mlops

Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams. Register for future sessions here: http://bit.ly/comet-ml-oh Checkout Comet ML by visiting: https://www.comet.ml/ Or on Twitter: https://twitter.com/CometML Connect with Ayodele LinkedIn: https://www.linkedin.com/in/ayodeleodubela/ Twitter: https://twitter.com/DataSciBae Check out her course on LinkedIn Learning: https://www.linkedin.com/learning/supervised-learning-essential-training/supervised-machine-learning-and-the-technology-boom [00:00:24] Getting to know my co-host, Ayodele [00:04:01] Ayodele talks about her new course on LinkedIn Learning [00:04:57] What is a data evangelist? [00:06:04] Why Comet ML decided to pair up with The Artists of Data Science [00:07:20] Where Comet ML fits into the machine learning lifecycle [00:12:58] What are the do’s and don'ts for anyone who wants to get into machine learning? [00:21:23] How to find labels for data in an image recognition project? [00:25:55] MLOps Engineer vs Machine Learning Engineer? [00:30:04] How do we convince senior leaders that we need an ML solution? [00:40:50] The trials and tribulations of being the only data scientist in an organization: dealing with what to learn and imposter syndrome. [00:53:37] How to learn more about a company [00:57:39] Struggles with linear algebra [01:02:56] Building data pipelines [01:04:25] Paying for internships

On this episode of The Artists of Data Science, we get a chance to hear from Srivatsan Srinivasan, a data scientist who has nearly two decades of applying his intense passion for building data driven products. He's a strong leader who effectively motivates, mentors, and directs others, and has served as a trusted advisor to senior level executives. He gives insight into how he broke into the data science field, the importance of focusing on business outcomes,, and some important soft skills. Srivatsan shares with us his tips on how to navigate crazy job descriptions, as well as his methods for communicating with executives. This episode contains actionable advice from someone who has been working with data since the beginning! WHAT YOU WILL LEARN [10:26] What it means to be a good leader in data science [11:45] How to productionize a model [15:01] Concept Drift [17:54] How to navigate difficult job descriptions [20:33] Tips on communicating with executives QUOTES [9:09] "I think more and more data scientists today are technology focused. They need to use technology to just solve a problem…they should focus more on business outcomes." [10:26] "…a good leader in data science…should be ready to embrace failure" [12:21] "…start with modularizing your code, see where are your common functions that you can use" FIND SRIVATSAN ONLINE LinkedIn: https://www.linkedin.com/in/srivatsan-srinivasan-b8131b/ YouTube: https://www.youtube.com/c/AIEngineeringLife SHOW NOTES [00:01:17] Introduction of our guest today [00:02:58] Let's talk a little bit about how you first heard of data science and what drew you to the field and maybe touch on some of the challenges you faced while breaking into the field. [00:05:13] You've been so generous with your knowledge and sharing your knowledge, creating some really well crafted content for LinkedIn and YouTube. And I'm wondering what's the inspiration behind that? [00:06:35] Where do you see the field headed in the next two to five years? [00:08:41] In this vision of the future, what's going to separate the great data scientists from the ones that are just merely good? [00:10:08] What does it mean to be a good leader in data science? And how can an individual contributor embody the characteristics of a good leader without necessarily having the title? [00:11:30] What are some challenges that a a notebook data scientist can face when it comes time to productionize a model? And do you have any tips for how to overcome those hurdles? [00:12:43] Some actionable tips that you can use today for moving outside of notebooks [00:13:32] What are some things that we should be keeping track of once we have deployed our model into production? [00:14:44] A discussion of concept drift and data drift [00:17:08] Do you have any advice or insight for people that are breaking into the field and they see these job postings that look like they want the abilities of an entire team rolled up into one person and then they they just become scared of applying. Do you have any tips or advice for them? [00:19:12] What are some soft skills that candidates are missing that are really going to separate them from their competition? [00:20:23] And do you have any tips for a data scientist who might find themselves having to present to a non-technical audience or perhaps a room full of executives? [00:21:16] What's the one thing you want people to learn from your story? [00:22:03] The lightning round Special Guest: Srivatsan Srinivasan.