Coffee Sessions #24 with Sara Robinson of Google, Machine Learning Design Patterns co-hosted by Vishnu Rachakonda. //Bio Sara is a Developer Advocate for Google Cloud, focusing on machine learning. She inspires developers and data scientists to integrate ML into their applications through demos, online content, and events. Before Google, she was a Developer Advocate on the Firebase team. Sara has a Bachelor’s degree from Brandeis University. When she’s not writing code, she can be found on a spin bike or eating frosting. --------------- ✌️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 David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/ Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/ Connect with Sara on LinkedIn: https://www.linkedin.com/in/sara-robinson-40377924/ Timestamps: [00:00] Introduction to Sara Robinson   [01:38] Sara's Background into tech [04:54] What were some things that jumped out at you right away with Machine Learning that is different? [07:44] Sara's Transition to the Machine Learning realm. [08:36] What is the role of a Developer Advocate? [11:41] Compared to traditional software developer advocacy, what stands out to you as being different, unique, perhaps more fun about working in the Machine Learning realm as a Developer Advocate? [13:40] "No one person has it right." [15:27] Given how new this space is, how did you go about writing a book? What leads you to write this book (Machine Learning Design Patterns)?  [19:00] Process of deciding to write the book [21:46] What is it that made the focus of these design patterns? [25:07] Who's the reader that you think who's gonna have this book on their shelf as a reference? [26:42] How would you advise readers to go about reconciling this domain-based needs and the design patterns that you may suggest or identify? [31:20] Can you tell us about a time that some of the design patterns as you're learning with your co-authors has been useful to you? [36:50] Workflow Pipeline breakdown in the book [42:23] How do you think about that level of maturity in terms of thinking about the design patterns? [46:06] How do I communicate in design pattern? What if there is resistance to formalization or implementational structure because it might prevent creativity or reiteration? [49:32] Pre-bill and custom components of Pipeline Frameworks [51:28] How do we know to do the next step or stay in Feature Store patterns? [56:07] Are we going to see the convergence of tools and frameworks soon? Resources referenced in this episode: https://www.oreilly.com/library/view/machine-learning-design/9781098115777/ https://www.amazon.com/Machine-Learning-Design-Patterns-Preparation/dp/1098115783 https://books.google.com.ph/books/about/Machine_Learning_Design_Patterns.html?id=djwDEAAAQBAJ&redir_esc=y https://amzn.to/38tM22C https://sararobinson.dev/2020/11/17/writing-a-technical-book.html

Coffee Sessions #23 with Todd Underwood of Google, Followups from OPML Talks on ML Pipeline Reliability co-hosted by Vishnu Rachakonda. //Bio Todd is a Director at Google and leads Machine Learning for Site Reliability Engineering Director. He is also Site Lead for Google’s Pittsburgh office. ML SRE teams build and scale internal and external ML services and are critical to almost every Product Area at Google. Before working at Google, Todd held a variety of roles at Renesys.  He was in charge of operations, security, and peering for Renesys’s Internet intelligence services that are now part of Oracle's Cloud service. He also did product work for some early social products that Renesys worked on. Before that Todd was Chief Technology Officer of Oso Grande, an independent Internet service provider (AS2901) in New Mexico. //Other links referenced by Todd: --------------- ✌️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 Todd on LinkedIn: https://www.linkedin.com/in/toddunder/ Timestamps: [00:00] Intro to Todd Underwood [02:04] Todd's background [08:54] What's kind of vision do you "paint"? [14:54] Playing a little bit "devil's advocate." Do you think that's even possible? [19:36] "Start serving to make sure of having the possibility to get it out." How do you feel about that? [23:56] What advise could you give to other people who wanted to bring in ML professionals into their companies to make ML useful for them? [29:53] Is it useful to use these new models?   [32:25] Do you feel like there would be a point where there would be a standard procedure? [35:50] How machine learning breaks [40:44] As an engineering leader, what's your advice to other engineering leaders in terms of how to make that reflection on your team needs and failures...?   [48:42] It's the design that you're looking at as the problem, not the person. [56:27] Do we think that people sold a bunch of stuff and now we were left with the results?      [1:00:46] Recommendations on readings, things to do to better hone our craft. [1:03:35] The more you explore, the more you realize, what's going on? Where can I learn from? [1:05:00] Since you are in the mode of predicting things and philosophical background, where are you seeing the industry going in the next 5 years as we create it? Resources referenced in this episode: https://www.youtube.com/watch?v=Nl6AmAL3i08&feature=emb_title&ab_channel=USENIX https://www.youtube.com/watch?v=hBMHohkRgAA&ab_channel=USENIX https://youtu.be/0sAyemr6lzQ https://youtu.be/EyLGKmPAZLY https://www.usenix.org/conference/opml20/presentation/papasian https://www.usenix.org/system/files/login/articles/02_underwood.pdf https://storage.googleapis.com/pub-tools-public-publication-data/pdf/da63c5f4432525bcaedcebeb50a98a9b7791bbd2.pdf

