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Coffee Sessions #44 with Grant Wright of SEEK Ltd., Autonomy vs. Alignment: Scaling AI Teams to Deliver Value. /Abstract Setting AI teams up for success can be difficult, especially when you’re trying to balance the need to provide teams with autonomy to innovate and solve interesting problems while ensuring they are aligned to the organizations' strategy. Operating models, rituals and processes can really help to set teams up for success; but, there is no right answer, and as you scale and priorities change your approach needs to change too. Grant shares some of his learnings in establishing a cross-functional team of data scientists, engineers, analysts, product managers, and otologists to solve employment information problems at SEEK, and how the team has evolved as they’ve scaled from a team of 30 in Melbourne Australia to over 100 team members across 5 countries in the past three years. // Bio Grant heads the Artificial Intelligence & Product Analytics teams at SEEK, where he leads a global team of over 120 Data Scientists, Software Engineers, Ontologists, and AI Product Managers who deliver AI Services to online employment and education platforms across the Asia Pacific and the Americas. Grant has held various strategy and product and tech leadership roles over the past 15 years, with experience in scaling, AI teams to deliver outcomes across multiple geographies. Grant holds a Bachelor of Computer and Information Science (Software Development) and a Bachelor of Business (Economics) from the Auckland University of Technology. --------------- ✌️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 Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/ Connect with Grant on LinkedIn: https://www.linkedin.com/in/wrightgrant/

In this Machine Learning System Design Review, Shaji Chennan Kunnummel walks us through the system design for Pinterest’s near-real-time architecture for detecting similar images. We discuss their usage of Kafka, Flink, rocksdb, and much more. Starting with the high-level requirements for the system, we discussed Pinterest’s focus on debuggability and an easy transition from their batch processing system to stream processing. We then touch on the different system interfaces and components involved such as Manas—Pinterest’s custom search engine—and how it all ends up in their custom graph database, downstream Kafka streams, and to Pinterest’s feature store—Galaxy. With Shaji’s expert knowledge of the system, we were able to do a deep dive into the system’s architecture and some of its components. // Experiences 15+ years of experience in software product development. Led multiple teams in a highly agile, collaborative, and cross-functional environment. Designed and implemented highly scalable, fault-tolerant, and optimized distributed systems that scale to handle millions of requests per second. In-depth knowledge of Object-oriented programming and design patterns in C++/Java/Python/Golang. Designed and built complex data pipelines and microservices to train and serve machine learning models. Built analytics pipelines for processing and mining high-volume data set using Hadoop and Map-Reduce frameworks. In-depth knowledge of distributed storage, consistency models, NoSQL data modeling, Cloud computing environment (AWS and Google Cloud).

MLOps community meetup #69! Last Wednesday we talked to Emmanuel Raj, Senior Machine Learning Engineer at TietoEvry. //Abstract The talk focuses on simplifying/demystifying MLOps, encourages others to take steps to learn this powerful SE method. We also talked about Emmanuel's journey in ML engineering, the evolution of MLOps, daily life, and SE problems, and what's next in MLOps (fusion of AIOps, EU AI regulations impact on MLOps workflow, etc). //Bio Emmanuel Raj is a Finland-based Senior Machine Learning Engineer. He is a passionate ML Researcher, Software engineer, speaker, and author. He is also a Machine Learning Engineer at TietoEvry and a Researcher at Arcada University of Applied Sciences in Finland. With over 6+ years of experience building ML solutions in the industry, he has worked on multiple domains such as Healthcare, Manufacturing, Finance, Retail, e-commerce, aviation, etc.    Emmanuel is passionate about democratizing AI and bringing state-of-the-art research to the industry. He has a keen interest in R&D in technologies such as Edge AI, Blockchain, NLP, MLOps, and Robotics. He believes the best way to learn is to teach and he is passionate about teaching about new technologies, that's one reason for writing a book and making an online course on MLOps.    Emmanuel is the author of the book "Engineering MLOps". The book covers industry best case practices and hands-on implementation to Rapidly build, test, and manage production-ready machine learning life cycles at scale. There is a big evolution happening in Data science for good, and we are moving away from notebooks and models sharing to a collaborative way of working via MLOps. We will discuss this big evolution of DevOps, MLOps, Data Engineering, Data Science, and Data-Driven business in the meetup. ----------- 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 Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Emmanuel on LinkedIn: https://www.linkedin.com/in/emmanuelraj7/ // Other Links:   www.emmanuelraj.com https://www.youtube.com/watch?v=m32k9jcY4pY https://www.youtube.com/watch?v=1sGECHbc9zg

