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MLOps Coffee Sessions #149 with Jason McCampbell, The Challenges of Deploying (many!) ML Models, co-hosted by Abi Aryan and sponsored by Wallaroo.// AbstractIn order to scale the number of models a team can manage, we need to automate the most common 90% of deployments to allow ops folks to focus on the challenging 10% and automate the monitoring of running models to reduce the per-model effort for data scientists. The challenging 10% of deployments will often be "edge" cases, whether CDN-style cloud-edge, local servers, or running on connected devices.// BioJason McCampbell is the Director of Architecture at Wallaroo.ai and has over 20 years of experience designing and building high-performance and distributed systems. From semiconductor design to simulation, a common thread is that the tools have to be fast, use resources efficiently, and "just work" as critical business applications.   At Wallaroo, Jason is focused on solving the challenges of deploying AI models at scale, both in the data center and at "the edge". He has a degree in computer engineering as well as an MBA and is an alum of multiple early-stage ventures. Living in Austin, Jason enjoys spending time with his wife and two kids and cycling through the Hill Country.// MLOps Jobs board  // MLOps Swag/Merchhttps://mlops-community.myshopify.com/// Related LinksWebsite: https://wallaroo.aiMLOps at the Edge Slack channel: https://mlops-community.slack.com/archives/C02G1BHMJRL--------------- ✌️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 Abi on LinkedIn: https://www.linkedin.com/in/abiaryan/Connect with Jason on LinkedIn: https://www.linkedin.com/in/jasonmccampbell/Timestamps:[00:00] Jason's preferred coffee[01:22] Takeaways[06:06] MLOps at the Edge Slack channel[06:36] Shoutout to Wallaroo![07:34] Jason's background[09:54] Combining Edge and ML[11:03] Defining Edge Computing[13:21] Data transport restrictions[15:02] Protecting IP in Edge Computing[17:48] Decentralized Teams and Knowledge Sharing[20:45] Real-time Data Analysis for Improved Store Security and Efficiency[24:49] How to Ensure Statistical Integrity in Federated Networks[26:50] Architecting ML at the Edge[30:44] Machine Learning models adversarial attacks[33:25] Pros and cons of baking models into containers[34:52] Jason's work at Wallaroo[38:22] Navigating the Market Edge[40:49] Last challenges to overcome[44:15] Data Science Use Cases in Logistics[46:27] Vector trade-offs[49:34] AI at the Edge challenges[50:40] Finding the Sweet Spot[53:46] Driving revenue[55:33] Wrap up

Karl Fezer, Intelligence & MLOps expert, discusses biases, defining intelligence, and the future of large language models in AI. He emphasizes the importance of efficient high-impact tasks in MLOps. The conversation touches on philosophical tangents but relates back to practical applications of these concepts.