MLOps community meetup #45! Last Wednesday, we talked to Joe Reis, CEO/Co-Founder of Ternary Data. // Abstract: The fact is that most companies are barely doing BI, let alone AI. Joe discussed ways for companies to build a solid data foundation so they can succeed with machine learning. This meetup covers the continuum from cloud data warehousing to MLOps. // Bio: Joe is a Data Engineer and Architect, Recovering Data Scientist, 20 years in the data game.  Joe enjoys helping companies make sense of their culture, processes, and architecture so they can go from dreaming to doing. He’s certified in both AWS and Google Cloud. When not working, you can find Joe at one of the two groups he co-founded—The Utah Data Engineering Meetup and SLC Python. Joe also sits on the board of Utah Python, a non-profit dedicated to advocating Python in Utah. // Other links to check on Joe: https://www.youtube.com/channel/UC3H60XHMp6BrUzR5eUZDyZg https://josephreis.com/ https://www.ternarydata.com/ https://www.linkedin.com/pulse/what-recovering-data-scientist-joe-reis/ https://www.linkedin.com/pulse/should-you-get-tech-certification-depends-joe-reis/ ----------- 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 Joe on LinkedIn: https://www.linkedin.com/in/josephreis/   Timestamps: [00:23] How did you get into tech? What brought you on to the journey into data? [04:50] You got into the auto ML and you decided to branch out and do your own thing? How did that happen? [08:18] What is it with BI and then making that jump to ML? [11:00] How have you seen Machine Learning fall flat with trying to shoehorn Machine Learning on top of the already weak foundation of BI? [13:45] Let's imagine we're doing BI fairly well and now we want to jump to Machine Learning. Do we have to go out and reinvent the whole stack or can we shoehorn it on? [15:36] How do you move from BI to ML? [18:24] What do you mean by realtime?   [20:35] Managed Services in DevOps [23:30] The maturity isn't there yet [26:03] Where would you draw the line between BI and AI? [30:45] What are the things is Machine Learning an overkill for? [33:43] Are you thinking about what data sets to collect and how different do those vary? [35:18] "Software Engineering and Data Engineering are basically going to merge into one." [38:27] What do you usually recommend moving from BI to AI? [40:45] What is "strong data foundation" in your eyes? [42:47] "MLFlow to gateway drug." What's your take on it?   [46:25] In this pandemic, how easy is it for you to pivot to a new provider? [49:10] Vision of companies starts coming together on different parts of the stack in the Machine Learning tools.

Coffee Sessions #22 with Carl Steinbach of LinkedIn, Deep in the Heart of Data. //Bio Carl is a Senior Staff Software Engineer and currently the Tech Lead for LinkedIn's Grid Development Team. He is a contributor to Emerging Architectures for Modern Data Infrastructure //Other links referenced by Carl: https://rise.cs.berkeley.edu/wp-content/uploads/2017/03/CIDR17.pdf https://www.youtube.com/watch?v=-xIai_FvcSk&ab_channel=WePayEngineering https://softwareengineeringdaily.com/2019/10/23/linkedin-data-platform-with-carl-steinbach/ https://www.slideshare.net/linkedin/carl-steinbach-open-source https://dreamsongs.com/RiseOfWorseIsBetter.html https://engineering.linkedin.com/blog/2017/03/a-checkup-with-dr--elephant--one-year-later https://engineering.linkedin.com/ https://engineering.linkedin.com/blog/2018/11/using-translatable-portable-UDFs https://a16z.com/2020/10/15/the-emerging-architectures-for-modern-data-infrastructure/ --------------- ✌️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 David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/ Connect with Carl on LinkedIn: https://www.linkedin.com/in/carlsteinbach/ Timestamps: [00:00] Introduction to Carl Steinbach [00:44] Carl's background [04:51] Breakdown of Transpiler [10:55] Advantages of Decoupling the Execution Layer [15:25] Differences between UDF (user-defined function) Functions and Views [18:45] How do you ensure the reproducibility of these Views? [23:58] Data structure evolution [27:55] Are Data Lakes and Data Warehouse fundamentally different things or are they on a path towards conversion? [33:37] It's inevitable that people will start doing machine learning on databases [36:01] Who gets permission on what, especially when it comes to data and how sensitive things can be? [41:27] Security aspect of data   [43:40] Does it require a level of obstruction on top of the data of the file system? [45:48] Why do we go back and go forward which sets this trend?