MLOps community meetup #68! Last Wednesday we talked to Veselina Staneva of TeachableHub, Simarpal Khaira of Intuit, and Korri Jones of Chick-fil-A, Inc. //Abstract Building, Designing, or even just casting the vision for MLOps for your company, whether a large corporation or an agile start-up up, shouldn't be a nigh-impossible task. Complex, but not an impossible mountain to climb.    In this meetup, we talked about the steps necessary to unlock the potential of data science for your organization, regardless of size. //Bio Veselina Staneva - Co-founder & Head of Product, TeachableHub Over the past few years, Vesi work at a product company called CloudStrap.io, where together with her team they are simplifying cloud technologies and crafting modern solutions that lay a solid foundation for digital transformation at scale. Vesi's main focus currently is their new product TeachableHub.com - an ML deployment and serving platform for teams, where she heads Product and Customer Development. In the past, Vesi had quite a diverse experience in managing projects for global enterprise companies such as telecommunications and internet service provider GTT and managed printing services giant HPInc, as well as deep-diving into e-commerce business development while running online stores on 7 Amazon markets as well as WordPress shops, where she managed to get from 0 to $30K MRR in less than a year without a dollar spent on paid advertising. In Vesi's free time, she enjoys spending the rest of her energy doing all kinds of sports, as well as participate in non-professional triathlons and mountain bike ultra races. Simarpal Khaira - Senior Product Manager, Intuit Simarpal is the product manager driving product strategy for Feature Management and Machine Learning tools at Intuit. Prior to Intuit, he was at Ayasdi, a machine learning startup, leading product efforts for machine learning solutions in the financial services space. Before that, he worked at Adobe as a product manager for Audience Manager, a data management platform for digital marketing. Korri Jones - Senior Lead Machine Learning Engineer, Chick-fil-A, Inc. Korri Jones is a Sr Lead Machine Learning Engineer and Innovation Coach at Chick-fil-A, Inc. in Atlanta, Georgia where he is focused on MLOps. Prior to his work at Chick-fil-A, he worked as a Business Analyst and product trainer for NavMD, Inc., was an adjunct professor at Roane State Community College, and instructor for the Project GRAD summer program at Pellissippi State Community College and the University of Tennessee, Knoxville. Korri's accolades are just as diverse, and he was in the inaugural 40 under 40 for the University of Tennessee in 2021, Volunteer of the year with the Urban League of Greater Atlanta with over 1000 hours in a single calendar year and has received the “Looking to the Future” award within his department at Chick-fil-A among many others, including best speaker awards in business case competitions.  However, the best award he has received so far is being a loving husband to his wife Lydia. ----------- 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 Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Vesi on LinkedIn: https://www.linkedin.com/in/veselina-d-staneva/ Connect with Simar on LinkedIn: https://www.linkedin.com/in/simarpal-khaira-6318959/ Connect with Korri on LinkedIn: https://www.linkedin.com/in/korri-jones-mba-780ba56/

Coffee Sessions #43 with Kyle Gallatin of Etsy, Maturing Machine Learning in Enterprise. //Abstract The definition of Data Science in production has evolved dramatically in recent years. Despite increasing investments in MLOps, many organizations still struggle to deliver ML quickly and effectively. They often fail to recognize an ML project as a massively cross-functional initiative and confuse deployment with production. Kyle will talk about both the functional and non-functional requirements of production ML, and the organizational challenges that can inhibit companies from delivering value with ML. // Bio Kyle Gallatin is currently a Software Engineer for Machine Learning Infrastructure at Etsy. He primarily focuses on operationalizing the training, deployment, and management of machine learning models at scale. Prior to Etsy, Kyle delivered ML microservices and lead the development of MLOps workflows at the pharmaceutical company Pfizer. In his spare time, Kyle mentors data scientists and writes ML blog posts for Towards Data Science. --------------- ✌️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 Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/ Connect with Kyle on LinkedIn: https://www.linkedin.com/in/kylegallatin/ // Takeaways Data science is still poorly defined and there is a large variance in organizational maturity   Basically, everything we need for mature ML in modern organizations exists technically except for the strategy, mentality, organization, and governance Organizations who poorly define data science often overburden their data scientists, but there are expectations that data scientists know some engineering Operationalizing data science is not that different from software engineering, and software engineering can be one of the most valuable skill sets for a data scientist. // Q&A with Kyle as a data science mentor:   https://www.youtube.com/watch?v=7byRQGHD39w&t=1s

