then connecting them together in a global community. After earning a degree in Computer Science in Colorado, Ryan moved to the UK and worked as a web developer. He then organized global tech conferences, hosting thousands of attendees and influential speakers such as Mark Zuckerberg, the founders of Android, Instagram, and Twitter, among others. His company also produced Twitter’s and Stack Overflow’s developer conferences. Following that, Ryan started an online Computer Science school. Under his leadership, the team grew to over 100 employees, educating more than 1,000,000 students. During this period, he secured $23 million in venture capital and earned recognition as Entrepreneur of the Year. Over the last two years Ryan dove deep into AI and LLMs. He built an educational proof-of-concept called maple.coach, which focuses on teaching Sales. The platform is built using technologies like Next.js, TypeScript, gpt-4, and Vercel. Outside of work, Ryan shares his life with his wife of 20 years and their two teenagers in Connecticut. They enjoy spending their free time sailing and taking walks with their Sheltie, Brinkley. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: ryancarson.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 Ryan on LinkedIn: https://www.linkedin.com/in/ryancarson/
Salman Avestimehr is a Dean's Professor, the inaugural director of the USC-Amazon Center for Secure and Trusted Machine Learning (Trusted AI), and director of the Information Theory and Machine Learning (vITAL) research lab. He is also the CEO and co-founder of FedML. MLOps podcast #230 with Salman Avestimehr, CEO & Founder of FedML, FedML Nexus AI: Your Generative AI Platform at Scale. A big thank you to FEDML for sponsoring this episode! // Abstract FedML is your generative AI platform at scale to enable developers and enterprises to build and commercialize their own generative AI applications easily, scalably, and economically. Its flagship product, FedML Nexus AI, provides unique features in enterprise AI platforms, model deployment, model serving, AI agent APIs, launching training/Inference jobs on serverless/decentralized GPU cloud, experimental tracking for distributed training, federated learning, security, and privacy. // Bio Salman is a professor, the inaugural director of the USC-Amazon Center for Secure and Trusted Machine Learning (Trusted AI), and the director of the Information Theory and Machine Learning (vITAL) research lab at the Electrical and Computer Engineering Department and Computer Science Department of the University of Southern California. Salman is also the co-founder and CEO of FedML. He received his Ph.D. in Electrical Engineering and Computer Sciences from UC Berkeley in 2008. Salman does research in the areas of information theory, decentralized and federated machine learning, secure and privacy-preserving learning, and computing. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links https://www.avestimehr.com/ https://fedml.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 Salman on LinkedIn: https://www.linkedin.com/company/fedml/ Timestamps: [00:00] AI Quality: First in-person conference on June 25 [01:28] Salman's preferred coffee [01:49] Takeaways [03:33] Please like, share, leave a review, and subscribe to our MLOps channels! [03:53] Challenges that inspired Salman's work [06:20] Controlled ownership [08:11] Dealing with data leakage and privacy problems [10:45] In-house ML Model Deployment [13:36] FEDML: Comprehensive Model Deployment [17:27] Integrating FEDML with Kubernetes [19:46] AI Evaluation Trends [24:37] Enhancing NLP with ML [25:48] FEDML: Canary, A/B, Confidence [29:36] FEDML customers [33:21] On-premise platform for secure data management [37:16] Future prediction: data's crucial for better applications [38:18] Maturity in evaluating and improving steps [41:38] Focus on ownership [45:12] Benefits of smaller models for specific use cases [48:57] Verify sensitive tasks, trust quick, important mobile content creation [51:50] Wrap up
Mohamed Elgendy is the Co-Founder & CEO at Kolena. Additionally, Mohamed Elgendy has had 1 past job as the Director Of Product and Engineering at Synapse Technology Corporation. Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/ MLOps podcast #228 with Mohamed Elgendy, Co-founder & CEO of Kolena Inc., What is AI Quality? // Abstract Delve into the multifaceted concept of AI Quality. Demetrios and Mo explore the idea that AI quality is dependent on the specific domain, equitable to the difference in desired qualities between a $1 pen and a $100 pen. Mo underscores the performance of a product being in sync with its intended functionality and the absence of unknown risks as the pillars of AI Quality. They emphasize the need for comprehensive quality checks and adaptability of standards to differing product traits. Issues affecting edge deployments like latency are also highlighted. A deep dive into the formation