MLOps.community

MLOps community meetup #45! Last Wednesday, we talked to Joe Reis, CEO/Co-Founder of Ternary Data.Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠⁠⁠⁠⁠⁠⁠// 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/UC3H60XHMp6BrUzR5eUZDyZghttps://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/slackFollow us on Twitter: @mlopscommunitySign 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 onto 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 real-time?  [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 is an overkill for?[33:43] Are you thinking about what data sets to collect and how different 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.Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠⁠⁠⁠⁠⁠//BioCarl 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.pdfhttps://www.youtube.com/watch?v=-xIai_FvcSk&ab_channel=WePayEngineeringhttps://softwareengineeringdaily.com/2019/10/23/linkedin-data-platform-with-carl-steinbach/https://www.slideshare.net/linkedin/carl-steinbach-open-sourcehttps://dreamsongs.com/RiseOfWorseIsBetter.htmlhttps://engineering.linkedin.com/blog/2017/03/a-checkup-with-dr--elephant--one-year-laterhttps://engineering.linkedin.com/https://engineering.linkedin.com/blog/2018/11/using-translatable-portable-UDFshttps://a16z.com/2020/10/15/the-emerging-architectures-for-modern-data-infrastructure/--------------- ✌️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 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.Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠⁠⁠⁠⁠// 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/cepoihttps://www.youtube.com/watch?v=lakPlz8GJcA&ab_channel=RConsortiumhttps://www.youtube.com/watch?v=-oMZAS9qfrE&ab_channel=AnalyticsIndiaMagazinehttps://www.youtube.com/watch?v=yyWirT279tY&ab_channel=FunctionalTVhttps://www.youtube.com/watch?v=QkRJ24Q0E-k&ab_channel=Matroid----------- 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 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 the 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 in?[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 have you 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 GuyJoin the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠⁠⁠⁠//BioBen 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 policies 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 working both hands-on with technical problems as well as helping leadership teams develop strategies to maximize their data.  //Other links you can check Ben onhttps://www.theseattledataguy.com/mlops-vs-aiops-what-is-the-difference/#page-contenthttps://medium.com/@benrogojanhttps://www.kdnuggets.com/2020/01/data-science-interview-study-guide.html--------------- ✌️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 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 at 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] A 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/@benrogojanhttps://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-c6be69698afhttps://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 StudyJoin the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠⁠⁠//BioNeal 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 TakeawaysMonzo Bank has a small but 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 onPersonal 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/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 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 use 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/

**Are Privacy Enhancing Technologies a myth** Data Privacy and machine learning are here to stay, and there’s no doubt they’re the hot trends to be following. But do they need to clash with each other? Can we have these titans to co-exist? It seems like finally 2020 and 2021 will be the years where Privacy Enhancing Technologies. But after all what are they? How are these techs being used and leveraged by organizations? Useful links: https://medium.com/@francis_49362/differential-privacy-not-a-complete-disaster-i-guess-d0345a76a5af Facebook and DIfferential Privacy Opacus Synthetic data generation

Coffee Sessions #19 with Barr Moses of Monte Carlo, Introducing Data Downtime: How to Prevent Broken Data Pipelines with Observability, co-hosted by Vishnu Rachakonda.Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠⁠// BioBarr Moses is CEO & Co-Founder of Monte Carlo, a data observability company backed by Accel and other top Silicon Valley investors. Previously, she was VP Customer Operations at customer success company Gainsight, where she helped scale the company 10x in revenue and, among other functions, built the data/analytics team. Prior to that, she was a management consultant at Bain & Company and a research assistant at the Statistics Department at Stanford. She also served in the Israeli Air Force as a commander of an intelligence data analyst unit. Barr graduated from Stanford with a B.Sc. in Mathematical and Computational Science.// Talk TakeawaysAs companies become increasingly data-driven, the technologies underlying these rich insights have grown more and more nuanced and complex. While our ability to collect, store, aggregate, and visualize this data has largely kept up with the needs of modern data teams (think: domain-oriented data meshes, cloud warehouses, data visualization tools, and data modeling solutions), the mechanics behind data quality and integrity have lagged.To keep pace with data’s clock speed of innovation, data engineers need to invest not only in the latest modeling and analytics tools but also in technologies that can increase data accuracy and prevent broken pipelines. The solution? Data observability, the next frontier of data engineering and a pillar of the emerging Data Reliability category, and the fix for eliminating data downtime.During this talk, listeners will learn about:The rise (and threat) of data downtimeThe relationship between DevOps Observability and Data ObservabilityData Observability and its five key pillarsHow the best data teams are leveraging Data Observability to prevent broken pipelines// About Monte Carlo As businesses increasingly rely on data to drive better decision-making, it’s mission-critical that this data is accurate and reliable. Billed by Forbes as the New Relic for data teams and backed by Accel and GGV, Monte Carlo solves the costly problem of broken data through their fully automated, end-to-end data reliability platform. Data teams spend north of 30% of their time tackling data quality issues, distracting data engineers, data scientists, and data analysts from working on revenue-generating projects. Providing full coverage of your data stack – all the way from data lake and warehouse to analytics dashboard – Monte Carlo’s platform empowers companies such as Eventbrite, Compass, Vimeo, and other enterprises to trust their data, saving time and money and unlocking the potential of data.// Other links you can check Barr onLearn more about Monte Carlo: https://www.montecarlodata.comWhat is data downtime? https://www.montecarlodata.com/the-rise-of-data-downtime/  What is data observability? https://www.montecarlodata.com/data-observability-the-next-frontier-of-data-engineering/How data observability prevents broken data pipelines: https://www.montecarlodata.com/data-observability-how-to-prevent-your-data-pipelines-from-breaking/