MLOps community meetup #66! Last Wednesday we talked to Alfredo Deza, Author and Speaker. //Abstract In this episode, the MLOps community talks about the importance of bringing DevOps principles and discipline into Machine Learning. Alfredo explains insights around creating the MLOps role, automation, constant feedback loops, and the number one objective - to ship Machine Learning models into production.    Additionally, we covered some aspects of getting started with Machine Learning that is critical, in particular how democratization ML knowledge is critical to a better environment, from libraries to courses, to production results. Spreading the knowledge is key! //Bio Alfredo Deza is a passionate software engineer, speaker, author, and former Olympic athlete. With almost two decades of DevOps and software engineering experience, he teaches Machine Learning Engineering and gives lectures around the world about software development, personal development, and professional sports.    Alfredo has written several books about DevOps and Python including Python For DevOps and Practical MLOps. He continues to share his knowledge about resilient infrastructure, testing, and robust development practices in courses, books, and presentations.   Alfredo Deza is the author of Python for DevOps and Practical MLOps. ----------- 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 Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Chris on LinkedIn: https://www.linkedin.com/in/chrisbergh/ Timestamps: [00:00] Introduction to Alfredo Deza [03:00] Alfredo's background in tech [13:15] Who is this book for? [14:15] "The reason why we need a Machine Learning book is that there's definitely a knowledge gap." [16:05] Hierarchy of MLOps [17:16] "Automation has to be the basis of pretty much, everything." [19:03] Logging - "When in doubt, log it out!" [24:50] Maturity [29:55] "The notion of self-healing is very appealing." [31:20] Learning Test [37:40] "Catch things as early as possible. Anything that comes at the end of the process, the closer you are to the production, the more expensive it could get."   [37:54] "Expensive can be the dollar amount in engineering time, or it can be the dollar amount in services that you're using to produce, and the dollar amount on how long it would take to ship the version that fixes the problem." [39:20] "Why not scan your containers before they hit the production and catch anything that has a critical vulnerability announced?" [40:08] Interrupibility standards and pains [42:34] "It is critical that we make it easier. How about we no longer point fingers and stigmatize people who don't do Machine Learning. The more people doing Machine Learning today, the better we're off." [45:50] Simple and opinionated or flexible and complex   [46:45] "You have to strike a balance but you have to stay true to your principles."   [50:38] Abstraction Layers [56:57] Take a risk or stay safe? [57:20] "I think, you're gonna have risk everywhere you are. You're gonna have risk when you hire a Machine Learning Engineer. You're gonna have a risk with a Data Scientist. You're gonna have a risk with a Software Engineer."