MLOps Coffee Sessions #147 with Alex DeBrie, Something About Databases, co-hosted by Abi Aryan.// AbstractFor databases, it feels like we're in the middle of a big shift. The first 10-15 years of the cloud were mostly about using the same core infrastructure patterns, but in the cloud (SQL Server, MySQL, Postgres, Redis, and Elasticsearch).  In the last few years, we're finally seeing data infrastructure that is truly built for the cloud. Elastic, scalable, resilient, managed, etc. Early examples were Snowflake + DynamoDB. The most recent ones are all the 'NewSQL' contenders (Cockroach, Yugabyte, Spanner) or the 'serverless' ones (Neon, Planetscale). Also seeing improvements in caching, search, etc. Exciting times!// BioAlex is an AWS Data Hero and a self-employed AWS consultant and trainer. He is the author of The DynamoDB Book, a comprehensive guide to data modeling with DynamoDB. Previously, he worked for Stedi and for Serverless, Inc., creators of the Serverless Framework. He loves being involved in the AWS & serverless community, and he lives in Omaha, NE with his family.// MLOps Jobs board  jobs.mlops.community// MLOps Swag/Merchhttps://mlops-community.myshopify.com/// RelatedLinks Key Takeaways from the DynamoDB Paper: https://www.alexdebrie.com/posts/dynamodb-paper/Understanding Eventual Consistency in DynamoDB: https://www.alexdebrie.com/posts/dynamodb-eventual-consistency/Two Scoops of Django 1.11: Best Practices for the Django Web Framework: https://www.amazon.com/Two-Scoops-Django-1-11-Practices/dp/0692915729CAP or no CAP?Understanding when the CAP theorem applies and what it means: https://www.alexdebrie.com/posts/when-does-cap-theorem-apply/Stop fighting your database/ The DynamoDB book: https://dynamodbbook.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 Abi on LinkedIn: https://www.linkedin.com/in/abiaryan/Connect with Alex on LinkedIn: https://www.linkedin.com/in/alex-debrie/Timestamps: [00:00] Alex's preferred coffee [00:27] Introduction to Alex DeBrie and DynamoDB [01:05] Takeaways [03:47] Please write down your reviews, and you might have a book by Alex! [04:57] Alex's journey from being an Attorney to becoming a Data Engineer [07:31] Why engineering? [10:07] Serverless Data [12:54] Before Airflow [15:41] Batch vs streaming [17:03] Difficulties in the Batch and Streaming sides [19:45] Modern data infrastructure databases [24:37] Cloud Ecosystem Maturity [27:59] New generation type of Snowflake [29:47] Comparing databases [30:58] What's next on connectors from 2 perspectives? [34:25] Management services at the MLOps level [36:38] DynamoDB [39:32] Why do you like DynamoDB? [41:00] Data used in DynamoDB and size limits [43:46] Comparison of tradeoffs between DynamoDB and Redis [45:52] Preferred opinionated databases [48:43] CAP or no CAP? Understanding when the CAP theorem applies and what it means [52:10] The DynamoDB book [56:17] Chapter you want to expand on in the book [57:43] Next book to write [59:25] ChatGPT iterations [1:01:59] Data modeling book wished to be written [1:03:27] Wrap up

MLOps Coffee Sessions #146 with Shalabh Chaudri, The Ops in MLOps - Process and People, co-hosted by Abi Aryan.// AbstractShalabh talks through their newfound appreciation for the MLOps perspective from a customer success standpoint. Shalabh's emphasis on setting realistic expectations and ensuring the delivery of promised value adds is particularly valuable.    Generally, this episode provides a unique and insightful perspective on MLOps from the lens of customer success.// BioShalabh has worked in the MLOps domain since 2020 at Algorithmia and Union AI. His experience spans startups and small and large public companies. He has 10+ years of experience in the design, delivery, adoption, and business value realization of B2B infrastructure and platform solutions.// MLOps Jobs boardjobs.mlops.community  // MLOps Swag/Merchhttps://mlops-community.myshopify.com/// Related Linkshttps://www.union.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 Abi on LinkedIn: https://www.linkedin.com/in/abiaryan/Connect with Shalabh on LinkedIn: https://www.linkedin.com/in/shalabhchaudhri/Timestamps: [00:00] Shalabh's preferred coffee [01:18] Takeaways [02:57] Huge shout-out to Union AI! [03:46] Reviews [05:26] Shalab's journey [07:00] The people and process of MLOps [10:25] Accuracy measures and Multiple Stakeholders [13:01] UnionAI's success where others fall short [14:45] Legacy equipment [17:06] Legacy tools versus open source [19:27] Cataloging solution [22:51] Stakeholders and maturity levels [24:26] People and Process in MLOps [29:00] Collaboration for Machine Learning [31:08] Overcoming challenges [34:17] AI and leadership decision-making [35:33] Legacy Companies and AI [39:39] Common pitfalls  [42:24] Neglecting ROI [46:25] Speaking to each level [49:50] Being realistic [51:29] Becoming a champion [53:08] Transitioning to machine learning [55:25] Customer's Skill and Success needed in ML  [57:46] Different sizes of companies 

