MLOps.community

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.Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠⁠⁠⁠⁠// AbstractWorking on a 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.// BioChris 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.// TakeawaysYour model is not an island. For success, Data science requires a high level of technical collaboration with other parts of the data organization.// Related LinksOn-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/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 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 on a piece of the task. Don't spend too much time 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

Coffee Sessions #40 with Srivatsan Srinivasan of AIEngineering, Scaling AI in Production.  Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠⁠⁠⁠// AbstractThis Coffee Session is a Collaboration with @AIEngineering. Srivatsan Srinivasan, the founder of the popular AI Engineering channel on YouTube and a senior partner at a major technology consultancy. Sri has a true passion for building ML systems, and his channel engages in some of the most detailed, thorough, and complete treatments of the MLOps topics we often discuss in the community. It's no surprise, then, that his coffee session with us went similarly!This conversation became pretty technical. We delved into details of how to set up CI/CD, how to set up the right kinds of tests, which cloud tools stand out to us on GCP, AWS, etc., and many other topics. Sri had a wealth of knowledge on all these fronts!// Bio20+ years of intense passion for building data-driven applications and products for top financial customers. Srivatsan has been a trusted advisor to a senior-level executive from business and technology, helping them with complex transformations in the data and analytics space. Srivatsan also runs a YouTube Channel (AIEngineering) where he talks about data, AI, and MLOps.// Related LinksAI and MLOps free courses - https://github.com/srivatsan88YouTube channel: bit.ly/AIEngineering--------------- ✌️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 Srivatsan on LinkedIn: https://www.linkedin.com/in/srivatsan-srinivasan-b8131b/Timestamps: [00:00] Introduction to Srivatsan Srinivasan [01:41] Background on YouTube AIEngineering [03:17] Tips on learning MLOps and starting with the field [06:00] "Focus on your key challenges, and that will drive the capability that you need to implement." [06:50] Tips on starting CI/CD [08:46] "Start with DevOps and see what additional capabilities you will require for the Machine Learning aspect of it." [09:24] Staying general in different environments [10:43] "Focus on the core concepts of it. The concepts are similar."   [12:10] Testing systems robustly[20:00] Trends within MLOps space [20:31] "Everybody can fail fast, but you need to fail smart because Machine Learning is a huge investment." [23:21] GCP Auto ML [26:54] Deployment [27:06] "It's not only the tools, but it's also the patterns." [29:34] Kubernetes perspective [31:21] Favorite model release strategy [36:22] Annotation, labeling, and concept of ground truth[38:10] Best practices in Architecture and systems design in the context of ML [41:29] "You learn a lot, at the same time the complexity also increases, so work with multiple teams in this process to learn it."  [42:35] "Your speed increases based on the way you envision your architecture." [42:55] Software engineering lifecycle vs machine learning development life cycle [44:55] Youtube experience [45:50] "My focus has always been from intermediate to experts." [46:24] Content creation [47:17] "You cannot do everything in MLOps at one stretch. You have to see what is critical for you." [47:23] "For me, continuous training is not that critical because I don't want to take the freedom out of the data scientists." [48:31] New contents planned [48:40] IoT and Edge Analytics - Predictive maintenance  [50:21] Wrap up

