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.
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question_answer ANECDOTE
Monmayuri Ray's Career Journey
Monmayuri Ray transitioned from mechanical engineering and risk analysis to data science and MLOps.
She has worked with natural language processing and scalable feature engineering before focusing on MLOps transformation.
insights INSIGHT
DevOps Lens on MLOps Challenges
DevOps engineers often find MLOps challenging as they lack deep understanding of machine learning.
Integrating machine learning into existing CI/CD pipelines requires orchestration across full software development lifecycles.
insights INSIGHT
Economic Perspective on MLOps
AI lowers the cost of machine prediction, increasing its use and complementary human decisions.
Coffee Sessions #41 with Monmayuri Ray of Gitlab, CI/CD in MLOPS.
//Abstract
We all are familiar with the concept of MVP. In the world of DevOps, one is also familiar with Minimal Viable Feature and further Minimal Viable change. CI/CD is the orchestrator and the underlying base to enable automated experimentation, to start small, and build an idea for production. Now if we use the same fundamentals in MLOps, what does that mean?
The podcast will take the audience on a journey in understanding the fundamentals of orchestrating machine predictions using responsible CI/CD in MLOps in this ever-changing, agile world of software development. One shall hope to learn how to excel at the craft of CI for Machine Learning (ML), lowering the cost of deployment through a robust CI/CD/CT/CF framework.
//Bio
Monmayuri is an advisor, data scientist, and researcher specializing in MLops/DevOps at GitLab in Sydney. She builds creative, products to solve challenges for companies in industries as diverse as financial services, healthcare, and human capital.
Along the way, Mon has built expertise in Natural Language Processing, scalable feature engineering, MLOps transformation and digitization, and the humanization of technology. With a background in applied mathematics in biomedical engineering, she likes to describe the essence of AI as “low-cost prediction” and MLOps as “low-cost transaction” and believes the world needs the collaboration of poets, historians, artists, psychoanalysts and scientists, engineers to unlock the potential of these emerging technologies where one works in making a machine think like humans and be efficient automated fortune tellers.
//Takeaways
Key Takeaways include how to incorporate the best CI/CD practice in your MLOPS lifecycle. Things to do and things not to do. How best to get the DevOps engineer, ML engineer, and data scientists to speak the same language and automate CI for pipeline and models.
--------------- ✌️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 Mon on LinkedIn: https://www.linkedin.com/in/monmayuri-ray-713164a0/
Timestamps:
[00:00] Introduction to Monmayuri Ray
[00:57] Mon's background in tech
[02:50] MLOps being approached at Gitlab
[07:00] CI/CD for MLOPS Definition
[07:57] "AI is the dropping cost of machine prediction."
[10:25] MLOps and other tools fitting into Gitlab
[12:18] "If you want to have an MLOps first strategy, anything you are putting first needs to be substituted with what you had before first. It's really important then to know your priorities."
[15:24] Process of how to build
[18:16] "Before getting into even understanding the maturity, understand the outcome."
[18:45] Challenges in CI/CD for MLOps
[19:50]" Automation also empowers collaboration."
[24:15] Keeping up
[28:33] "I think, the best tools and frameworks are to give people the freedom to be the best version of who they are. As a system, being governed, having that controlled freedom, you can be more Human."
[31:20] Resources to suggest in terms of MLOps Education
[32:12] Understand the business outcomes of MLOps - Understanding the economics of AI and Machine Learning - Cultural shift
[35:57] Effectiveness of understanding the business outcomes of MLOps to Gitlab customers.
[39:42] "It's judgment, action, outcome, and how does this fully impact the overall workflow."
[40:00] Enabling vs Keeping the guardrails on
[43:26] Best practices
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