MLOps Coffee Sessions #145 with Sahil Khanna, Griffin, ML platform at Instacart, co-hosted by Mike Del Balso.// Abstract The conversation revolves around the journey of Instacart in implementing machine learning, starting from batch processing to real-time processing. The speaker highlights the importance of real-time processing for businesses and the relevance of Instacart's journey to other machine learning teams.   Sahil emphasizes the soft factors, such as staying customer-focused and the right approach, that contributed to the success of Instacart's machine learning implementation. We also recommend two blog posts by Sahil about Instacart's journey.// Bio Sahil is currently a machine learning engineer at Instacart, where they are building a centralized platform for the training, deployment, and management of diverse ML applications. Before Instacart, Sahil developed ML training and inference platforms at Etsy.// MLOps Jobs board  jobs.mlops.community// MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links--------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunity Sign 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 Mike on LinkedIn: https://www.linkedin.com/in/michaeldelbalso/Connect with Sahil on LinkedIn: www.linkedin.com/in/sahil-khanna-umdTimestamps:[00:00] Sahil's preferred coffee[01:35] Introduction to Sahil Khanna[01:59] Takeaways[08:07] Subscribe to our Newsletter and join our In Real Life Meetups around 30 cities in the world![09:25] Learning how to make Pizza and Focaccia[10:45] Batch prediction style to real-time[13:15] High-Level MLOps Context Determination[17:00] 2 kinds of ML Platform[20:12] The Dilemma of Rapidly Evolving Requirements[24:31] Targeting the Right User: Understanding the ML Platform Team's Customers[25:29] Interesting journey[27:18] Griffin[31:31] Docker base components, a unified interface, and extensible sections[31:50] Navigating the challenges across Consistent Development Environments[36:30] Feature management[38:33] Stages in adopting real-time ML [41:06] On-demand features[42:21] Future of streaming[44:00] Sessions featurization[47:27] Buying third-party products from the engineer and vendor side[50:11] Modular Dependency Integration[51:46] Wrap up

MLOps Coffee Sessions #144 with Matthew Dombrowski, Non-traditional Career Paths in MLOps, co-hosted by Mihail Eric.// Abstract Let's explore the different aspects of ML and data roles and the variety of responsibilities each role entails! This conversation emphasizes the need for understanding the unique insights each role provides and the similarities in responsibilities and soft skills that are required across different roles.   This episode also highlights the significance of stakeholder alignment in the context of working in big companies and the importance of navigating these complexities for a successful career in ML.// Bio Matt has performed a number of MLOps positions, including Solutions Consultant, Solutions Architect, and Product Manager, from startups to large organizations. In his current role, Matt builds tools to help social media influencers discover unique and exciting Amazon products to recommend to their audiences.// MLOps Jobs board  jobs.mlops.community// MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links--------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunity Sign 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 Mihail on LinkedIn: https://www.linkedin.com/in/mihaileric/Connect with Matt on LinkedIn: https://www.linkedin.com/in/matthewdombrowski/Timestamps: [00:00] Matt's preferred coffee [00:28] Mihail's new creation [05:09] Introduction to Matthew Dombrowski [06:02] Takeaways [08:30] Pizza and coffee nerds [10:54] Data careers [13:35] Matt's progression through the ML sphere [20:10] Dealing with machine learning [23:20] Transition from deep technical implementer to PM role [27:42] Data is a product [29:30] From start-ups to big companies [32:41] Ambiguity of ML [36:17] Matt's daily routine [40:23] Social media influencers [42:07] Developer advocate [44:00] Stakeholder alignment [49:41] Non-traditional career paths, military influence [54:11] Good ways to recommend people to get into ML [57:56] MLOps Meetups all over the world [59:00] Wrap up

