MLOps for GenAI Applications // Harcharan Kabbay // #256

Harcharan Kabbay is a Data Scientist & AI/ML Engineer with Expertise in MLOps, Kubernetes, and DevOps, Driving End-to-End Automation and Transforming Data into Actionable Insights.MLOps for GenAI Applications // MLOps Podcast #256 with Harcharan Kabbay, Lead Machine Learning Engineer at World Wide Technology.// AbstractThe discussion begins with a brief overview of the Retrieval-Augmented Generation (RAG) framework, highlighting its significance in enhancing AI capabilities by combining retrieval mechanisms with generative models. The podcast further explores the integration of MLOps, focusing on best practices for embedding the RAG framework into a CI/CD pipeline. This includes ensuring robust monitoring, effective version control, and automated deployment processes that maintain the agility and efficiency of AI applications. A significant portion of the conversation is dedicated to the importance of automation in platform provisioning, emphasizing tools like Terraform. The discussion extends to application design, covering essential elements such as key vaults, configurations, and strategies for seamless promotion across different environments (development, testing, and production). We'll also address how to enhance the security posture of applications through network firewalls, key rotation, and other measures. Let's talk about the power of Kubernetes and related tools to aid a good application design. The podcast highlights the principles of good application design, including proper observability and eliminating single points of failure. I would share strategies to reduce development time by creating templates for GitHub repositories by application types to be reused, also templates for pull requests, thereby minimizing human errors and streamlining the development process.// BioHarcharan is an AI and machine learning expert with a robust background in Kubernetes, DevOps, and automation. He specializes in MLOps, facilitating the adoption of industry best practices and platform provisioning automation. With extensive experience in developing and optimizing ML and data engineering pipelines, Harcharan excels at integrating RAG-based applications into production environments. His expertise in building scalable, automated AI systems has empowered the organization to enhance decision-making and problem-solving capabilities through advanced machine-learning techniques.// MLOps Jobs board jobs.mlops.community// MLOps Swag/Merchhttps://mlops-community.myshopify.com/// Related LinksHarcharan's Medium - https://medium.com/@harcharan-kabbayData Engineering for AI/ML Conference: https://home.mlops.community/home/events/dataengforai --------------- ✌️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 Harcharan on LinkedIn: https://www.linkedin.com/in/harcharankabbay/locale=en_USTimestamps:[00:00] Harcharan's preferred coffee[00:21] Takeaways[01:03] Against local LLMs[02:11] Creating bad habits[02:42] Operationalizing RAG from the CI/CD perspective[09:39] Kubernetes vs LLM Deployment[12:12] Tool preferences in ML[14:39] DevOps perspective of deployment[17:44] Terraform Licensing Controversy[22:47] PR Review Template Guidance[27:32] People process tech order[29:22] Register for the Data Engineering for AI/ML Conference now![30:00] ML monitoring strategies explained[39:39] Serverless vs Overprovisioning[44:43] Model SLA's and Monitoring[51:04] LLM to App transition[52:42] Ensuring Robust Architecture[58:53] Chaos engineering in ML[1:04:43] Wrap up