CI/CD in MLOPS // Monmayuri Ray // MLOps Coffee Sessions #41

Coffee Sessions #41 with Monmayuri Ray of Gitlab, CI/CD in MLOPS.Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠⁠⁠⁠⁠⁠// AbstractWe are all 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.// BioMonmayuri 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.//TakeawaysKey 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 the pipeline and models?--------------- ✌️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 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. 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 [35:57] Effectiveness of understanding the business outcomes of MLOps to Gitlab customers.[40:00] Enabling vs Keeping the guardrails on [43:26] Best practices