MLOps - Design Thinking to Build ML Infra for ML and LLM Use Cases // Amritha Arun Babu & Abhik Choudhury // #221

Join us at our first in-person conference on June 25, all about AI Quality: https://www.aiqualityconference.com/Amritha Arun Babu Mysore has been an expert in the field of consumer electronics, software products, and online marketplaces for the past 15 years. She has experience developing supply chains from the ground up, delivering AI-based products to millions of users, and advocating for ethical AI across Amazon, Wayfair, Salesforce, and NetApp.Abhik Choudhury is a Senior Analytics Managing Consultant and Data Scientist with 11 years of experience in designing and implementing scalable data solutions for organizations across various industries.Huge thank you to  @latticeflow  for sponsoring this episode. LatticeFlow - https://latticeflow.ai/MLOps podcast #221 with Amritha Arun Babu Mysore, ML Product Leader at Klaviyo and Abhik Choudhury, Managing Consultant, Analytics at IBM, MLOps - Design Thinking to Build ML Infra for ML and LLM Use Cases.// AbstractAs machine learning (ML) and large language models (LLMs) continue permeating industries, robust ML infrastructure and operations (MLOps) are crucial to deploying these AI systems successfully. This podcast discusses best practices for building reusable, scalable, and governable ML Ops architectures tailored to ML and LLM use cases.// BioAmritha Arun Babu MysoreAmritha is an accomplished technology leader with over 12 years of experience spearheading product innovation and strategic initiatives at both large enterprises and rapid-growth startups.Leveraging her background in engineering, supply chain, and business, Amritha has led high-performing teams to deliver transformative solutions solving complex challenges. She has driven product road mapping, requirements analysis, system design, and launch execution for advanced platforms in domains like machine learning, logistics, and e-commerce.Abhik ChoudhuryAbhik is a Senior Analytics Managing Consultant and Data Scientist with 11 years of experience in designing and implementing scalable data solutions for organizations across various industries. Throughout his career, Abhik developed a strong understanding of AI/ML, Cloud computing, database management systems, data modeling, ETL processes, and Big Data Technologies. Abhik's expertise lies in leading cross-functional teams and collaborating with stakeholders at all levels to drive data-driven decision-making in longitudinal pharmacy and medical claims and wholesale drug distribution areas.// MLOps Jobs board jobs.mlops.community// MLOps Swag/Merchhttps://mlops-community.myshopify.com/// Related LinksAI Quality in Person Conference in collaboration with Kolena: https://www.aiqualityconference.com/LatticeFlow website: https://latticeflow.ai/ --------------- ✌️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 Abhik on LinkedIn: https://www.linkedin.com/in/abhik-choudhury-35450058Connect with Amritha on LinkedIn: https://www.linkedin.com/in/amritha-arun-babu-a2273729/Timestamps:[00:00] Amritha and Abhik's preferred coffee[00:30] Takeaways[02:42] Please like, share, leave a review, and subscribe to our MLOps channels![04:46] Abhik's background[05:38] Amritha's TLDR Journey[06:29] New Challenges in MLOps[08:49] ML Workflow Maturity Levels[11:25] Dev & Deploy Process Overview[14:51] Maturity Metrics and Progress[18:18] Automated ML Comparison: Semi vs. Fully[22:40] LLMs vs Traditional ML[28:38] Design MLOps for Usability[30:34] LatticeFlow Ad[33:30] Metrics Impact Assessment[35:31] Spark Learning Risks Analysis[36:37] MLOps User Journeys[45:44] AI Engineer Transition Guide [52:09] Data Compliance Challenge[55:00] Wrap up