Santona Tuli's guest appearances

Santona Tuli

MLOps.community 

The Artists of Data Science

Monday Morning Data Chat

Annie Duke

Tiffany Shlain

David Benjamin

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Barbara Oakley

John Vervaeke

Jeff Kreisler

Donald Robertson

Marcus du Sautoy

OwlTail

Roy Hasson

MLOps Coffee Sessions #166 with Roy Hasson &amp; Santona Tuli, Eliminating Garbage In/Garbage Out for Analytics and ML.// AbstractShift left data quality ownership and observability that makes it easy for users to catch bad data at the source and stop it from entering your analytics/ML stack.// BioSantona TuliSantona Tuli, Ph.D. began her data journey through fundamental physics—searching through massive event data from particle collisions at CERN to detect rare particles. She’s since extended her machine learning engineering to natural language processing, before switching focus to product and data engineering for data workflow authoring frameworks. As a Python engineer, she started with the programmatic data orchestration tool, Airflow, helping improve its developer experience for data science and machine learning pipelines. Currently, at Upsolver, she leads data engineering and science, driving developer research and engagement for the declarative workflow authoring framework in SQL. Dr. Tuli is passionate about building, as well as empowering others to build, end-to-end data and ML pipelines, scalably. Roy HassonRoy is the head of product at Upsolver helping companies deliver high-quality data to their analytics and ML tools. Previously, Roy led product management for AWS Glue and AWS Lake Formation.// MLOps Jobs board https://mlops.pallet.xyz/jobs// MLOps Swag/Merchhttps://mlops-community.myshopify.com/// Related Linkshttps://royondata.substack.com/--------------- ✌️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/registerCatch all episodes, blogs, newsletters, and more: https://mlops.community/Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/Connect with Roy on LinkedIn: https://www.linkedin.com/in/royhasson/Connect with Santona on LinkedIn: https://www.linkedin.com/in/santona-tuli/Timestamps:[00:00] Santona's and Roy's preferred coffee[01:05] Santona's and Roy's background[03:33] Takeaways[05:49] Please like, share, and subscribe to our MLOps channels![06:42] Back story of having Santona and Roy on the podcast[09:51] Santona's story[11:37] Optimal tag teamwork[16:53] Dealing with stakeholder needs[26:25] Having mechanisms in place[27:30] Building for data Engineers vs building for data scientists[34:50] Creating solutions for users[38:55] User experience holistic point of view[41:11] Tooling sprawl is real[42:00] LLMs reliability [45:00] Things would have loved to learn five years ago[49:46] Wrap up

MLOps Coffee Sessions #166 with Roy Hasson &amp; Santona Tuli, Eliminating Garbage In/Garbage Out for Analytics and ML. // Abstract Shift left data quality ownership and observability that makes it easy for users to catch bad data at the source and stop it from entering your analytics/ML stack. // Bio Santona Tuli Santona Tuli, Ph.D. began her data journey through fundamental physics—searching through massive event data from particle collisions at CERN to detect rare particles. She’s since extended her machine learning engineering to natural language processing, before switching focus to product and data engineering for data workflow authoring frameworks. As a Python engineer, she started with the programmatic data orchestration tool, Airflow, helping improve its developer experience for data science and machine learning pipelines. Currently, at Upsolver, she leads data engineering and science, driving developer research and engagement for the declarative workflow authoring framework in SQL. Dr. Tuli is passionate about building, as well as empowering others to build, end-to-end data and ML pipelines, scalably. Roy Hasson Roy is the head of product at Upsolver helping companies deliver high-quality data to their analytics and ML tools. Previously, Roy led product management for AWS Glue and AWS Lake Formation. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch <a href="https://mlops-community.myshopify.com/" rel="nofollow" target="_blank">https://mlops-community.myshopify.com/</a> // Related Links <a href="https://royondata.substack.com/" rel="nofollow" target="_blank">https://royondata.substack.com/</a> --------------- ✌️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 Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: <a href="https://www.linkedin.com/in/dpbrinkm/" rel="nofollow" target="_blank">https://www.linkedin.com/in/dpbrinkm/</a> Connect with Roy on LinkedIn: <a href="https://www.linkedin.com/in/royhasson/" rel="nofollow" target="_blank">https://www.linkedin.com/in/royhasson/</a> Connect with Santona on LinkedIn: <a href="https://www.linkedin.com/in/santona-tuli/" rel="nofollow" target="_blank">https://www.linkedin.com/in/santona-tuli/</a> Timestamps: [00:00] Santona's and Roy's preferred coffee [01:05] Santona's and Roy's background [03:33] Takeaways [05:49] Please like, share, and subscribe to our MLOps channels! [06:42] Back story of having Santona and Roy on the podcast [09:51] Santona's story [11:37] Optimal tag teamwork [16:53] Dealing with stakeholder needs [26:25] Having mechanisms in place [27:30] Building for data Engineers vs building for data scientists [34:50] Creating solutions for users [38:55] User experience holistic point of view [41:11] Tooling sprawl is real [42:00] LLMs reliability [45:00] Things would have loved to learn five years ago [49:46] Wrap up