of gold standards for AI, the nuanced necessities for various use cases, and the paramount need for collaboration among AI builders, regulators, and infrastructure firms form the core of the discussion. Elgendy brings to light their ambitious AI Quality Conference, aiming to set tangible, effective, but innovation-friendly Quality standards for AI. The dialogue also accentuates the urgent need for diversification and representation in the tech industry, the variability of standards and regulations, and the pivotal role of testing in AI and machine learning. The episode concludes with an articulate portrayal of how enhanced testing can streamline the entire process of machine learning. // Bio Mohamed is the Co-founder & CEO of Kolena and the author of the book “Deep Learning for Vision Systems”. Previously, he built and managed AI/ML organizations at Amazon, Twilio, Rakuten, and Synapse. Mohamed regularly speaks at AI conferences like Amazon's DevCon, O'Reilly's AI conference, and Google's I/O. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: www.kolena.io Deep Learning for Vision Systems book: https://www.amazon.com/Learning-Vision-Systems-Mohamed-Elgendy/dp/1617296198/ --------------- ✌️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 Mo on LinkedIn: https://www.linkedin.com/in/moelgendy/ Timestamps: [00:00] Mo's preferred coffee [00:07] Takeaways [02:52] See you all in San Francisco on June 25! [03:04] Please like, share, leave a review, and subscribe to our MLOps channels! [03:22] AI Quality in Mo's eyes [08:36] Quality Standards for Software [14:11] Common Chatbot Functionality [19:20] The Birth of Innovation [24:27] Transforming Insights into Standards [30:27] Testing: One step to quality [34:58] Two different data points to be harmonized [37:29] Model cards [39:12] Test Coverage Democratizes Collaboration [42:55] Representation matters [44:50] Wrap up
Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com Simon Karasik⁠ is a proactive and curious ML Engineer with 5 years of experience. Developed & deployed ML models at WEB and Big scale for Ads and Tax. Huge thank you to Nebius AI for sponsoring this episode. Nebius AI - https://nebius.ai/ MLOps podcast #228 with Simon Karasik, Machine Learning Engineer at Nebius AI, Handling Multi-Terabyte LLM Checkpoints. // Abstract The talk provides a gentle introduction to the topic of LLM checkpointing: why is it hard, how big are the checkpoints. It covers various tips and tricks for saving and loading multi-terabyte checkpoints, as well as the selection of cloud storage options for checkpointing. // Bio Full-stack Machine Learning Engineer, currently working on infrastructure for LLM training, with previous experience in ML for Ads, Speech, and Tax. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links --------------- ✌️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 Simon on LinkedIn: https://www.linkedin.com/in/simon-karasik/ Timestamps: [00:00] Simon preferred beverage [01:23] Takeaways [04:22] Simon's tech background [08:42] Zombie models garbage collection [10:52] The road to LLMs [15:09] Trained models Simon worked on [16:26] LLM Checkpoints [20:36] Confidence in AI Training [22:07] Different Checkpoints [25:06] Checkpoint parts [29:05] Slurm vs Kubernetes [30:43] Storage choices lessons [36:02] Paramount components for setup [37:13] Argo workflows [39:49] Kubernetes node troubleshooting [42:35] Cloud virtual machines have pre-installed mentoring [45:41] Fine-tuning [48:16] Storage, networking, and complexity in network design [50:56] Start simple before advanced; consider model needs. [53:58] Join us at our first in-person conference on June 25 all about AI Quality
Sol Rashidi is an esteemed executive, leader, and influencer within the AI, Data, and Technology space.  Having helped IBM launch Watson in 2011 as one of the earliest world applications of Artificial Intelligence, Sol has pioneered some of the early advancements of space.  Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/ Huge thank you to  @WeightsBiases  for sponsoring this episode. WandB Free Courses - http://wandb.me/courses_mlops MLOps podcast #227 with Sol Rashidi, CEO & Co-Founder of ExecutiveAI, Leading Enterprise Data Teams. // Abstract In the dynamic landscape of MLOps and data leadership, Sol shares invaluable insights on building successful teams and driving impactful projects. In this podcast episode, Sol delves into the importance of prioritizing relationships, introduces a pragmatic "Wrong Use Cases Formula" to streamline project prioritization, and emphasizes the critical role of effective communication in data leadership. Her wealth of experience and practical advice provide a roadmap for navigating the complexities of MLOps and leading data-driven initiatives to success. // Bio With eight (8) patents granted, 21 filed, and received awards that include: "Top 100 AI People" 2023 "The Top 75 Innovators of 2023" "Top 65 Most