Hello everyone, and welcome. My name is Pascal Goyle and I'm thrilled to be speaking at contu. Today we are going to deep dive into a topic that's crucial for any organization looking to leverage artificial intelligence effectively. That is cloud native AI at scale architectural patterns for enterprise success. In the next 20 minutes, we will explore how adopting cloud native principles is t just a trend, but a fundamental shift that's revolutionizing AI deployment. As a slide says, these architectures deliver measurable performance improvements, faster development cycles, optimize resource use, and ultimately a strong return on your AI investments. We'll walk through the challenges, the core native concepts, specific architectural patterns, and the tangible benefits you can expect, often backed by industry observations and project results. Let's start by contrasting the old way with the new. Many of us have experienced traditional AI deployment. It often involved manual steps, complex handoffs between data science and operation teams, and infrastructure that wasn't purpose built for ML workloads. This frequently led to the deployment cycles measured in weeks, sometimes even in months, plagued by manual error prone processes. Now compare that to the cloud native approach. By leveraging automation, containerization, and infrastructure as a code concepts will detailed shortly, will dramatically shift deployment times down to days, hours, or even minutes. Rollouts become automated and consistent reducing. The IT worked on my machine syndrome. The measurable results often cited in industry reports and confirmed through project experience speak for themselves. Organizations adopting these patterns typically see around 70% faster time reproduction for the AI models. Think about the competitive advantage that speeds provide. Furthermore, the automation and consistency lead to what case studies show can be staggering. 85% reduction in deployment failure. This isn't just about speed, it's about relay reliability and trust in the deployment process. A corner store of cloud native approach is containerization using technologies like daca. Why is this so helpful for ai? First, environment, consistency containers build the code, bundle the code, libraries and dependencies together. This eliminates those frustrating works on my machine issues and ensures the environment is identical for a deployment through two production. This consistency alone based on operational data can reduce deployment failures significantly, often by as much as 78%. Second reduced serving latency. We can build highly optimized minimal container images specifically for inference. This focus cuts down on bloat and performance benchmarks often show this can decrease modern inference time by around 35%, leading to a much better and faster user experience for your AI applications. Third resource, your isolation. Containers allow components to run in isolation, preventing resource contention when one process hogs CPU, or memory needed by another. This isolation also enables precise independent scaling. You can scale your inference service without necessarily scaling other parts of your application. Finally, enhance security containers. Provide isolated runtime environments. By using minimal base images, which significantly reduce the potential attack surface, a widely recognized security benefit, making our AI deployments more secure. Another foundational element is infrastructure as code, which is sometimes called as IAZ. Instead of manually clicking buttons in a cloud console, we define our infrastructure servers, networks. Databases and kuban Kubernetes clusters in code. The key is declarative configuration. We use tools like Terraform or Lummi to define the desired state of our infrastructure and the tool configures out how to achieve that result. This is much more reliable and repeatable than writing procedural skills. Do this and do then that. These configuration files are version control, like application code, giving us transparent change tracking, and ability to easily roll back if issues arise. Tools like Terraform also offer seamless multi-cloud deployments using a consist syntax. The business outcome of IAC is profound for AI infrastructure as seen across many implementations. A potential 90% reduction in configuration drift across environments, ensuring dev, staging and production look the same. Often 65%. Faster disaster recovery because we can automatically reprovision the entire infrastructure stack from code. Experience often shows 42% lower infrastructure costs through optimization, eliminating manual provision, zombie resources, and leveraging automation and significantly reducing operational toil. Sometimes 75% fewer manual interventions needed for routine task. So the question is, how do these concepts come together? In an architecture, a common effective approach is a layered architectural pattern, which promotes decoupling and specialization. At the top, we have application layer, which promotes decoupling and specialization. At the top below that we, that sits the ML framework layer. This is where optimized framework like TensorFlow or PyTorch, often with custom accelerated runtimes designed for high performance or specific hardware. Then comes container orchestration. This is typically Kubernetes managing the lifecycle of our contained rise application. And the model. It handles auto scaling based on load, ensure high availability, and can use specialized ML operators like Q Flow operators, TF job for managing training and inference workloads. Finally, the last is the infrastructure layers, which provides the raw compute power like machine virtual machines, bare metal servers. Which are crucial for ai sometimes like GPUs, TPU clusters for accelerated computation. The beauty of this decoupled architecture is that each layer can be scaled and optimized independently. Project outcomes often demonstrate benefits like 45%, lower maintenance cost, 60% improved resource utilization as each layer is right sized and greatly enhance operational flexibility. Within our cloud native AI architecture, it's critical to recognize that model training and model inference serving have very different requirements. We should design separate environments for them. The training environment needs massive computation power, often leveraging high powered GPU clusters. It benefits from the batch processing optimization. Admin can use cost saving strategies like spot instances, which are interruptible vs. At lower cost. Since training jobs can often tolerate restart, the infrastructure here can be ephemeral, spun off for a training run or to down afterwards. The inference environment, on the other hand, must be optimized for low latency to serve predictions quickly. It requires robust auto-scaling capabilities to handle fluctuating request volumes. Users right size, compute resources for efficiency, and absolutely needs a high availability design to ensure the service is always responsive. This separation has a direct business impact. Frequently observed and optimized cloud deployments, we often see 35% reduction cloud costs by using the right resources for each job. Example, spot for training model deployment can be 50% faster. As the inference environment is streamlined and ready, we can achieve 99.9% or higher. Inference service uptime a common SLA target enabled by HA design and the system exhibits elasticity. Scaling automatically during demand spikes. As AI models become more complex, numerous managing the d complex and numerous, managing the data features they rely on becoming, becomes a major challenge. This is where a feature store comes in. A feature store is a centralized