Coffee Sessions #39 with Stephen Galsworthy of Quby, MLOps: A leader's perspective.Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠⁠⁠// Abstract  Demetrios and Vishnu sit down with Stephen Goldsworthy, former Chief Data and Product Officer at Quby, to explore the evolving intersection of machine learning, organizational culture, and leadership. The discussion traces Stephen’s journey from data scientist to executive board member, highlighting how the toughest challenges in scaling ML aren’t technical—they’re organizational. He shares lessons from embedding data science into core product teams, aligning executives around AI literacy, and navigating the cultural transformation of merging a fast-moving tech company with a traditional utility. This episode unpacks how true MLOps maturity depends less on tools and more on communication, structure, and shared understanding across every level of the business.//Bio Dr. Stephen Galsworthy is a data leader skilled at building high-performing teams and passionate about developing data-powered products with lasting impact on users, businesses, and society.  Most recently, he was the Chief Data and Product Officer at Quby, an Amsterdam-based tech company offering data-driven energy services. He oversaw its transformation from a hardware-based business to a digital organization with data and AI at its core. He put in place a central cloud-based data infrastructure and unified analytics platform to collect and take advantage of petabytes of IoT data. His team deployed real-time monitoring and energy insight services for 500k homes across Europe.   Stephen has a Master’s degree and Ph.D. in Mathematics from Oxford University and has been leading data science teams since 2011.//Takeaways MLOps as a process, people, and technological problem.  Experiences from a team working at the forefront of data and AI.--------------- ✌️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 Stephen on LinkedIn: https://www.linkedin.com/in/galsworthy/Timestamps:[00:00] Introduction to Stephen Galsworthy[01:28] Stephen’s Background in Tech[03:53] ML at Scale and Production[05:28] Production Is Not Final[06:15] From Zero to One[07:35] Non-Technical Challenges in ML[09:13] Technology No Longer Stumbling Block[09:37] Maximizing Value from Teams[10:20] Focusing on Business Impact[10:30] Organizational View of MLOps[18:00] Importance of Labeled Data[20:43] Aligning with Stakeholders Effectively[21:05] Different Approaches for Stakeholders[25:34] Filtering Noise for Strategy[26:54] Stephen’s Role and Mandate[28:30] Beyond Traditional Data Leadership[31:37] MLOps Organizational Challenge Project[32:15] Lessons from First Projects[35:37] Speed Through Team Discipline[37:00] Processes Enable Smooth Operations[38:07] Communicating Effectively with Stakeholders[41:34] Transparency with Leadership Peers[43:25] Ensuring End-User Benefits[43:44] Sharing Success Inside and Out[46:06] Prioritizing High-Impact Problems[47:05] Simple Solutions Over Machine Learning

MLOps community meetup #63! Last Wednesday, we talked to Felipe Campos Penha, Senior Data Scientist, Cargill.Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠⁠// AbstractCan one learn anything useful by creating content online? The usual answer is a sounding YES. But what about live coding an MLOps project on Twitch? Can anything good come out of it?//BioFelipe Penha creates content about Data Science regularly on the Data Science Bits channel on YouTube and Twitch. He has 8+ years of experience with hands-on data-related work, starting with his doctorate in Astroparticle Physics. His career in the private sector has been devoted to bringing value to various segments of the Food and Beverages Industry through the use of Analytics and Machine Learning.----------- 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 Felipe on LinkedIn: https://www.linkedin.com/in/fcpenha/Timestamps:[00:00] Introduction to Felipe Campos Penha[01:30] Felipe's background[05:36] Developing models in physics vs developing models for companies[08:07] Felipe's transition from Jupyter Notebook to Operational ML[09:34] "The thought of business basically for customers, they always wanted to see the value and try to roll out more manual work like spreadsheets so they could try out that model in the field."[12:07] Felipe's software engineering development learning[14:10] Catalyst on YouTube and Twitch[18:06] Elements of Twitch[20:02] Non-polished versions of Twitch[21:16] "Twitch was not made for coding, it was for gamers."[26:17] Felipe's audience impact on Twitch[28:02] Logistical pieces[30:43] Words of wisdom on live streaming[30:56] "Don't be afraid to start. There are many streamers who are actually learning from scratch, and they are showing the process of learning online. They are learning faster because the help is faster."[33:16] Blog post as another means to Twitch[33:50] "I'm a perfectionist when I'm writing. The shorter it is, the harder it could get. You want to polish it a lot to make nice figures. I learned a lot, but for me, I feel that process is too slow because you're thinking about one subject for a long time, trying to polish it, while in live streaming, it's very dynamic and fast."[34:25] Twitch affecting Felipe's career[36:36] "Exposing yourself, showing your mistakes, vocalizing your thoughts, I think all of this makes you a better programmer."  [37:12] Getting through a problem[39:41] Recommended streamers that caught Felipe's interest[41:00] Community aspect and importance of Twitch[42:42] Role of community on Twitch[45:16] "Twitch is becoming such a trend that even companies are following."