Influential Women in 2023" "Forbes AI Maverick of the 21st Century" 2022 “Top 10 Global Women in AI & Data”, 2023 "Top AI 100 Award", 2023 “50 Most Powerful Women in Tech”, 2022 “Global 100 Power List” - 2021, 2022, 2023 “Top 20 CDOs Globally” - 2022 "Chief Analytics Officer of the Year" - 2022 "Isomer Innovators of the Year" - 2021, 2022, 2023 "Top 100 Innovators in Data & Analytics” - 2020, 2021, 2022, 2023 "Top 100 Women in Business" - 2022 Sol is an energetic business executive and a goal-oriented technologist, skilled at coupling her technical acumen with story-telling abilities to articulate business value with both startups and Fortune 100's who are leaning into data, AI, and technology as a competitive advantage while wanting to preserve the legacy in which they were founded upon. Sol has served as a C-Suite member across several Fortune 100 & Fortune 500 companies including: Chief Analytics Officer - Estee Lauder Chief Data & Analytics Officer - Merck Pharmaceuticals EVP, Chief Data Officer - Sony Music Chief Data & AI Officer - Royal Caribbean Cruise Lines Sr. Partner leading the Digital & Innovation Practice- Ernsty & Young Partner leading Watson Go-To-Market & Commercialization - IBM Sol now serves as the CEO of ExecutiveAI LLC. A company dedicated to democratizing Artificial Intelligence for Humanity and is considered an outstanding and influential business leader who is influencing the space traveling the world as a keynote speaker, and serving as the bridge between established Gen1.0 markets and those evolving into 4.0. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Sol's Book will be out on April 30, 2024 Your AI Survival Guide: Scraped Knees, Bruised Elbows, and Lessons Learned from Real-World AI Deployments: https://www.amazon.com/Your-Survival-Guide-Real-World-Deployments/dp/1394272634?ref_=ast_author_mpb --------------- ✌️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 Sol on LinkedIn: https://www.linkedin.com/in/sol-rashidi-a672291/
Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/ Chad Sanderson is passionate about data quality, and fixing the muddy relationship between data producers and consumers. He is a former Head of Data at Convoy, a LinkedIn writer, and a published author. He lives in Seattle, Washington, and is the Chief Operator of the Data Quality Camp. Huge thank you to @amazonwebservices for sponsoring this episode. AWS - https://aws.amazon.com/ MLOps podcast #226 with Chad Sanderson, CEO & Co-Founder of Gable, The Rise of Modern Data Management. // Abstract In this session, Chad Sanderson, CEO of Gable.ai and author of the upcoming O’Reilly book: "Data Contracts," tackles the necessity of modern data management in an age of hyper iteration, experimentation, and AI. He will explore why traditional data management practices fail and how the cloud has fundamentally changed data development. The talk will cover a modern application of data management best practices, including data change detection, data contracts, observability, and CI/CD tests, and outline the roles of data producers and consumers. Attendees will leave with a clear understanding of modern data management's components and how to leverage them for better data handling and decision-making. // Bio Chad Sanderson, CEO of Gable.ai, is a prominent figure in the data tech industry, having held key data positions at leading companies such as Convoy, Microsoft, Sephora, Subway, and Oracle. He is also the author of the upcoming O'Reilly book, "Data Contracts” and writes about the future of data infrastructure, modeling, and contracts in his newsletter “Data Products.” // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links AWS Trainium and Inferentia: https://aws.amazon.com/machine-learning/trainium/ https://aws.amazon.com/machine-learning/inferentia/ --------------- ✌️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 Chad on LinkedIn: https://www.linkedin.com/in/chad-sanderson/
Patrick Beukema has a Ph.D. in neuroscience and has worked on AI models for brain decoding, which analyzes the brain's activity to decipher what people are seeing and thinking. Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/ Huge thank you to LatticeFlow for sponsoring this episode. LatticeFlow - https://latticeflow.ai/ MLOps podcast #225 with Patrick Beukema, Head / Technical Lead of the Environmental AI, Applied Science Organization at AI2, Beyond AGI, Can AI Help Save the Planet? // Abstract AI will play a central role in solving some of our greatest environmental challenges. The technology that we need to solve these problems is in a nascent stage -- we are just getting started. For example, the combination of remote sensing (satellites) and high-performance AI operating at a global scale in real-time unlocks unprecedented avenues to new intelligence. MLOPs is often overlooked on AI teams, and typically there is a lot of friction in integrating software engineering best practices into the ML/AI