MLOps community meetup #65! Last Wednesday we talked to Kseniia Melnikova, Product Owner (Data/AI), SoftwareOne. //Abstract In this MLOps Meetup, we talked about the Machine Learning model lifecycle and development stages and then analyze the main mistakes that everybody does at each stage. Kseniia also provided the audience with solutions to the mistakes and we discussed existing tools for experiment management. //Bio Kseniia is a product owner for Data/AI-based products. Right now, she is working mostly with numeric data analysis, customer insights, and product recommendations. Previously Kseniia worked at Samsung Research with the biometrics team. She was studying computer science in Russia (Moscow) and a little bit of management in South Korea (Seoul). One of the most interesting directions of research - Model Lifecycle Management Systems and Reproducibility. ----------- 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 Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Kseniia on LinkedIn: https://www.linkedin.com/in/kseniia-melnikova/ Timestamps: [00:00] Introduction to Kseniia Melnikova [02:00] MLOps World Conference Announcement [03:40] AI Development process: Common Mistakes [07:45] Step 1: Planning [07:48] Mistake #1: Personal Decisions - Teamwork [08:31] Mistake #1: Cases [09:00] Mistake #1: Solution [11:52] Scrum [12:50] "In Scrum, it's hard to plan because especially in research, you don't know which result affects new tasks that's why it might be a little slow for Machine Learning." [14:28] Step 2: Data Processing [14:34] Mistake #2: Chaos with Datasets [15:26] Mistake #2: Cases [16:48] Mistake #2: Solution [20:12] Step 3: Experiments [20:21] Mistake #3: Lack of Experiments Tracking [22:13] Mistake #3: Case - Manual Experiments Tracking [24:10] Mistake #3: Solutions [25:57] Experiments Tracking Tools Example: MLFlow UI [26:46] Awareness of Existing Tools [28:21] Tools' Features [29:21] Possible Combination [29:48] Another Possible Combination [30:24] Best Practice [31:42] Mistake #0: Lack of Information Sharing [32:26] Mistake #0: Solution - Organize more meetings/standups!   [34:18] Find Your Mistakes [34:41] Mistake #0: Solution - Organize more meetings/standups!   [35:35] Audio Data [39:32] Experiment tracking of only 1 ML engineer [41:38] "I prefer reproducibility tools because it's automatic and it also takes a lot of time to manually upload the results into conference." [43:03] AI Development Check-list [43:40] Check-list Results [44:52] "I think it's always interesting to rate yourself to share the results with other people to compete out of it." [45:10] Why to Implement [45:17] "If we have more automation on experimentations for data sets versioning, it will lead to less manual work." [45:28] "AI Development process implementation will have the possibility to reproduce and compare experiments." [45:37] "AI Development process implementation will make you comfortable on solving the issues you'll face every day." [45:52] "AI Development process implementation will lead to a faster commercialization cycle because you will take less time on the process and more time for the results." [46:03] "If we will take all the principles of AI Development process implementation, it will lead to easy communication between team members. You'll gain trust, have great teamwork, and everyone will have respect for each other."   [46:50] War stories prior to having AI Development process [49:50] Calculating the lost money

Coffee Sessions #42 with Amit Paka of Fiddler AI, Model Performance Monitoring. //Abstract Machine Learning accelerates business growth but is prone to performance degradation due to its high reliance on data. Moreover, MLOps is often fragmented in many organizations, causing frictions to debug models in production. With new rules from the EU that focus on trust and transparency, it’s becoming more important to keep track of model performance. But how? We propose a new framework, a centralized ML Model Performance Management powered by Explainable AI. Learn more about how you can stay compliant while maximizing your model performance at all times with explainability and continuous monitoring. //Bio Amit is the co-founder and CPO of Fiddler, a Machine Learning Monitoring company that empowers companies to efficiently monitor and troubleshoot ML models with Explainable AI. Prior to founding Fiddler, Paka led the shopping apps product team at Samsung. Paka founded Parable, the Creative Photo Network, now part of the Samsung family. He also led PayPal's consumer in-store mobile payments launching innovations like hardware beacon payments and has developed successful startup products particularly in online advertising - paid search, a contextual, ad exchange, and display advertising. Paka has passions for actualizing new concepts, building great teams, and pushing the envelope, and aims to leverage these skills to help define how AI can be fair, ethical, and responsible. --------------- ✌️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 Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/ Connect with Amit on LinkedIn: https://www.linkedin.com/in/amitpaka/ Timestamps: [00:00] Thank you to Fiddler AI! [00:46] Introduction to Amit Paka [05:04] Amit's background in tech [09:55] EU Regulation [12:39] "The goal that the EU seems to be going for is they want to go for helping build human-centric and responsible AI."   [13:28] 4 AI Categories:               1. Unacceptable risk applications 2. High-risk applications 3. Limited risk applications 4. Minimal risk applications   [14:58] Deep dive into High-risk applications [17:28] Digital Services Act (DSA) and Digital Marketing Act (DMA) [19:02] Military   [19:33] "They don't know what they don't know and they probably wanted the door open."   [21:13] US on JIC Team - transparency and increasing trustworthiness on AI [23:06] Diversity of industries and Explainability   [24:22] "The urgent need for Explainability comes from verticals that are facing the problems today on the ground and cannot run their business." [30:09] Model Performance Management (MPM) [34:05] "When your model is facing issues, you now have to root-cause it within life." [35:40] Control Theory [36:10] "Control Theory means that you do not just measure it but you can influence it so you can actually keep it." [38:14] Abstraction into being useful [43:23] "You can train a model that accurately represents the reality." [44:00] Data scientist doing ML Flow [49:55] Amit's favorite surprise! [53:04] Banking and Insurance adoption of ML [55:48] Advise ML Scientists and Data Scientists in terms of Explainable AI [58:25] "Models are incredibly hard to debug. You're just training a model for high accuracy but you don't know how that accuracy is distributed." [59:49] Linking of EU Regulation and MPM