MLOps community meetup #62! Last Wednesday, we talked to Oguzhan Gencoglu, Co-founder & Head of AI, Top Data Science.Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠// AbstractStarting the AI adoption with AI Proof-of-Concepts (PoCs) is the most common choice for most companies. Yet, a significant percentage of AI PoCs do not make it into production, whether they were successful or not. Furthermore, running yet another AI PoC follows the law of diminishing returns in various aspects. This talk will revolve around this theme.// BioOguzhan "Ouz" Gencoglu is the Co-founder and Head of AI at Top Data Science, a Helsinki-based AI consultancy. With his team, he delivered more than 70 machine learning solutions in numerous industries for the past 5 years. Before that, he used to conduct machine learning research in several countries, including the USA, the Czech Republic, Turkey, Denmark, and Finland. Oguzhan has given more than 40 talks on machine learning to audiences of various backgrounds.----------- 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 Oguzhan on LinkedIn: https://www.linkedin.com/in/ogencoglu/Timestamps:[00:00] Introduction to Oguzhan Gencoglu[00:47] Ouz's background[01:47] Recurring/repetitive problem patterns[03:16] "When you solve a repetitive task in an automatic way, that's Scalability."[04:32] Evolution expected of Machine Learning[05:10] "People are quite confused about the titles and what's worse, those titles don't have a common definition in different companies. If you feel a little bit overwhelmed, that's normal."[08:04] Proof-of-Concepts[10:35] Successful PoCs but not Productionized[16:03] Productionize as soon as possible[16:47] "In your Proof-of-Concepts, it's not only technical, but it's also a mindset."[20:00] Framework of a successful PoCs[24:28] Taking too much on PoCs[28:05] Proof-of-Concepts after Proof-of-Concepts and Proof-of-Concepts hell[31:30] Wholistic view[34:00] Operationalizing PoCs[37:17] "The teams also need to adjust themselves to these new tools, new paradigms, and the different needs of the whole industry."  [37:26] Horror stories[39:54] Open communication tips43:31] "Open communication should not only be from the technical perspective but also down to the business and strategy perspective."[44:20] Translation tips[44:39] "I believe the most crucial part of today's ML scientists' role is not building a machine learning model but translating a real-life problem into a machine learning problem. It's crucial because it's a scarce talent and skill."[49:30] Realistic budget for small PoCs[50:18] "You need at least 1 month of work of proof of value, but that doesn't mean things will go to production."[51:40] Understanding the questions fully[52:55] "That translation skill is the greatest skill to have in this industry because you can't auto ML that or whatever. It stands the test of time because that will be needed all the time."

Coffee Sessions #38 with Adam Sroka of Origami Energy, Organisational Challenges of MLOps.Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter// AbstractDeploying data science solutions into production is challenging for both small and large organizations. From platform and tooling wars to architecture and design pattern trade-offs, it can get overwhelming for inexperienced teams. Furthermore, many organizations will only go through the painful discovery process once. Adam will share some of his experiences from consulting and leading data teams to successfully deploying machine learning solutions, highlighting some of the more difficult challenges to overcome. You might not be surprised to hear it’s not all down to the tech.// BioDr. Adam Sroka, Head of Machine Learning Engineering at Origami Energy, is an experienced data and AI leader helping organizations unlock value from data by delivering enterprise-scale solutions and building high-performing data and analytics teams from the ground up. Adam shares his thoughts and ideas through public speaking, tech community events, on his blog, and in his podcast.--------------- ✌️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 Adam on LinkedIn: https://www.linkedin.com/in/aesroka/Timestamps: [00:00] Introduction to Adam Sroka [01:53] Adam's background in tech [08:06] 2 blog posts of Adam: Why So Many Data Scientists Quit Good Jobs at Great Companies and Why You Shouldn’t Hire More Data Scientists [08:31] High turn rate Adam has in the data science role[13:50] Avoiding hiring talents with deficits and coaching people[16:05] "I can't teach you to care about the standard of your core of what you're doing. It's quite hard to teach people charisma. Everything else, you pick up." [16:45] Resume-driven development, the idea of not playing the game, and politics in the workplace. [17:57] "You have to realize, other people don't have the same experience in the context that you do."  [19:59] Exit, Voice, Loyalty, and Neglect Model[22:35] You probably don't need a data scientist [23:40] "Data scientists can do everything slower and more expensively than everyone else, but they can do everything, and that's the important bit." [27:54] "My success is just driven by who I am as much as what I can do." Vishnu [28:24] Being Candor[30:37] Disconnect between the senior stakeholders and data scientists [32:30] "Before you come out to bring someone in an expensive talent search, engage with the consultancy. Do a four-week PRC, get them to tell you like." [34:18] Educational experiences as a consultant[37:35] Adam's journey into MLOps, productionize ML models when you are a data scientist, and tips[43:16] "Beginners can help beginners. Your perspective is really important. The value is not in the content. The value is in your perspective of the content." [45:21] Educating clients on uncertainty [48:34] Decision-making process [52:32] Organizational problems [53:43] "All models are wrong, but some are useful." George Box

