Hello everyone. My name is Pastor Gole and I am a software engineer at Google. Today I'll be presenting a talk on building bulletproof AI systems from container kiosk to production paradise. The journey is often described as a nightmare a chaotic world of containers, configurations, and last minute fires. We are here to change that narrative. In this session we'll explore how to transform that deployment narrative into a production paradise. We'll look at battle tested architectural patents that will help you build bulletproof AI platforms that actually deliver on their promises. Let's start by acknowledging the reality of enterprise AI deployment. Many organizations, despite their best effort, run into significant hurdles that lead. To delays and even project failures. These challenges generally fall into three categories. The first is environment inconsistencies. We have all heard it like, but it works on my machine. This syndrome leads to the unpredictable behavior. When models move from a data scientist laptop to a staging or a production environment, inconsistent libraries, dependencies, and even hard work can cause the morals to behave different or fail entirely. The next are the manual deployment processes. These are often error prone, incredibly time consuming, and create bottlenecks. They frequently rely on specialized knowledge of a few key individuals. If that person is on vacation, good luck deploying that critical update. And finally, this leads to the production fires those late night emergencies caused by unforeseen discrepancies between what was tested and what is now live. These fires not only burn out our teams, but also erode confidence in the AI initiatives themselves. The result is a deployment cycle that stretches from days into works, or even months drastically diminishing the returns of our AI system. So what does a production paradise look like? Leading organizations have already transformed their AI deployment processes and the results are dramatic. Success isn't just about avoiding failure. It's about creating a streamlined, efficient, and reliable system. Imagine reducing your model deployment time from weeks to may hours. Think about cutting configuration drifts. And errors by as much as 80%. This is achievable. We are talking about true deployment to production parity, which eliminates those nasty late night surprises. Furthermore, success means having scalable infrastructure that grows with demand without breaking your budget. It means empowering your data scientists with a self-service platforms, allowing them to innovate faster. All while maintaining the governance and control the business needs. This is the paradise we are aiming for. Okay, now let's look at what the session mode map will look like. How did we get here? I have structured our journey today into four key parts. This is your part to AI platform success. First, we'll dive into containerization strategies. Specifically help ER and Kubernetes can eliminate environment inconsistencies. Next, we will cover infrastructure as core patterns using Terraform and help to prevent my configuration drifts. Then we'll explore GitHub's workflows to automate our pipelines and make them version and trustworthy. Finally, we'll look at real world ROI data. From organizations that have successfully implemented these patterns at scale, our foundation begins with containerization. This is what solves the classic. It works on my machine problem by unpacking and packaging our models with all their dependencies, libraries, and runtime configurations into a single portable unit. But AI workloads have unique needs. That generalization approaches often don't address. We need to think about GPU acceleration and access to other specialized hardware. We are often dealing with very large model artifacts that can be gigabytes in size, which has implications for image storage and memory requirements, and our inference and scaling patterns are often very difficult from traditional web services. The impact of getting this right is huge. As a principal, Mr. Engineer at Forbes 500 financial services company, put it containerization, reduces our model deployment failure by 65% and eliminates an entire class of environment rated bugs. This is the power of a solid container foundation To build on that foundation, we need to use docker patterns that are optimized for ai. Let's look at three key ones. First, multi-stage builds. This involves using separate development and production stages in a Docker file. This results in the much smaller, more secure final image, leading to faster pools, a reduced attack surface, and lower resource consumption. Second, the hardware aware base images. Don't use a generic base image for everything. Use Cuda enabled base images. Especially for your GPU workloads to get maximum performance for CPU based inference services, use CPU optimized images. This simple step can drastically improve performance and minimize cost. And the third, a smart layer caching strategy. Structure your DOC files to maximize build cash hits. This means placing stable dependencies like system packages at the beginning of the file and frequently changing code like your model artifacts towards the end. Together. These patterns significantly reduce image size and build times accelerating then entire development cycle. Once we have a optimized