workflow. However, performance ML/AI depends on extremely tight feedback loops from the user back to the model that enables high iteration velocity and ultimately continual improvement. We are making progress but environmental causes need your help. Join us fight for sustainability and conservation. // Bio Patrick is a machine learning engineer and scientist with a deep passion for leveraging artificial intelligence for social good. He currently leads the environmental AI team at the Allen Institute for Artificial Intelligence (AI2). His professional interests extend to enhancing scientific rigor in academia, where he is a strong advocate for the integration of professional software engineering practices to ensure reliability and reproducibility in academic research. Patrick holds a Ph.D. from the Center for Neuroscience at the University of Pittsburgh and the Center for the Neural Basis of Cognition at Carnegie Mellon University, where his research focused on neural plasticity and accelerated learning. He applied this expertise to develop state-of-the-art deep learning models for brain decoding of patient populations at a startup, later acquired by BlackRock. His earlier academic work spanned research on recurrent neural networks, causal inference, and ecology and biodiversity. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Variety of relevant papers/talks/links on Patrick's website: https://pbeukema.github.io/ --------------- ✌️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 Patrick on LinkedIn: https://www.linkedin.com/in/plbeukema/ Timestamps: [00:00] AI Quality Conference [01:29] Patrick's preferred coffee [02:00] Takeaways [04:14] Learning how to learn journey [07:04] Patrick's day to day [08:39] Environmental AI [11:07] Environmental AI models [14:35] Nature Inspires Scientific Advances [18:11] R&D [24:58] Iterative Feedback-Driven Development [26:37 - 28:07] LatticeFlow Ad [33:58] Balancing Metrics for Success [38:16] Model Retraining Pipeline [44:11] Series Models: Versatility [45:57] Edge Models Enhance Output [50:22] Custom Models for Specific Data [53:53] Wrap up
Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/ Verena Weber believes that GenAI is going to transform the way we work and interact with devices. Her mission is to help companies prepare for this transformation. She has strong expertise in NLP and over 7 years of experience in Machine Learning. Huge thank you to  @zilliz  for sponsoring this episode. Zilliz - https://zilliz.com/ MLOps podcast #224 with Verena Weber, Generative AI Consultant at Verena Weber, GenAI in Production - Challenges and Trends. // Abstract The goal of this talk is to provide insights into challenges for Generative AI in production as well as trends aiming to solve some of these challenges. The challenges and trends Verena see are: Model size and moving towards mixture of experts architectures context window - new breakthroughs for context lengths from unimodality to multimodality, next step large action models? regulation in form of the EU AI Act Verena uses the differences between Gemini 1.0 and Gemini 1.5 to exemplify some of these trends. // Bio Verena leverages GenAI in natural language to elevate business competitiveness and navigate its transformative impact. Her varied experience in multiple roles and sectors underpins her ability to extract business value from AI, blending deep technical expertise with strong business acumen. Post-graduation, she consulted in Data Science at Deloitte and then advanced her skills in NLP, Deep Learning, and GenAI as a Research Scientist at Alexa team, Amazon. Passionate about gender diversity in tech, she mentors women to thrive in this field. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: verenaweber.de Sign up for Verena's newsletter: https://verenas-newsletter-63558b.beehiiv.com/ Zilliz - https://zilliz.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 Verena on LinkedIn: https://www.linkedin.com/in/verena-weber-134178b9/ Timestamps: [00:00] AI Quality Conference [01:33] Verena's preferred coffee [02:15] Takeaways [06:33] Ski Person of Influence [11:31] Verena's background in the last 5-10 years [14:24] Tech Evolution: Rapid Transformation [18:13] Working at Amazon and key challenges [20:10] Research-inspired suggestions [22:21] AI Updates Impact Workflows [22:52] Alexa Query Distribution Analysis [24:06] Innovative Solutions for Alexa [25:27] Robust T5 Data Prompting [27:38] Audio Data Quality Challenges [28:21-29:28] Zilliz ad [29:28] Alexa data transcription and data cleaning [35:38] Considering needs, costs, and complexity [37:44] ChatGPt is not ideal for classification [39:32] Comparison of model building using TF, IDF [45:08] Struggle to boost diversity in conference speakers [47:30] Creating safe environments helps underrepresented individuals participate [48:29] Wrap up
Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/ MLOps Coffee Sessions Special episode with Databricks, Introducing DBRX: The Future of Language Models, fueled by our Premium Brand Partner, Databricks. DBRX is designed to be especially capable of a wide range of tasks and outperforms other open LLMs on standard benchmarks. It also promises to excel at code and math problems, areas where others have struggled. Our panel of experts will get into the technical nuances, potential applications, and implications of DBRx for businesses, developers, and the broader tech community. This session is a great opportunity to hear from insiders about how DBRX's capabilities can benefit you. // Bio Denny Lee - Co-host Denny Lee is a long-time Apache Spark™ and MLflow contributor, Delta Lake maintainer, and a Sr. Staff Developer Advocate at Databricks. A hands-on distributed systems and data sciences engineer with extensive experience developing internet-scale data platforms and predictive analytics systems. He has previously built enterprise DW/BI and big data systems at Microsoft, including Azure Cosmos DB, Project Isotope (HDInsight), and SQL Server. Davis Blalock Davis Blalock is a research scientist and the first employee at MosaicML. He previously worked at PocketSonics (acquired 2013) and completed his PhD at MIT, where he was advised by John Guttag. He received his M.S. from MIT and his B.S. from the University of Virginia. He is a Qualcomm Innovation Fellow, NSF Graduate Research Fellow, and Barry M. Goldwater Scholar. He is also the author of Davis Summarizes Papers, one of the most widely-read machine learning newsletters. Bandish Shah Bandish Shah is an Engineering Manager at MosaicML/Databricks, where he focuses on making generative AI training and inference efficient, fast, and accessible by bridging the gap between deep learning, large-scale distributed systems, and performance computing. Bandish has over a decade of experience building systems for machine learning and enterprise applications. Prior to MosaicML, Bandish held engineering and development roles at SambaNova Systems where he helped develop and ship the first RDU systems from the ground up, and Oracle where he worked as an ASIC engineer for SPARC-based enterprise servers. Abhi Venigalla Abhi is an NLP architect working on helping organizations build their own LLMs using Databricks. Joined as part of the MosaicML team and used to work as a researcher at Cerebras Systems. Ajay Saini Ajay is an engineering manager at Databricks leading the GenAI training platform team. He was one of the early engineers at MosaicML (acquired by Databricks) where he first helped build and launch Composer (an open source deep learning training framework) and afterwards led the development of the MosaicML training platform which enabled customers to train models (such as LLMs) from scratch on their own datasets at scale. Prior to MosaicML, Ajay was co-founder and CEO of Overfit, an online personal training startup (YC S20). Before that, Ajay worked on ML solutions for ransomware detection and data governance at Rubrik. Ajay has both a B.S. and MEng in computer science with a concentration in AI from MIT. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://www.databricks.com/ Databricks DBRX: https://www.databricks.com/blog/introducing-dbrx-new-state-art-open-llm --------------- ✌️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/
Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/ // Abstract Dive into the challenges of scaling AI models from Minimum Viable Product (MVP) to full production. The panel emphasizes the importance of continually updating knowledge and data, citing examples like teaching AI systems nuanced concepts and handling brand name translations. User feedback's role in model training, alongside evaluation steps like human annotation and heuristic-based assessment, was highlighted. The speakers stressed the necessity of tooling for user evaluation, version control, and regular performance updates. Insights on in-house and external tools for annotation and evaluation were shared, providing a comprehensive view of the complexities involved in scaling AI models. // Bio Alex Volkov - Moderator Alex Volkov is an AI Evangelist at Weights & Biases, celebrated for his expertise in clarifying the complexities of AI and advocating for its beneficial uses. He is the founder and host of ThursdAI, a weekly newsletter, and podcast that explores the latest in AI, its practical applications, open-source, and innovation. With a solid foundation as an AI startup founder and 20 years in full-stack software engineering, Alex offers a deep well of experience and insight into AI innovation. Eric Peter Product management leader and 2x founder with experience in enterprise products, data, and machine learning. Currently building tools for generative AI @Databricks. Donné Stevenson Focused on building AI-powered products that give companies the tools and expertise needed to harness to power of AI in their respective fields. Phillip