MLOps community meetup #61! Last Wednesday, we talked to Lex Beattie, Michael Munn, and Mike Moran.Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠⁠⁠⁠// AbstractWe started out talking about some of the main bottlenecks they have encountered over the years of trying to push data products into production environments. Then things started to heat up as we dove into the topic of monitoring ML, and inevitably, the word explainability started being thrown around.Turns out Lex is currently doing a Ph.D. on the subject, so there was much to talk about. We had to ask if explainability is now table-stakes when it comes to monitoring solutions on the market? The short answer from the team. Yes!  Please excuse the bit of sound trouble we had with Google Mike at the beginning.// BioLex Beattie - ML Engagement Lead, SpotifyIn the last year, Lex has helped over 40 different teams across Spotify understand ML best practices, productionize ML workflows, and implement impactful ML in their products. Lex is also a Ph.D. candidate at the University of Oklahoma, focusing on feature importance and interpretability in deep neural networks. Beyond her passion for all things ML, she enjoys exploring the great outdoors in Montana with her German Wirehaired Pointer, Bridger.Michael Munn - ML Solutions Engineer, GoogleMichael is an ML Solutions Engineer with Google Cloud and Google's Advanced Solutions Lab. In his role, he works with customers to build and deploy end-to-end ML solutions with Google Cloud. Within the Advanced Solutions Lab, he teaches these skills to customers.Mike Moran - Principal Engineer, SkyscannerMike has worked across many dimensions; in large/tiny companies, back-end/front-end, with many languages, and as a sys-admin /engineer/manager. Mike has a healthy skepticism for most things and likes solving problems through applying systems thinking.Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/Connect with Lex on LinkedIn: https://www.linkedin.com/in/lexbeattie/Connect with Michael on LinkedIn: https://www.linkedin.com/in/munnm/Connect with Mike on LinkedIn: https://www.linkedin.com/in/mrmikemoran/Timestamps:[00:00] Introduction to Lex, Michael, & Mike[02:46] Common roadblocks[05:25] Consolidating knowledge[07:02] Bottlenecks on failures[09:58] Don't go on a detour[12:22] Bringing on complexity signs[19:33] Explainable AI[21:34] "There are different ways to approach Explainable AI. It starts to get more complicated when you start working with more complicated models." Lex[24:43] "If there are a lot of disparate sources out there about Explainability, I'd found myself hunting down various resources to simplify it for customers I'd worked with." Michael[26:46] "Being clear about who you're explaining it to because in our context, sometimes the organization needs to explain it to a regulator." Mike [28:04] Monitoring solution[31:00] ML Canvas[33:24] Explainable AI Resources[34:48] Explainable Predictions by Michael[36:48] Purpose of Explainable Model[39:40] Work in the same language[42:46] Use of War Stories[49:11] Hot seat![49:15] Mike - Skyscanner pricing[50:30] Lex - Spotify recommendation sudden stop[51:35] Michael - NLP models on emails