ML and Encryption - It's all about secure insights #7! In this episode, we've invited Théo Ryffel, Founder of Arkhn and founding member of the Open-Mined community.  // Abstract: In this episode,  Théo introduces us to the concept of encrypted Machine Learning, when and the best practices to have it applied in the development of Machine Learning based solutions, and the challenges of building a community.  //Other links to check on Théo: https://twitter.com/theoryffel https://arkhn.com https://openmined.org https://arxiv.org/pdf/1811.04017.pdf https://arxiv.org/pdf/1905.10214.pdf //Final thoughts Feel free to drop some questions into our slack channel (https://go.mlops.community/slack)  Watch some of the other podcast episodes and old meetups on the channel: https://www.youtube.com/channel/UCG6qpjVnBTTT8wLGBygANOQ ----------- 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 Fabiana on LinkedIn: https://www.linkedin.com/in/fabiana-clemente/ Connect with Théo on LinkedIn: https://www.linkedin.com/in/theo-ryffel

**Privacy-preserving ML with Differential Privacy** Differential privacy is without a question one of the most innovative concepts that came around in the last decades, with a variety of different applications even when it comes to Machine Learning. Many are organizations already leveraging this technology to access and make sense of their most sensitive data, but what is it? How does it work? And how can we leverage it the most? To explain this and provide us a brief intro on Differential Privacy, I've invited Christos Dimitrakakis. Professor at University, counts already with multiple publications (more than 1000!!!) in the areas of Machine Learning, Reinforcement Learning, and Privacy. Useful links: Christos Dimitrakakis list of publications Differential privacy for Bayesian inference through posterior sampling Authors: Christos Dimitrakakis, Blaine Nelson, Zuhe Zhang, Aikaterini Mitrokotsa, Benjamin IP Rubinstein Differential privacy use cases Open-source differential privacy projects Open-source project for Differential Privacy in SQL databases

MLOps community meetup #44! Last Wednesday, we talked to Savin Goyal, Tech lead for the ML Infra team at Netflix. // Abstract: In this conversation, Savin talked about some of the challenges encountered and choices made by the Netflix ML Infrastructure team while developing tooling for data scientists. // Bio: Savin is an engineer on the ML Infrastructure team at Netflix. He focuses on building generalizable infrastructure to accelerate the impact of data science at Netflix. // Other links to check on Savin: https://www.usenix.org/conference/opml20/presentation/cepoi https://www.youtube.com/watch?v=lakPlz8GJcA&ab_channel=RConsortium https://www.youtube.com/watch?v=-oMZAS9qfrE&ab_channel=AnalyticsIndiaMagazine https://www.youtube.com/watch?v=yyWirT279tY&ab_channel=FunctionalTV https://www.youtube.com/watch?v=QkRJ24Q0E-k&ab_channel=Matroid ----------- 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 Savin on LinkedIn: https://www.linkedin.com/in/savingoyal/ Timestamps: [00:00] Background of Savin Goyal [02:41] Breakdown of Metaflow [05:44] In the stack, where does Metaflow stand? [13:23] Where does Metaflow start in Runway Project? [15:27] What tools or storage does Netflix use for DataOps, ie: the front-end management of data sets and how does that integrate with Metaflow? [18:56] Recommender Systems: Can you explain the other areas that you're using Machine Learning? [22:27] What do you feel is the hardest part of building an operating  Machine Learning workflow? [28:45] 3 Pillars: Reproducibility, Scalability, Usability. [36:05] You give so much power to people. How do you keep them from going overboard? [37:47] Can you explain this Pillar of Usability? [41:09] Road-based access control has been coming up a lot recently. Does Metaflow do something specific for that? [44:49] What are some learnings that come across that you didn't have since you open-sourced when you were working at Netflix? [48:10] What kind of trends you have been seeing? Where do you feel like the market is going? [50:33] Have you seen some companies really interested in Metaflow? How have you been seeing them combine other tools that are out there?