containers, we need a way to manage the mat scale. This is where Kubernetes comes up. A Kubernetes provides an orchestration layer that makes our contained rise. AI deployments truly production ready kuban it is. Gives us powerful capabilities like horizontal part auto scaling. This allows us to automatically scale our inference services based on CPU Memory, our even custom metrics, like the number of prediction requests per second. This ensures we have the capacity to meet demand without over provisioning resources for our expensive hardware. We can use GPU, no pools and resource quotas. This allows us to efficiently and fairly allocate these costly GPU resources across multiple teams and workloads, ensuring maximum utilization. Finally, Kubernetes enables advanced deployment strategies like can redeployments. We can gradually roll out our new model versions to a small subset of users, monitor their performance. And then proceed with a full rollout, significantly reducing the risk of a bad deployment. Now let's move to an next pillar infrastructure as a code or I-A-C-I-A-C transform your AI platform and from a fragile, manually configured snowflake system into a reproducible version, controlled and automated one. The key benefit is environment parity. With IEC, you can create identical deployments, development, testing, and production environments with a click of a button. Eliminating the it works in dev surprises. IEC is also your key to boost disaster recovery. If an outage occurs, you can rebuild the entire platform, reducing downtime from days to hours. It also ensures consistent compliance and governance by allowing you. B to implement security controls and access patterns as code applied uniformly across all departments. Terraform patterns for AI infrastructure is very important when implementing IESE for ai. Terraform is a fantastic tool. Let's look at three battle tested Terraform patterns. First, a module based architecture. Instead of writing monolithic configurations, define reusable infrastructure modules for common air components like model, race, inference, clusters, or feature stores. This promotes consistency, accelerates development, and simplifies maintenance. Second, a clear environment pro promotion strategy. Use separate directories or terraform workspaces to manage your different environments. This ensures that. Identical configurations are applied consistently. As you promote changes through your CI slash CD pipelines, minimizing discrepancies and critically use remote state management. Use your Terraform State files in a remote shared location, like an S3 bucket, or Google storage with locking enabled. This is crucial for team collaboration, prevents corruption and maintain an authoritative record of your infrastructure. Configuration. Always ensure encryption and versioning are enabled for security and auditability. This brings us to GitHubs, which ties everything together. GitHubs transform our manual. Deployment processes into fully automated auditable workflows by making GI the central hub of our operations. The key component are simple but powerful. Git becomes a single source of truth for both infrastructure and applications configurations. We use pull based deployment operators like Argo CD or Flux that automatically sync the cluster state with what's defined in gi and we have continuous verification. That detects and remedies and drifts from the desired state. The enterprise benefits are immense. We see an 80% reduction in deployment time and a complete audit trail for compliance, self-healing architecture that maintains its desired state and simplified rollbacks, just you just revert a get command. Simple. So what does this all mean for your business? The return on investment is tangible and significant organizations adopting these patterns have seen an 85% reduction in deployment time. For complex AI workloads moving from weeks to hours, they have experienced 73% fewer production incidents due to environment, consistency and automated testing. We have achieved 40% infrastructure cost saving through better resource utilization and auto scaling. And perhaps most importantly, they have been able to get three times more models into production, dramatically increasing the throughput and impact of AI delivery pipelines. I want to leave with a clear, actionable plan to get started on this one. Here's a 30 day plan for some quick ones. Start with by contain rising one high value AI workload using multi-stage docker build. Establish basic Terraform modules for your key infrastructure components, and set up GitHub's repository to manage configurations and automate your initial deployments. From there, you can accelerate towards production readiness within 90 day transformation plan. Implement Kubernetes clusters with GPU support. Introduce candry deployment strategies for safer model updates, and set up comprehensive. Monitoring and observability solutions tailored for your AI workloads to ensure ongoing performance and reliability. By embracing these principles of containerization infrastructure as code and GitHubs, you are truly move from container kiosk to a production paradise. You can build AI platforms that are not just powerful, but also reliable, scalable, and bulletproof. Thank you so much for your time, and happy to answer any questions you have. Thank you for this.