Monmayuri Ray, an MLOps expert from GitLab, shares her journey from applied mathematics to data science. She dives into the integration of MLOps with DevOps, discussing the need for modernization and collaboration. The conversation also covers the economics of AI impacting decision-making and the importance of balancing technical skills with business insights in AI initiatives. Mon highlights the delicate dance between empowering data scientists with tools while safeguarding against errors, advocating for community-driven best practices in the evolving landscape.

MLOps community meetup #64! Last Wednesday we talked to Christopher  Bergh, CEO, DataKitchen. //Abstract Working on a shared technically difficult problem there will be some things that are important no matter what industry you are in. Whether it's building cars in a factory, using agile or scrum methodology, or productionizing ML models you need a few basics. Chris gives us some of his best practices in the conversation. //Bio Chris Bergh is the CEO and Head Chef at DataKitchen. Chris has more than 25 years of research, software engineering, data analytics, and executive management experience. At various points in his career, he has been a COO, CTO, VP, and Director of Engineering. Chris is a recognized expert on DataOps. He is the co-author of the "DataOps Cookbook” and the “DataOps Manifesto,” and a speaker on DataOps at many industry conferences. //Takeaways Your model is not an island. For success, Data science requires a high level of technical collaboration with other parts of the data organization. //Other Links On-Demand Webinar - Your Model is Not an Island:  Operationalizing Machine Learning at Scale with ModelOps   https://info.datakitchen.io/watch-on-demand-webinar-operationalize-machine-learning-at-scale-with-modelops ----------- 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 Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Chris on LinkedIn: https://www.linkedin.com/in/chrisbergh/ Timestamps: [00:00] Introduction to Christopher Bergh [02:57] MLOps community in partnership with MLOps World Conference [04:34] Chris' Background [07:59] "When we started with the company, I realized that the problem I have is generalizable to everyone. I'm getting enough there in years and I wanted to remove the amount of pain that other people have." [09:53] DataOps vs MLOps [10:15] "I don't really honestly care what Ops you use, right? Hahaha! Call it your favorite Ops 'cause first of all as an engineer, I want precise definitions. I look at it from a completely odd-ball way so you could call it whatever Ops term you want." [12:45] Best practices of companies [14:16] "When that code runs in production, monitor and check to see if it's right. Absorb it, monitor it because the model could go out of tune. The data going into it could be wrong. The data transformation could break. Shit happens and don't trust your data providers." [19:00] The whole is still greater than its part [20:26] "It is harder to focus on the results than just under a piece of the task. Don't spend too much time on doing the wrong thing." [23:50] DevOps Principles and Agile [27:17] DataOps Manifesto - DataOps is Data Management reborn [27:45] "The 'Ops' term is ending up encompassing the work that you do in addition to the system you build to do the work." [30:45] Standardization   [32:22] "I think that there's a lack of perception of the need to spend time on doing the operations part of the equation." [34:15] Tools as lego blocks [34:49] "Good interphases make good neighbors." [36:23] "Standards can help but they're not the panacea." [36:30] Cultural side - You build it, you own it, you ship it [39:28] Value chain [44:19] Ripple effect of testing [48:03] Google on "One tool to rule them all" [49:50] "Legacy happens if you're gonna live in the real world and not start greenfield projects." [53:47] Starting MLOps in the legacy system