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Coffee Sessions #45 with Diego Oppenheimer of Algorithmia, Enterprise Security and Governance MLOps.Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠// AbstractMLOps in the enterprise is difficult due to security and compliance. In this MLOps Coffee Session, the CEO of Algorithmia, Diego, talks to us about how we can better approach MLOps within the enterprise. This is an introduction to essential principles of security in MLOps and why it is crucial to be aware of security best practices as an ML professional.// BioDiego Oppenheimer is co-founder and CEO of Algorithmia. Previously, he designed, managed, and shipped some of Microsoft’s most used data analysis products, including Excel, Power Pivot, SQL Server, and Power BI. He holds a Bachelor’s degree in Information Systems and a Master’s degree in Business Intelligence and Data Analytics from Carnegie Mellon University.--------------- ✌️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/registerConnect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/Connect with Diego on LinkedIn: https://www.linkedin.com/in/diego/Timestamps: [00:00] Thank you, Diego and Algorithmia, for sponsoring this session! [01:04] Introduction to Diego Oppenheimer [02:55] Security [04:42] "The level of scrutiny for apps and development and that of the operational software is much higher." [07:40] "We take the Ops part of MLOps very, very seriously, and it's really about the operational side of the equation." [09:22] MLSecOps [11:42] "The code doesn't change, but things change cause the data changed." [15:23] Maturity of security [18:45] "To a certain degree, we have general parameters of software DevOps in software engineering and DevOps, and we're adapting it to this new world of ML."  [19:03] Development workflow [20:58] "In the ideal world, you're just sitting in your data science platform, your auto ML platform, whatever it is that you're working with, you can push a model." [22:50] Security, responsibility, and authentication [23:38] "What you don't want to learn is how to do automation every single time there's a new use case. That's just not a good use of your time."  [24:30] Hurdles needed to be cleared [24:47] "I would argue that there's no such thing as Bulletproof in software. That doesn't exist. It never has and never will." [26:25] Machine Learning security risks                         1. Operational risk           2. Brand risk           3. Strategic risk[28:23] Machine Learning security risk standards [31:11] "There's a world where you can reverse engineer a model by essentially feeding a whole bunch of data and understanding where that comes back."[33:55] How to change the mindset of relaxed companies when it comes to security [35:19] "It takes time and money to figure out security."[37:52] Conscientious when building systems [39:44] "Look at the end result of the workflow and understand the value of that workflow, which you should know at that point because if you're going into an ML workflow without understanding what the end value is going to be, it's not a good sign." [40:19] Root cause analysis [41:00] Threat modeling [41:14] "There's a natural next step where there's threat modeling for ML systems, and it's a task that gets built and understood, and nobody's going to enjoy doing it."  [43:07] Security as code[45:29] MLRE

Coffee Sessions #44 with Grant Wright of SEEK Ltd., Autonomy vs. Alignment: Scaling AI Teams to Deliver Value.Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter// 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 with the organization's 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/slackFollow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/registerConnect 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/[00:00] Introduction to Grant Wright[01:49] Grant’s Journey into Tech[03:57] Managing Many Data Scientists[04:50] Challenges Managing Top Talent[05:00] Collaborating with Data Scientists[06:00] Driving Cross-Functional Collaboration Effectively[06:19] Journey to Growing the Team[10:13] Handling Model Drift Autonomously[11:20] Core Use Cases at Seek[14:41] Transition Period at Seek[16:12] Organizational Models Have Trade-Offs[16:58] Motivation Through Delivered Value[17:08] No Clear Blueprint[19:50] Seek’s War Story[20:26] Lessons Learned and Reflections[22:52] Interfaces and Accountability[24:32] Partners, Not Customers Philosophy[26:22] Building Team Self-Sufficiency[27:34] Cross-Team Collaboration Encouragement[28:28] Avoiding Duplicate Team Efforts[29:24] Balancing Speed and Oversight[30:18] Seek’s Priorities This Year[31:05] Balancing Commonality and Flexibility[32:04] Investing in Platforms[33:07] Avoiding Half-Used Solutions[33:35] Unified Systems vs. Team Autonomy[34:27] Focus on Organization Structure[37:16] Streamlining to One Approach[38:17] Successful Business Strategies[40:23] Gaining Executive Understanding[41:45] Estimating Machine Learning Projects[43:30] Challenging False Assumptions[44:00] Balancing Speed and Quality[45:55] Thin Slice Approach[47:30] Defining Tight Success Boundaries