MLOps Coffee Sessions #143 with Jill Chase & Manmeet Gujral, Investing in the Next Generation of AI & ML.// AbstractInvestors are currently focusing on developer tooling and the foundational AI model movement, as they have seen explosive growth in this area. This podcast explores the impact of foundational models on investment thesis and the future of machine learning operations.The discussion also touches on the idea of generative AI and large language models, and their potential impact on MLOps in the next 10 years. Jill and Manmeet from Capital G share their insights on this topic.// BioJill ChaseJill is an investor at CapitalG, where she focuses on enterprise software, with an emphasis on data infrastructure and AI/ML.Prior to joining CapitalG, Jill worked in senior startup operating roles, both as the CEO of a private equity-backed business and as the founder of a Y Combinator-backed startup.Jill graduated magna cum laude from Williams College with a dual degree in Economics and Psychology and was captain of the women’s basketball team. She came out to the West Coast to earn an MBA from the Stanford Graduate School of Business, but she was born and raised in Boston, where she had the opportunity to cheer on the most impressive era of professional sports a city has ever experienced (Go Patriots).She lives in the Bay Area with her husband, where they spend weekends doing as many outside activities as possible, such as pickleball, tennis, hiking, and running.Manmeet GujralManmeet is a member of the CapitalG investment team, where he focuses on enterprise software, AI & ML, open source, and product-led-growth companies. Prior to joining CapitalG in 2021, Manmeet worked in product marketing and operations at Tecton. Before that, he worked as a consultant at Bain & Company in San Francisco, where he specialized in the go-to-market strategy for technology companies and private equity investment diligence. Manmeet is originally from Albany, New York, and graduated from Dartmouth College with a dual degree in Computer Science and Economics. Manmeet is highly opinionated about pizza, an avid New York sports fan, and always willing to share his latest house or hip-hop playlists.// MLOps Jobs board  jobs.mlops.community// MLOps Swag/Merchhttps://mlops-community.myshopify.com/// Related Links--------------- ✌️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 Jill on LinkedIn: https://www.linkedin.com/in/jill-greenberg-chase-53747538/Connect with Manmeet on LinkedIn: https://www.linkedin.com/in/manmeet-gujral/Timestamps: [00:00] Manmeet and Jill's preferred coffee [00:25] Takeaways [01:31] CapitalG, Jill, and Manmeet's Background [05:12] Sideswiping MLOps by Foundational Models [08:50] MLOps space and the market revenue  [14:50] Foundational models B to C style [20:37] Foundational models taking over [23:00] Uninnovative sentiments [27:50] 2 prototypes of companies [31:51] Finding product market fit [36:20] MLOps market growth changes [40:30] Monster valuations [41:43] The ones that got away [44:07] Wrap up

MLOps Coffee Sessions #142 with Murtuza Shergadwala, Approaches to Fairness and XAI, co-hosted by Abi Aryan. This episode is sponsored by Fiddler AI.// AbstractThe field of Explainable Artificial Intelligence (XAI) is continuously evolving, with an increasing focus on providing model-centric explanations in a human-centric manner. However, better frameworks and training for users are needed to fully utilize the potential of XAI tools.  Additionally, there is a discrepancy in the approach to fairness in XAI, with the industry approaching it from a regulatory standpoint, while academia is engaging in more discussion and research on the topic.// BioDr. Murtuza Shergadwala is a data scientist at Fiddler AI. His background is in human-machine interaction and design decision-making. He received his Ph.D. from Purdue University in Mechanical Engineering. Prior to Fiddler, he was a postdoc at the Games User Interaction and Intelligence Lab at UC Santa Cruz where he focused on using Bayesian approaches for modeling cognition and investigating the theory of mind. He’s super passionate about fairness in AI for underrepresented communities.// MLOps Jobs board  jobs.mlops.community// MLOps Swag/Merchhttps://mlops-community.myshopify.com/// Related Linkshttps://murtuzashergadwala.wixsite.com/murtuzahttps://www.fiddler.ai/blog/detecting-intersectional-unfairness-in-ai-part-1--------------- ✌️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 Abi on LinkedIn: https://www.linkedin.com/in/abiaryan/Connect with Murtuza on LinkedIn: https://www.linkedin.com/in/murtuza-shergadwala/Timestamps: [00:00] Moto's preferred coffee [00:35] Introduction to Murtuza Shergadwala [01:06] Takeaways [04:30] Huge shout-out to Fiddler AI for sponsoring this episode! [05:00] Don't forget to like, comment, and subscribe. Give us a rating  [06:10] Moto's background and transition to Human-centric AI [10:52] Decision-making behaviors of engineering designers in design contests [15:10] Gaining insights from data decisions [18:00] Defining latent variables [20:32] Designer's perspective on building systems [23:14] XAI as a movement [27:47] Selling regulations and bridging the gap [32:18] Data integrity towards detecting outliers, alerting, and data drifts [34:32] Dealing with alerts and alert fatigue [37:31] Approaches and their limitations [39:10] Alert-level systems [42:19] Alerts putting into practice [45:30] Creative alerts [47:02] One solution fits all? [50:08] Wrap up