Coffee Sessions #37 with Ariel Biller of ClearML, MLOps Memes.Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠⁠⁠⁠// AbstractThe Meme king of MLOps joins us to talk about why we need more MLOps memes and how he got so damn good at being able to zoom out and see things from a metta level, then make a meme about it!// BioA researcher first, developer second, in the last 5 years, Ariel worked on various projects from the realms of quantum chemistry, massively parallel supercomputing, and deep-learning computer vision. With AllegroAi, he helped build an open-source R&D platform (Allegro Trains), and later went on to lead a data-first transition for a revolutionary nanochemistry startup (StoreDot). Answering his calling to spread the word on state-of-the-art research best practices, He recently took up the mantle of Evangelist at ClearML. Ariel received his Ph.D. in Chemistry in 2014 from the Weizmann Institute of Science. With a broad experience in computational research, he made the transition to the bustling startup scene of Tel-Aviv and to cutting-edge Deep Learning research.--------------- ✌️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 Ariel on LinkedIn: https://www.linkedin.com/in/LSTMeow/ // Related Linkshttps://youtu.be/1C_l5ICJlEohttps://youtu.be/yTtTrwXEhN4https://youtu.be/F4Ghp-phFuITimestamps:[00:00] Introduction to Ariel Biller [01:20] Ariel's background [03:40] Story behind Memeing [06:36] "Memes can be as extreme as you want because people don't know if they're going to take you seriously or you're joking." [07:21] MLOps memes and more [10:15] MLOps fear[13:00] MLOps is being more complicated than DevOps. [13:10] "A meme material is a social commentary about what there is and what there is now." [16:00] Standardization [18:18] "Would we have MLOps' code in a sweeping way or not?" [18:26] "I'm not sure as a community of builders, we have the right perspective that will work for all the cases." [20:26] Journey into evangelism[26:45] "Feature stores are a big meme." [27:08] "Memeing is like a muscle. If you flex it daily, it creates tensions."[31:26] We need to de-jargonize MLOps and ML engineering[35:55] Current Israeli tech scene [39:16] "The deficit is that there's a limited number of people doing MLOps right now."[43:14] Tooling space [46:57] "Concentrate on the basic stuff that will survive forever, and if you need to reach out for a tool, don't reach out for a tool, reach out for obstruction."[51:47] Standardization of ID Tree [52:43] "Everybody is doing whatever they want because it works for them. Someday, someone will come out with some good obstruction and a good toolchain that works across the board that will click for everyone and will use it from that time on." [55:20] Ecosystem support

Coffee Sessions #36 with Luigi Patruno of 2U, Luigi in Production Part 2.   Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠⁠⁠// AbstractLearning Voraciously: We talk a lot in the community about how to learn and upskill in an efficient way. Luigi provided great insight into how he applies certain principles to his learning practices. One tip he shared is to rigorously read and digest books. Luigi himself has used books to address his knowledge gaps in areas like product, finance, etc. I appreciated the emphasis on books. A lot of the reason we feel inundated by new learning resources is that they are online. Emphasizing books, which are often far higher-quality than blog posts, can slow things down and focus our learning.  Leadership Patience: Lately, Luigi has been spending more time managing projects and the data science team at 2U. He shared a lot of his insights into how to manage data science and machine learning properly. One of the most important things he emphasized to us was his patient attitude towards solving problems important to leadership. Turning around organizations is hard work. It's slow, it takes energy, and it is a nonlinear process. As he has course-corrected at various times as a data science leader, Luigi has brought admirable patience to the task, which has helped him be more successful on the things that matter to the entire company.  Communication Flows: It's easy to imagine Luigi as a great communicator, given his experience running MLInProduction.com. In our conversation, he showed us how he puts it to use in his management style. Luigi shared the importance of understanding how communication flows across an organization. Being aware of this is crucial to working on the right, most impactful things. Having a pulse on what different groups and leaders are thinking about can help you evaluate your impact as a team.// BioLuigi Patruno is a Data Scientist focused on helping companies utilize machine learning to create competitive advantages for their business. As the Director of Data Science at 2U, Luigi leads the development of machine learning models and MLOps infrastructure for predicting student success outcomes across 2U’s portfolio of university partners. As the Founder of MLinProduction.com, Luigi creates and curates content to educate machine learning practitioners about best practices for running resilient machine learning systems in production. Luigi has consulted on data science and machine learning at Fortune 500 companies and start-ups and has taught graduate-level courses in Statistics and Big Data Engineering. He has an M.S. in Computer Science and a B.S. in Mathematics.--------------- ✌️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 Luigi on LinkedIn: https://www.linkedin.com/in/luigipatruno91/Timestamps: [00:00] Introduction to Luigi Patruno [01:12] Update about Luigi [04:08] Luigi's transition [07:18] Problem-focused[11:00] New problem [12:51] Rational platform strategy[18:18] Bringing the learnings to the team[20:57] Formulating and communicating vision[25:40] Problem-driven mindset[35:53] Organizational blind spots[41:12] Continuous learning  [42:46] "Default to reading."[44:44] The Lindy effect[46:20] "You'll fail less often on the easy problems."  [46:25] Act upon reading [51:48] Ethical implications of ML [53:24] Wrap up