repository for curated, documented, and versioned features used in ML models. It typically involves several components, feature engineering, standardizing how features are defined, validated and transformed through shared pipelines. Feature storage, maintaining time, consistent version feature. Data often with comprehensive metadata tracking so you can know exactly what data was used for. Track training. Training access, enable, enabling reproducible model training by allowing Retriable retrieval of features as they were at specific points in time. Point in time, correct. Inference serving, delivering optimized low latency feature vectors needed by models for real-time protections. Feature stores significantly accelerate air development. A benefit highlighted in many M lops case studies by enabling feature reuse across team, they eliminate duplicated effort. They solve the notorious training service skew problem, which features used in training differ from production. And reduce overall deployment time by about 40%. Organizations implementing feature store are often report results like 35% faster time to market for new models, and a 60% improvement around operational efficiency related to feature management. We mentioned Kubernetes earlier. Let's look at some specific Kubernetes organization patterns that are particularly valuable. For AI ML workloads, the first one is custom resource definitions, sometimes called as CRDs. Kubernetes allows us to define our own custom types for ml. This means resources like TF Job or Python job that understand the specifics of running distributed training jobs or custom resources for managing model deployments. This enables a declarative approach to managing the ML lifecycle itself. Operators, these automate complex stateful operations, ML operators can manage the entire lifecycle of the ml machine learning, workflow, provisioning resources, running training, deploying models, monitoring, codifying operational knowledge. Third is the horizontal pod auto scaling. This automatically scales the number of pods, containers based on metrics like CP utilization, request volume, or even metrics like GPA utilization. This ensures inference services have the right amount of resources in the real time. Fourth is a service mesh integration. Example is issue a service mesh provides advanced capabilities like fine-grain traffic routing. Essential for carrying deployment, candry deployments or AB testing models. Mutual DLS for security and detailed observability metrics, latency error rates across services. These patterns turn Kubernetes into a powerful ML aware platform, enhancing automation and control AI particularly. Particularly deep learning heavily relies on specialized hardware like GPUs and TPUs. Effectively orchestrating these expensive resources is critical in a cloud native environment. So the first is the GPU and TPU management Kubernetes device plugin allow fine-grained hardware allocation. Advanced schedulers can enable multi-tenant GPU sharing with some implementation. Showing utilization increase to three X compared to the dedicated, dedicating a whole GP to one task performance optimizations. Techniques like numa aware scheduling according to performance tuning guides can improve throughput by 40%. Similarly, using mixed precision inference often reduces latency by 60% with minimal accuracy loss as documented in frameworks, best practices. Cost optimization. In integrating spot instances for training workloads. A common cloud saving strategy can slash training costs by up to 70%. Furthermore, automatic hardware hibernation spinning down expensive GPUs TPUs when idle project experience shows that it can be saved around 45% on idle costs, which is crucial given how expensive these accelerators are. Intelligent orchestration ensures we get the maximum performance and value from our hardware investments. Bringing all of this together requires robust automation, which leads us to ML lops and CI ICD pipeline, tailored for machine learning. ML OPS applies DevOps principles to the ML lifecycle. CICD pipelines are the backbone of ML ops continuous integration. This isn't just about code anymore. For ml, it involves automated model testing, unit testing, integration tests, data validation, model validation against predefined metrics and quality gates. It often integrates with code model repositories via web hooks. We need automated checks for unit tests, model quality metrics, and even security scanning for dependencies. Continuous delivery focuses on automating the preparation for deployment. It includes packaging the model and core code o often in two containers, generating environment specific configurations like Kubernetes, helm charts and versioning. All artifacts produce continuous deployment, automates the actual rollout of the model to production. We use progressive deployment strategies like blue-green deployment, switching traffic to a new version or caning releases routing a small percentage of traffic first. Automated analysis for of performance metrics and roll back capabilities are crucial here for ensure to ensure safety. These pipelines central to ML lops maturity models. Ensure. Moving from code. Commit to a validated deployed model is fast, reliable, and repeatable. Deployment is in the end of the story. Models degrade over time due to changes in their data patterns sometimes. What has drift? Automating? Monitoring solutions are essential for maintaining performance and production. Cloud native monitoring tools integrated with ML platform provide critical capabilities. First is the faster drift detection. Advanced algorithms continuously monitor input data and model protections, detecting data drift and model drift in near real time. This allows for immediate corrective action like retraining before performance degrades significantly. Reports suggests improvements of up to 85% in detection speed compared to manual checks system of time. A fault tolerant monitoring architecture potentially with predictive maintenance alerts helps ensure continuous operation even during infrastructure clips contributing to a 99.95% or higher system uptime that many platforms aim for resource optimization. Intelligent monitoring feeds back into the resource allocation by observing actual workload patterns. Dynamic adjustment can significantly reduce cloud expenditure. Practical results often show reduction around 40%. ROI multiplier crucially, end-to-end monitoring connects ML performance metrics directly with business key performance indicators. This allows organizations to clearly see the value of AI is delivering and optimized models based on business impact. Effectively acting as an ROI multiplier. Some case studies indicate this can potentially triple the return on AI investments by ensuring models stay relevant and performant. So to wrap up, adopting cloud native architectural patterns is fundamental for achieving AI success at scale with the enterprise. We have seen how containerization. Infrastructure as code layered architecture, separating training and inference feature stores, Kubernetes orchestration, hardware management, ML ops, pipelines, and automated monitoring work together. The key to takeaways are the tangible benefits widely reported across industry, dramatically accelerated deployment cycles, improved resource utilization and cost savings. Enhance reliability and security and ultimately a much stronger and more measurable return on your AI investment. Thank you very much for your time and attention today. I hope this overview of cloud native AI patterns was valuable. Please feel free to correct me on LinkedIn. If you have any questions, my contacts are on the screen. Thank you so much.