Hi everyone. I'm Manoja, and today we're exploring how to build AI platforms that actually work in prediction systems, handling millions of realtime decisions without breaking down. We'll understand the engineering practices that separate successful AI platforms from failed experiments. We'll cover the complete lifecycle designing containerized ML pipelines, implementing efficient models, serving architectures, establishing observability frameworks. Building self-healing, fault tolerant infrastructure. These are theoretical concepts. These are proven practices from real deployments processing millions of requests daily with measurable business impact. The transition from traditional software to AI driven platforms introduces fundamental changes. AI platforms must handle compute intensive GPU workloads, massive real-time data ingestion models, lifecycle management. Dynamic scaling requirements for computer vision applications, autonomous systems, surveillance, quality control. These challenges are amplified. Processing visual data at scale with millisecond response time requires a resilient engineering foundation, integrating automation, scalability, and observability. Traditional DevOps provides static point but is insufficient. Three foundational principles guide success infrastructure as code. Using terraform or cloud formation enables consistent replication of GPU enabled environments and version control infrastructure evolution. GitHub's workflows treat GIT as the single source of truth with automated synchronization tools, ensuring immutable deployments and automated rollbacks automated testing strategies. Require specialized frameworks for data validation, model validation, and integration tests for end-to-end pipeline Workflows fundamentally different from traditional software testing. Containerization delivers reproducibility and modularity through modular pipelines with separate containers for ingestion, pre-processing, training, and inference. GPU enable enablement. Leverages Nvidia Docker runtime and Kubernetes device plugins while version bills ensure deterministic bills tied to GI comets. Kubernetes orchestration handles spot scheduling with GPU constraints, horizontal autoscaling for inference, workloads, and node pools optimized for different pipeline stages. Here's how this works in practice. Data ingestion bots. Pull video streams from IOT sensors. Pre-processing pods apply transformations like normalization and augmentation model training pods. Leverage distributed GPUs and inference pods. Serve predictions via REST or GRPC endpoints. This enterprise scale vision system creates an end-to-end pipeline efficiently processing visual data while maintaining high availability and performance. Serving models at a low latency while ensuring reliability requires specific deployment patterns. Bluegreen deployments run new model versions parallel with old ones switching traffic seamlessly. Canary releases gradually rollout model to user subsets. Shadow deployments test model in product production environments without exposing outputs to users. Each pattern addresses different risk tolerance and validation requirements for production model updates. Three Key optimizations delivered dramatic performance improvements, batching and fairness Request groups multiple requests to maximize GPU utilization and reduce per request overhead. Model caching and W starts keep frequency access models in memory, eliminating cold start latency, accelerated serving frameworks like Tenser rt, Triton inference server, and tot serve optimized model execution on specified hardware. These optimizations typically provide three to five x throughput improvements while reducing latency from 200 milliseconds to 15 milliseconds. Observability transcends infrastructure monitoring by incorporating model and data centric metrics across three layers. Infrastructure metrics tracks, C-P-U-G-P-U, utilization, memory and network throughput. Pipeline monitoring covers success rates, Q latencies and data integrity checks, model observability, monitors, prediction, drift latency, error rates, and famous metrics. This comprehensive approach enables detection anomalies before they impact users such as GPU saturation, rising inference, latency, or degraded model accuracy. The tooling ecosystem combines traditional and ML specific monitoring. Prometheus plus Grafana provide comprehensive infrastructure dashboards, tracking resources, usage. And System Health Open Telemetry enables distributed tracing across ML pipelines, identifying bottlenecks and latency issues. Custom ML monitoring tools like YAPS eyes, AI handle data drift and bias detection coupling observability with alert thresholds, enable systems to detect and respond to anomalies automatically. Resilient platforms must recover automatically without manual intervention. Through several mechanisms. Kubernetes liveness readiness probes automatically restart failing containers. When health check fails cluster auto-scaling dynamically adjust compute resources based on workload demands. Chaos Engineering uses using tools like chaos mesh injects failures to violate system resilience under unexpected conditions. These mechanisms ensure continuous service availability and optimal resource utilization. Eliminate single point of failure requires comprehensive distribution. Distributed processing pipelines ensure no single point of failure by distributing workloads across multiple nodes and regions. Geo-redundant clusters support global AI workloads with multi-region deployments for disaster recovery and low latency serving. Event driven recovery provides automated rerouting of workloads on node failure through event based orchestration systems. Organization, imple implementing these principles report significant improvement, 99.9%. Uptime and reliability. SLA. Compliance in production AI systems means less than nine hours downtime per year. Deployment velocity measured in ours, model updates deployed within hours versus weeks in traditional workflows. 20 to 40% cost reduction through optimized GPU scheduling and resource allocation. Increased developer productivity teams focus on in innovation instead of firefighting operations. These are, these metrics demonstrate clear business value, justifying implementation complexity, the conversions of platform engineering and ML ops. Represents a paradigm shift enabling faster time to market through accelerated delivery of AI products via streamlined deployment, pipelines and automated workflows, reduced operational overhead with lower maintenance costs, through comprehensive automation of routine tasks and self feeling systems sustained trust with enhanced confidence in AI systems. By ensuring transparency, fairness, and consistent performance. These practices extend beyond computer vision to NLP recommendation systems and generative ai, where real time processing is equally critical. Building resilient AI platforms at scale is fundamentally about engineering trust into AI systems through containerized ML pipelines efficient model serving architecture. Comprehensive observability and self-healing infrastructure organizations deploy computer vision systems that are both high, perform high performing, and relatable by applying modern platform engineering principles, infrastructure as code GitHubs. Automated testing and fault tolerant design enterprises achieve operational resilience while unlocking faster innovation cycles and enhanced developer productivity. As such, your current AI de deployment practices prioritize containerization and monitoring as foundational capabilities and implement automation testing for both infrastructure and models. As realtime AI continues transforming industries, the ability of ability to engineer resilience, scalable platforms will define the next generation of enterprise success stories. Thank you.