Automating Continuous Compliance for Scalable, Sustainable SaaS ML

Learn how to implement automated continuous compliance frameworks for scalable SaaS machine learning systems in this 24-minute conference talk from Conf42 ML 2026. Discover practical approaches to building end-to-end ML tools on cloud platforms while maintaining regulatory compliance and operational efficiency. Explore how AI and LLMs can automate IT support processes and help define customer requirements through data-driven feature development. Master the architecture principles for secure development, compliance integration, and touchless CI/CD pipelines that enable seamless deployments at scale. Understand how to implement ML-driven reliability monitoring including SLA tracking, latency optimization, automated alerting, and root cause analysis. Examine the integration of AI into CI/CD gates for automated code coverage analysis, security testing, and compliance validation. Gain insights into sustainability practices through ML-powered hardware optimization, right-sizing strategies, and carbon footprint reduction techniques for cloud-based machine learning operations.

Syllabus

Welcome to 42 Machine Learning 2026
Meet Puja Ranjan: Oracle Cloud & ML Engineering Background
What Puja’s Team Builds: End-to-End ML Tools on the Cloud
Why AI in Silicon Valley Matters: Automating IT Support with LLMs
Cloud Economics & Empowering Small Businesses with SaaS
From Data to Features: How AI Helps Define What Customers Want
Building the Feature: Architecture, Compliance, and Secure Dev
Deploying at Scale: Touchless CI/CD, Rollouts, and Regulations
SLA Monitoring & ML-Driven Reliability Latency, Alarms, Root Cause
AI in CI/CD Gates: Code Coverage, Security Tests, and Compliance Automation
Sustainability & Hardware Optimization with ML Right-Sizing, Carbon Footprint
Wrap-Up: The Promising Future of AI/ML Integration