MLOps Coffee Sessions #141 with Stephen Bailey, Airflow Sucks for MLOps, co-hosted by Joe Reis.// AbstractStephen discusses his experience working with data platforms, particularly the challenges of training and sharing knowledge among different stakeholders. This talk highlights the importance of having clear priorities and a sense of practicality and mentions the use of modular job design and data classification to make it easier for end users to understand which data to use.   Stephen also mentions the importance of being able to move quickly and not getting bogged down in the quest for perfection. We recommend Stephen's blog post "Airflow's Problem" for further reading.// BioStephen has worked as a data scientist, analyst, manager, and engineer, and loves all the domains equally. He currently works at Whatnot, a collectibles marketplace that focuses on live shopping, and has previously worked in privacy tech at Immuta. He has his Ph.D. from Vanderbilt University in educational cognitive neuroscience, but it has yet to help him understand why his three children are so crazy.// MLOps Jobs board jobs.mlops.community // MLOps Swag/Merchhttps://mlops-community.myshopify.com/// Related LinksAirflow's Problem blog post: https://stkbailey.substack.com/p/airflows-problemAirflow's Problem and the reception it got on Hacker News: https://news.ycombinator.com/item?id=32317558--------------- ✌️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 Joe on LinkedIn: https://www.linkedin.com/in/josephreis/Connect with Stephen on LinkedIn: https://www.linkedin.com/in/stkbailey/Timestamps: [00:00] Stephen's preferred coffee [00:19] Introduction to co-host Joe Reis [01:40] Takeaways [06:29] Subscribe to our newsletters! [06:55] Shout out to our sponsor, Wallaroo! [08:05] Whatnot [10:47] Stephen's side hustle [14:35] Stephen's work breakdown at Whatnot [18:03] Fundamental tensions in the data world [21:27] Initial questions to answer that you were on the right path [24:06] Recommender systems [28:15] Coordinating with ML teams [29:43] Daxter [31:38] Too advanced, more challenging [34:37] Orchestration layer [36:14] Decision criteria [39:23] Human design aspect of Daxter [40:53] Orchestration layer centralization and sharing knowledge with stakeholders [46:18] Airflow's Problem and the reception it got on Hacker News [51:00] Wrap up

MLOps Coffee Sessions #140 with Sakib Dadi, The Evolution of ML Infrastructure sponsored by Wallaroo.// Abstract The toolkit and infrastructure empowering machine learning practitioners are advancing as ML adoption accelerates. We'll go through the current landscape of ML tooling, startups, and new projects from an investor's perspective.// Bio Sakib is a vice president in the San Francisco office, where he primarily focuses on early-stage investments in developer platforms, data infrastructure, and machine learning. He has been involved with Bessemer’s investments in Prefect, Coiled, Arcion, Periscope Data (acquired by Sisense), Okera, npm (acquired by GitHub), and LaunchDarkly. Before joining Bessemer, Sakib worked in product at Viagogo, an international marketplace for buying and selling tickets for live events.     Sakib also worked in the technology investment banking group at Morgan Stanley and as an engineer at Innova Dynamics (acquired by TPK), a startup manufacturing flexible touchscreen displays.// MLOps Jobs boardjobs.mlops.community  // 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 Sakib on LinkedIn: https://www.linkedin.com/in/sakib-dadi-77938937/Timestamps: [00:00] Sakib's preferred coffee [00:13] Introduction to Sakib Dadi [01:33] Sakib's background [02:40] Shout out to this episode's Sponsor, Wallaroo! [04:17] ML investing [05:57] Investing regrets [08:06] Transformers are today what would be tomorrow? [09:18] Company that you wish existed now [10:23] Current thoughts on the MLOps market [12:32] MLOps transition to Generative AI [15:52] Mind maps [17:03] Jasper [22:14] Intersection   [24:10] Differences in models in-house [26:08] Orchestration space [28:23] Nuances of Monitoring [29:20] Demetrios' theory on Monitoring [31:48] Non-funded Monitoring Companies [34:29] Investment risks [36:55] Orchestration markets [39:38] MLOps market at a plateau [42:14] Vertical problems, vertical solutions   [45:45] Sakib is starting a company [50:10] Structuring deals [51:50] Infrastructure tools [53:26] Firing a founder   [53:49] Parting ways with a founder [57:07] Wrap up