Carter Phillip is on the product team at Honeycomb where he works on a bunch of different developer tooling things. He's an OpenTelemetry maintainer -- chances are if you've read the docs to learn how to use OTel, you've read his words. He's also Honeycomb's (accidental) prompt engineering expert by virtue of building and shipping products that use LLMs. In a past life, he worked on developer tools at Microsoft, helping bring the first cross-platform version of .NET into the world and grow to 5 million active developers. When not doing computer stuff, you'll find Phillip in the mountains riding a snowboard or backpacking in the Cascades. Andrew Hoh Andrew Hoh is the President and Co-Founder of LastMile AI. Previously, he was a Group PM Manager at Meta AI, driving product for their AI Platform. Previously, he was the Product Manager for the Machine Learning Infrastructure team at Airbnb and a founding team member of Azure Cosmos DB, Microsoft Azure's distributed NoSQL database. He graduated with a BA in Computer Science from Dartmouth College. A big thank you to our Premium Sponsors,  @Databricks  and  @baseten  for their generous support! // Sign up for our Newsletter to never miss an event: https://mlops.community/join/ // Watch all the conference videos here: https://home.mlops.community/home/collections // Check out the MLOps Community podcast: https://open.spotify.com/show/7wZygk3mUUqBaRbBGB1lgh?si=242d3b9675654a69 // Read our blog: mlops.community/blog // Join an in-person local meetup near you: https://mlops.community/meetups/ // MLOps Swag/Merch: https://mlops-community.myshopify.com/ // Follow us on Twitter: https://twitter.com/mlopscommunity //Follow us on Linkedin: https://www.linkedin.com/company/mlopscommunity/
Shane Morris is now a Senior Executive Advisor at Devis. Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/ Huge thank you to  @WeightsBiases  for sponsoring this episode. WandB Free Courses - https://wandb.ai/telidavies/ml-news/reports/Introducing-W-B-MLOps-Courses-Free-Course-Effective-MLOps-Model-Development--VmlldzozMDk2ODA2 MLOps podcast #223 with Shane Morris, Senior Executive Advisor of Devis, Data Engineering in the Federal Sector. // Abstract Let's focus on autonomous systems rather than automation, and then super-narrow it down to smaller, cheaper, and more accessible autonomous systems. // Bio Former music and entertainment data and software person somehow moves into defense and national security, with hilarious and predictable results. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links AI Quality in Person Conference: https://www.aiqualityconference.com/ Website: https://shanemorris.sucks TikTok: https://www.tiktok.com/@shanemorrisdotsucks WandB Free Courses - https://wandb.ai/telidavies/ml-news/reports/Introducing-W-B-MLOps-Courses-Free-Course-Effective-MLOps-Model-Development--VmlldzozMDk2ODA2 --------------- ✌️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 Shane on LinkedIn: https://www.linkedin.com/in/shanetollmanmorris/
Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/ ⁠Peter Guagenti⁠ is an accomplished business builder and entrepreneur with expertise in strategy, product development, marketing, sales, and operations. Peter has helped build multiple successful start-ups to exits, fueling high growth in each company along the way. He brings a broad perspective, deep problem-solving skills, the ability to drive innovation amongst teams, and a proven ability to convert strategy into action -- all backed up by a history of delivering results. Huge thank you to AWS for sponsoring this episode. AWS - https://aws.amazon.com/ MLOps podcast #222 with Peter Guagenti, President & CMO of Tabnine - What Business Stakeholders Want to See from the ML Teams. // Abstract Peter Guagenti shares his expertise in the tech industry, discussing topics from managing large-scale tech legacy applications and data experimentation to the evolution of the Internet. He returns to his history of building and transforming businesses, such as his work in the early 90s for People magazine's website and his current involvement in AI development for software companies. Guagenti discusses the use of predictive modeling in customer management and emphasizes the importance of re-architecting solutions to fit customer needs. He also delves deeper into the AI tools' effectiveness in software development and the value of maintaining privacy. Guagenti sees a bright future in AI democratization and shares his company's development of AI coding assistants. Discussing successful entrepreneurship, Guagenti highlights balancing technology and go-to-market strategies and the value of failing fast. // Bio Peter Guagenti is the President and Chief Marketing Officer at Tabnine. Guagenti is an accomplished business leader and entrepreneur with expertise in strategy, product development, marketing, sales, and operations. He most recently served as chief marketing officer at Cockroach Labs, and he previously held leadership positions at SingleStore, NGINX (acquired by F5 Networks), and Acquia (acquired by Vista Equity Partners). Guagenti also serves as an advisor to a number of visionary AI and data companies including DragonflyDB, Memgraph, and Treeverse. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links AI Quality in Person Conference: https://www.aiqualityconference.com/ Measuring the impact of GitHub Copilot Survey: https://resources.github.com/learn/pathways/copilot/essentials/measuring-the-impact-of-github-copilot/ AWS Trainium and Inferentia: https://aws.amazon.com/machine-learning/trainium/ https://aws.amazon.com/machine-learning/inferentia/AI coding assistants: 8 features enterprises should seek: https://www.infoworld.com/article/3694900/ai-coding-assistants-8-features-enterprises-should-seek.htmlCareers at Tabnine: https://www.tabnine.com/careers --------------- ✌️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 Peter on LinkedIn: https://www.linkedin.com/in/peterguagenti/
Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/ Amritha Arun Babu Mysore has been an expert in the field of consumer electronics, software products, and online marketplaces for the past 15 years. She has experience developing supply chains from the ground up, delivering AI-based products to millions of users, and advocating for ethical AI across Amazon, Wayfair, Salesforce, and NetApp. Abhik Choudhury is a Senior Analytics Managing Consultant and Data Scientist with 11 years of experience in designing and implementing scalable data solutions for organizations across various industries. Huge thank you to  @latticeflow  for sponsoring this episode. LatticeFlow - https://latticeflow.ai/ MLOps podcast #221 with Amritha Arun Babu Mysore, ML Product Leader at Klaviyo and Abhik Choudhury, Managing Consultant Analytics at IBM, MLOps - Design Thinking to Build ML Infra for ML and LLM Use Cases. // Abstract As machine learning (ML) and large language models (LLMs) continue permeating industries, robust ML infrastructure and operations (ML Ops) are crucial to deploying these AI systems successfully. This podcast discusses best practices for building reusable, scalable, and governable ML Ops architectures tailored to ML and LLM use cases. // Bio Amritha Arun Babu Mysore Amritha is an accomplished technology leader with over 12 years of experience spearheading product innovation and strategic initiatives at both large enterprises and rapid-growth startups. Leveraging her background in engineering, supply chain, and business, Amritha has led high-performing teams to deliver transformative solutions solving complex challenges. She has driven product road mapping, requirements analysis, system design, and launch execution for advanced platforms in domains like machine learning, logistics, and e-commerce. Abhik Choudhury Abhik is a Senior Analytics Managing Consultant and Data Scientist with 11 years of experience in designing and implementing scalable data solutions for organizations across various industries. Throughout his career, Abhik developed a strong understanding of AI/ML, Cloud computing, database management systems, data modeling, ETL processes, and Big Data Technologies. Abhik's expertise lies in leading cross-functional teams and collaborating with stakeholders at all levels to drive data-driven decision-making in longitudinal pharmacy and medical claims and wholesale drug distribution areas. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links AI Quality in Person Conference in collaboration with Kolena: https://www.aiqualityconference.com/ LatticeFlow website: https://latticeflow.ai/ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Abhik on LinkedIn: https://www.linkedin.com/in/abhik-choudhury-35450058 Connect with Amritha on LinkedIn: https://www.linkedin.com/in/amritha-arun-babu-a2273729/
Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/ Demetrios Brinkmann is the founder of the MLOps Community. Brinkmann fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and Machine Learning. Huge thank you to Weights & Biases for sponsoring this episode. Weights & Biases - https://wandb.ai/site MLOps podcast #220 with our very own Founder of MLOps Community, Demetrios Brinkmann, Looking Back on 4 Years of the MLOps Community. // Abstract In this lively podcast episode, Mihail Eric hosts Demetrios Brinkmann, the founder of the MLOps Community, discussing its origin, structure, and challenges. Demetrios shares amusing tales of job hunting on LinkedIn and building the community despite lacking technical expertise, emphasizing the value of sharing and humor. They delve into the practicalities of hosting events, transitioning from self-funded to sponsorship-based, and tease upcoming activities with renowned speakers. Mihail and Demetrios explore job dynamics, the importance of sustained relationships, and diverse engagement methods like newsletters and volunteering. Demetrios reflects on his journey to Germany post-company closure, envisioning a global hub for AI learning, embodying the community's mission. // Bio At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.community meetups. 