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.Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter// AbstractThe 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).// BioEmmanuel 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 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/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerConnect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/Connect with Emmanuel on LinkedIn: https://www.linkedin.com/in/emmanuelraj7/// Related Links:  www.emmanuelraj.comhttps://www.youtube.com/watch?v=m32k9jcY4pYhttps://www.youtube.com/watch?v=1sGECHbc9zg[00:00] Introduction to Emmanuel Raj[03:37] Emmanuel’s Background in Tech[04:18] Software Beyond Deterministic Rules[05:48] Common Machine Learning Failures[09:07] Real-World Testing Importance[10:10] Onyx vs. Jupyter for Production[12:13] Keep Solutions Lean and Simple[12:20] Building Robust CI/CD Pipelines[14:58] Monitoring the ML Lifecycle[15:48] Developing AIOps Systems[16:20] AIOps in CI/CD Pipelines[19:33] Starting MLOps Capability Building[22:00] Company Legacy Considerations[24:47] Optimal Solutions Over Tools[26:47] Open-Source Tools Discussion[29:08] Security as a Roadblock[31:00] What’s Next for MLOps[32:08] Three Core MLOps Blocks[34:40] MLflow Live Coding Highlights[38:00] FastAPI Microservice Overview[40:28] FastAPI Terminal Demo[40:40] Local Testing with Locust[40:56] Running Image and Container[41:10] Predict Endpoint Post Request[41:54] Load Testing Process[43:47] Running Locust on Server[44:38] Specifying the Endpoint[45:20] Starting Locust Tests[47:40] Pushing to Production[48:15] Automating the Workflow[50:00] Engineering MLOps Release Announcement

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.⁠⁠⁠⁠⁠⁠⁠⁠⁠// AbstractBuilding, designing, or even just casting the vision for MLOps for your company, whether a large corporation or an agile start-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.// BioVeselina Staneva - Co-founder & Head of Product, TeachableHubOver the past few years, Vesi has worked 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 on 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 participating in non-professional triathlons and mountain bike ultra races.Simarpal Khaira - Senior Product Manager, IntuitSimarpal 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 an 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/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerConnect 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.Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠// AbstractThe 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.// BioKyle 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 led 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/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerConnect 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/// TakeawaysData 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 governanceOrganizations that poorly define data science often overburden their data scientists, but there are expectations that data scientists know some engineeringOperationalizing 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=1sTimestamps:[00:00] Introduction to Kyle Gallatin[01:00] Kyle’s Path into Tech[02:04] Data Analyst to Engineer[03:45] Reflections on Learning CS[04:04] SAS App with ML Services[05:13] Python’s Strength in Machine Learning[06:43] Working Effectively with YAML[07:10] Choosing Technologies and Plug-ins[08:43] Take the Easy Way[09:00] Favorite Plug-ins Overview[09:07] VS Code Remote SSH[09:44] Future of Machine Learning[11:12] MLOps Growth and Buzzword Status[12:08] Exploring Heuristics and Next Steps[15:19] Navigating Unknown ML Territory[15:33] Monitoring and Observability Practices[16:21] Specialized and Customized Solutions[17:43] Balancing Commonality and Specificity[17:54] Integrations Across ML Systems[20:00] Measuring Time to Production[20:22] Data Scientists’ Team Fit[21:34] One Size Doesn’t Fit[22:40] Building Depends on People[23:40] Defining Data Science Roles[24:00] Platform Engineering Perspective[25:00] Optimizing Model Serving Value[25:21] Model Serving Platforms[27:13] Importance of Standardization[29:00] Exercising Good Judgment[29:57] Breaking Work into Pieces[30:30] Data Access Regulations[33:32] Technical Standpoint Discussion[34:37] Defining Use Cases Clearly[36:04] Next Big Thing: MLOps[37:50] Modern Scaling Approaches[38:46] Nontechnical Companies Stepping Up[41:18] Defining Core Problems[42:38] Considering Value and Needs

MLOps community meetup #66! Last Wednesday, we talked to Alfredo Deza, Author and Speaker.Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠⁠⁠⁠⁠⁠⁠⁠// AbstractIn 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 are 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!// BioAlfredo 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/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerConnect 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] Interruption 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.Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠⁠⁠⁠⁠⁠⁠⁠// AbstractIn this MLOps Meetup, we talked about the Machine Learning model lifecycle and development stages, and then analyzed the main mistakes that everybody makes at each stage. Kseniia also provided the audience with solutions to the mistakes, and we discussed existing tools for experiment management.// BioKseniia 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/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerConnect 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 Experiment 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 [34:18] Find Your Mistakes  [35:35] Audio Data [41:38] "I prefer reproducibility tools because it's automatic, and it also takes a lot of time to manually upload the results into the 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 with 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 take all the principles of the 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."  [49:50] Calculating the lost money

Coffee Sessions #42 with Amit Paka of Fiddler AI, Model Performance Monitoring.Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠⁠⁠⁠⁠⁠⁠// AbstractMachine 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 friction in debugging 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.// BioAmit 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 a passion 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/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerConnect 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 reality." [44:00] Data scientist doing ML Flow [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.