Coffee Sessions #21 with Benjamin Rogojan of Seattle Data Guy, A Conversation with Seattle Data Guy //Bio Ben has spent his career focused on all forms of data. He has focused on developing algorithms to detect fraud, reduce patient readmission and redesign insurance provider policy to help reduce the overall cost of healthcare. He has also helped develop analytics for marketing and IT operations in order to optimize limited resources such as employees and budget. Ben privately consults on data science and engineering problems both solo as well as with a company called Acheron Analytics. He has experience both working hands-on with technical problems as well as helping leadership teams develop strategies to maximize their data.   //Other links you can check Ben on https://www.theseattledataguy.com/mlops-vs-aiops-what-is-the-difference/#page-content https://medium.com/@benrogojan https://www.kdnuggets.com/2020/01/data-science-interview-study-guide.html --------------- ✌️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 David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/ Connect with Ben on LinkedIn: https://www.linkedin.com/in/benjaminrogojan/ Timestamps [00:00] Intro to Benjamin Rogojan   [01:22] Ben's background [03:30] What are some of your learnings/key things that jumped out of you? [08:15] Agile and Data Science [10:28] Likelihood of failure [13:05] Sometimes you have to wait [15:11] Defining your data science process [19:55] Layer of communication is important between the data scientists and higher-ups [21:29] How do you navigate challenges? Are there any tools or processes you quantify to work with your clients? [24:30] How do you show the value of your work using monitoring and observability [27:58] How can we be better communicators?   [31:15] Have you seen other roles that really helped the jell of the team? [33:50] What are your interests? What are you passionate about at the moment? [34:29] Is there something new you're learning at the moment? [37:55] Do you have a process about how you figure out even data science or ML is right for a company? [39:33] Do you have a blog about the process you follow? [41:24] What is one negative wisdom that you want to share with the community? [44:35] How did you come up with the company name Seattle Data Guy? Links mentioned in this episode:   https://medium.com/@benrogojan https://www.cprime.com/resources/blog/agile-methodologies-how-they-fit-into-data-science-processes/ https://www.coriers.com/the-data-science-interview-study-guide/ https://medium.com/@SeattleDataGuy/from-data-scientist-to-data-leader-workshop-c6be69698af https://towardsdatascience.com/4-must-have-skills-every-data-scientist-should-learn-8ab3f23bc325

Coffee Sessions #20 with Neal Lathia of Monzo Bank, talking about Monzo Bank - An MLOps Case Study //Bio Neal is currently the Machine Learning Lead at Monzo in London, where his team focuses on building machine learning systems that optimise the app and help the company scale. Neal's work has always focused on applications that use machine learning - this has taken him from recommender systems to urban computing and travel information systems, digital health monitoring, smartphone sensors, and banking. //Talk Takeaways Monzo Bank has a small, but a very impactful team continuously learning new things. Optimistically do their utmost to avoid “throwing problems over the wall,” and so they build systems, iterate on machine learning models, and collaborate very closely with each other and with many folks across the business. Hopefully, all of that paints a picture of a team that aims to bring real and valuable machine learning systems to life. Monzo does not spend time trying to advance the state-of-the-art in machine learning or tweak models to absolute perfection. //Other links you can check Neal on Personal Website: http://nlathia.github.io/ Research: http://nlathia.github.io/research/ Press & Speaking: http://nlathia.github.io/public/ http://nlathia.github.io/2020/06/Customer-service-machine-learning.html http://nlathia.github.io/2020/10/ML-and-rule-engines.html http://nlathia.github.io/2020/10/Monzo-ML.html http://nlathia.github.io/2019/09/Large-NLP-in-prod.html http://nlathia.github.io/2020/07/Shadow-mode-deployments.html  https://github.com/operatorai --------------- ✌️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 David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/ Connect with Neal on LinkedIn: https://www.linkedin.com/in/nlathia/ Timestamps: [00:00] Intro to Neal Lathia   [02:48] Background of Monzo Bank [05:06] Problems you're solving with Machine Learning at Monzo?   [08:36] Why do you think it's fairly easy to frame a lot of problems using Machine Learning?   [11:56] How do you decide on rule-based or Machine learning?   [15:33] Team Structure   [19:18] What are some challenges like size, latency and the like? [21:52] How have you addressed learning skills/challenges in your team?   [26:17] Do you have something that connects your team with all the metadata you have? [27:14] Are you also having the monitoring models in your dashboard or is that something else? [28:51] Why should I bring another tool that the company is not familiar with when we already have one?   [31:43] Do you feel like there will be a point in time where you need to buy a tool because one problem is taking so much of your time? [38:30] Engineering optimization teams for machine learning?   [40:34] Take us through the idea to production? [46:29] How do you deal with reproducibility? [49:48] Do you have ethics people on the team? [54:12] Why are you using GCP and AWS? [56:09] What are these different used cases and how do they differ? [57:57] How do you address applications that don't work?

**The intersection between DataOps and privacy** DataOps is considered by many as the new era of data management, a set of principles that emphasizes communication, collaboration, integration, and automation of cooperation between the different teams in an organization that have to deal with data: data engineers, data scientists to data analysts.  But is there any relation between DataOps and data privacy protection? Can organizations leverage DataOps to ensure that their data is privacy compliant? For this episode we've invited Lars Albertsson founder of Scling and former Data Engineer at Spotify, Lars has been educating organizations on how to get value from data and engineering efficiency! You can easily find him and reach out on Twitter and LinkedIn. Don't forget to join the MLOps.Community if you are not yet a member. Useful links: What is DataOps - https://www.ibm.com/blogs/journey-to-ai/2019/12/what-is-dataops/ Data engineering reading list - https://www.scling.com/reading-list/ Data engineering courses - https://www.scling.com/courses/