Eliminating Garbage In/Garbage Out for Analytics and ML // Roy Hasson & Santona Tuli // MLOps Podcast #166

Santona Tuli discusses the product management aspects as a data scientist - product and stakeholder management, due diligence of requirements gathering, and developing a strategy before implementing DS/ML pipelines. You know, the fun stuff ;)

Santona Tuli discusses the product management aspects as a data scientist - product and stakeholder management, due diligence of requirements gathering, and developing a strategy before implementing DS/ML pipelines. You know, the fun stuff ;)

#127 - Product Management as a Data Scientist w/ Santona Tuli

On this episode of The Artists of Data Science, we get a chance to hear from Santona Tuli, a physicist and data scientist who has a PhD in physics specializing in nuclear science and quantum chromodynamics.  She currently leads a team of five doctoral and postdoctoral physicists studying a new plasma phase of matter and the elusive nuclear effects in high energy proton and nucleus collisions at the Large Hadron Collider at CERN in Geneva, Switzerland. Santona shares with us her journey into data science as a physicist, and her perspective on the future of the field. She also discusses the differences between data science and decision science, tips to break into the field, and advice for women in STEM.  It was an absolute delight hearing Santona’s advice, and I believe her unique perspectives can help all data scientists!  WHAT YOU'LL LEARN [4:46] Where the field of data science is headed [32:42] Is data science an art or science?   [49:07] Tips for breaking into data science [57:55] How to get over the perfectionist mindset and feeling like a failure  [1:02:07] Diversity and inclusion of minorities in STEM QUOTES [34:51] “...just being able to step outside and think of alternative approaches, stepping outside the predefined paths. To me, that's how the creative part of my brain is really engaged when I'm doing Data science.” [39:38] “...the audience should be able to look up at this screen and see themselves reflected in it, being able to understand that the physics that's going on...physics is very much within their reach. Science is very much within their reach.” [52:40] “...separate or distinguish what the end goal is and the steps that you need to take in order to get there” [55:21] “get over this idea that it has to be perfect before [you] push it out...What's the worst that can happen? Maybe someone criticizes in some way...But it might turn out that this criticism that you're receiving on it is actually going to help you iterate on that project and make it better.” WHERE TO FIND SANTONA LinkedIn: https://www.linkedin.com/in/santona-tuli/ SHOW NOTES [00:01:21] Introduction for our guest today [00:02:40] The path into data science [00:03:20] What the heck is quantum chromodynamics? [00:03:54] Data science and the study of nuclear forces [00:04:49] The future of data science [00:08:17] Data science and empathy [00:09:27] How to be a great data scientist [00:10:48] What is CERN? [00:13:13] What is this Y particle? [00:15:15] The data science work flow and particle physics [00:20:25] Data reduction and data bottlenecks [00:23:43] Selection cuts and rules based clustering [00:29:43] The importance of feature engineering [00:32:31] How do you view data science? Do you view it as an art or a science? [00:34:17] How does the creative process come to life in Data science? [00:36:39] Santona talks about the IMAX movie that she stars in [00:40:43] The difference between interpretable and explainable machine learning. [00:44:22] Decision science and data science [00:48:49] Words of encouragement for people learning new things [00:51:04] What does it mean to think like a product manager? [00:54:14] Break free of the perfectionist mindset [00:57:00] How to deal with feedback and criticism [00:58:31] What are some soft skills that you think Data scientists are missing? [01:01:29] Advice and words of encouragement for the women in our audience who are breaking into tech or currently in tech. [01:05:48] Santona talks about the impact she hopes to have on young women in STEM [01:09:08] What can men do, in particular in the Data community, to help foster the inclusion of women in STEM, in tech and Data? [01:11:28] What's the one thing you want people to learn from your story, [01:11:57] What's your data science superpower. [01:12:02] What would you say is the most fundamental truth of physics that all human beings should understand? [01:12:19] What do you think is the most mysterious aspect of our universe? [01:12:43] What is an academic topic outside of Data science that you think every data scientist should spend some time researching or studying on. [01:12:53] What's the number one book? Fiction, nonfiction? Or if you want to pick one of each that you would recommend our audience read. And what was your most impactful takeaway from it? [01:14:02] If we can somehow get a magical telephone that allowed you to contact 18 year old Santona, what would you tell her? [01:15:09] What song do you have on repeat. [01:15:28] How do people connect with you? Where can they find you?Special Guest: Santona Tuli, PhD.