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 AI Quality in Person Conference in collaboration with Kolena: https://www.aiqualityconference.com/ Weights & Biases Free Course: https://wandb.ai/telidavies/ml-news/reports/Introducing-W-B-MLOps-Courses-Free-Course-Effective-MLOps-Model-Development--VmlldzozMDk2ODA2What Does Best in Class AI/ML Governance Look Like in Fin Services? // Charles Radclyffe // MLOps #2: https://youtu.be/l52sRMVPVk0 --------------- ✌️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/ Timestamps: [00:00] Demetrios preferred coffee and bizarre listening [01:44] The MLOps Community Brainchild [04:22] The MLOps Community today [07:15] AI Quality in Person Conference on June 25th! [08:42] Community Quality [10:00] Community Learnings and the Genesis [17:55] The 600 Mark [20:15] The Feedback form [22:52] Demetrios' Journey and Learnings [29:01] Building full tolerance [29:55] Weights & Biases Free Course Ad [34:52] Building community involvement for professional success and networking [38:52] Balance in Community Growth [43:56] Collection of volunteers [49:00] Events Challenges [53:28] The future of MLOps Community [59:40] "Caveman" lifestyle choice [1:00:45] Stronger Hallucinogen [1:02:30] Wrap up
Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/ Huge thank you to ⁠Databricks⁠ AI for sponsoring this episode. Databricks - http://databricks.com/ Bandish Shah is an Engineering Manager at MosaicML/Databricks, where he focuses on making generative AI training and inference efficient, fast, and accessible by bridging the gap between deep learning, large-scale distributed systems, and performance computing. Davis Blalock is a Research Scientist and the first employee of Mosaic ML: a GenAI startup acquired for $1.3 billion by Databricks. MLOps podcast #219 with Databricks' Engineering Manager, Bandish Shah and Research Scientist Davis Blalock, The Art and Science of Training Large Language Models. // Abstract What's hard about language models at scale? Turns out...everything. MosaicML's Davis and Bandish share war stories and lessons learned from pushing the limits of LLM training and helping dozens of customers get LLMs into production. They cover what can go wrong at every level of the stack, how to make sure you're building the right solution, and some contrarian takes on the future of efficient models. // Bio Bandish Shah Bandish Shah is an Engineering Manager at MosaicML/Databricks, where he focuses on making generative AI training and inference efficient, fast, and accessible by bridging the gap between deep learning, large-scale distributed systems, and performance computing. Bandish has over a decade of experience building systems for machine learning and enterprise applications. Prior to MosaicML, Bandish held engineering and development roles at SambaNova Systems where he helped develop and ship the first RDU systems from the ground up, and Oracle where he worked as an ASIC engineer for SPARC-based enterprise servers. Davis Blalock Davis Blalock is a research scientist at MosaicML. He completed his PhD at MIT, advised by Professor John Guttag. His primary work is designing high-performance machine learning algorithms. He received his M.S. from MIT and his B.S. from the University of Virginia. He is a Qualcomm Innovation Fellow, NSF Graduate Research Fellow, and Barry M. Goldwater Scholar. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links AI Quality In-person Conference: AI Quality in Person Conference: https://www.aiqualityconference.com/ Website: http://databricks.com/ Davis Summarizes Papers ⁠Newsletter signup link Davis' Newsletters: Learning to recognize spoken words from five unlabeled examples in under two seconds: https://arxiv.org/abs/1609.09196 Training on data at 5GB/s in a single thread: https://arxiv.org/abs/1808.02515 Nearest-neighbor searching through billions of images per second in one thread with no indexing: https://arxiv.org/abs/1706.10283 Multiplying matrices 10-100x faster than a matrix multiply (with some approximation error): https://arxiv.org/abs/2106.10860 Hidden Technical Debt in Machine Learning Systems: https://proceedings.neurips.cc/paper_files/paper/2015/file/86df7dcfd896fcaf2674f757a2463eba-Paper.pdf --------------- ✌️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 Davis on LinkedIn: https://www.linkedin.com/in/dblalock/ Connect with Bandish on LinkedIn: https://www.linkedin.com/in/bandish-shah/
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Comments (2)

Marco Gorelli

"in Kaggle you normally see a 1-1 ratio of positive to negative examples" huh? has he ever done a Kaggle competition? this statement is totally off

Jul 27th
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