On this episode of The Artists of Data Science, we get a chance to hear from Santona Tuli, a physicist and data scientist who has a PhD in physics specializing in nuclear science and quantum chromodynamics. She currently leads a team of five doctoral and postdoctoral physicists studying a new plasma phase of matter and the elusive nuclear effects in high energy proton and nucleus collisions at the Large Hadron Collider at CERN in Geneva, Switzerland. Santona shares with us her journey into data science as a physicist, and her perspective on the future of the field. She also discusses the differences between data science and decision science, tips to break into the field, and advice for women in STEM. It was an absolute delight hearing Santona’s advice, and I believe her unique perspectives can help all data scientists! WHAT YOU'LL LEARN [4:46] Where the field of data science is headed [32:42] Is data science an art or science? [49:07] Tips for breaking into data science [57:55] How to get over the perfectionist mindset and feeling like a failure [1:02:07] Diversity and inclusion of minorities in STEM QUOTES [34:51] “...just being able to step outside and think of alternative approaches, stepping outside the predefined paths. To me, that's how the creative part of my brain is really engaged when I'm doing Data science.” [39:38] “...the audience should be able to look up at this screen and see themselves reflected in it, being able to understand that the physics that's going on...physics is very much within their reach. Science is very much within their reach.” [52:40] “...separate or distinguish what the end goal is and the steps that you need to take in order to get there” [55:21] “get over this idea that it has to be perfect before [you] push it out...What's the worst that can happen? Maybe someone criticizes in some way...But it might turn out that this criticism that you're receiving on it is actually going to help you iterate on that project and make it better.” WHERE TO FIND SANTONA LinkedIn: <a href="https://www.linkedin.com/in/santona-tuli/" rel="nofollow" target="_blank">https://www.linkedin.com/in/santona-tuli/</a> SHOW NOTES [00:01:21] Introduction for our guest today [00:02:40] The path into data science [00:03:20] What the heck is quantum chromodynamics? [00:03:54] Data science and the study of nuclear forces [00:04:49] The future of data science [00:08:17] Data science and empathy [00:09:27] How to be a great data scientist [00:10:48] What is CERN? [00:13:13] What is this Y particle? [00:15:15] The data science work flow and particle physics [00:20:25] Data reduction and data bottlenecks [00:23:43] Selection cuts and rules based clustering [00:29:43] The importance of feature engineering [00:32:31] How do you view data science? Do you view it as an art or a science? [00:34:17] How does the creative process come to life in Data science? [00:36:39] Santona talks about the IMAX movie that she stars in [00:40:43] The difference between interpretable and explainable machine learning. [00:44:22] Decision science and data science [00:48:49] Words of encouragement for people learning new things [00:51:04] What does it mean to think like a product manager? [00:54:14] Break free of the perfectionist mindset [00:57:00] How to deal with feedback and criticism [00:58:31] What are some soft skills that you think Data scientists are missing? [01:01:29] Advice and words of encouragement for the women in our audience who are breaking into tech or currently in tech. [01:05:48] Santona talks about the impact she hopes to have on young women in STEM [01:09:08] What can men do, in particular in the Data community, to help foster the inclusion of women in STEM, in tech and Data? [01:11:28] What's the one thing you want people to learn from your story, [01:11:57] What's your data science superpower. [01:12:02] What would you say is the most fundamental truth of physics that all human beings should understand? [01:12:19] What do you think is the most mysterious aspect of our universe? [01:12:43] What is an academic topic outside of Data science that you think every data scientist should spend some time researching or studying on. [01:12:53] What's the number one book? Fiction, nonfiction? Or if you want to pick one of each that you would recommend our audience read. And what was your most impactful takeaway from it? [01:14:02] If we can somehow get a magical telephone that allowed you to contact 18 year old Santona, what would you tell her? [01:15:09] What song do you have on repeat. [01:15:28] How do people connect with you? Where can they find you?Special Guest: Santona Tuli, PhD.

Physics and the Art of Data Science | Santona Tuli, PhD