Hello everyone and thank you for joining. Today I will be talking about platform engineering for data science at scale. Over the past decade, data science has evolved dramatically. What started as individuals analyzing spreadsheets has now become enterprise wide AI projects. These projects need sophisticated and infrastructure and scalable computing and reliable deployment pipe. But here is the challenge. Many organizations invest heavily in tools and talent, yet still struggle to move machine learning from experiments into real business systems. That's where platform engineering plays a critical role. Slide two. The problem isn't usually the algorithms or the data, it's the infrastructure. Traditional approaches often create three main issues. First, fragmented tool genes. Different team use different tools, which makes integration very difficult. Second inconsistent environments. The classic works on my machine problem where models fail once moved into production. Third, deployment bottlenecks. Data scientists build models, but engineering teams struggle to operationalize them. Platform engineering solves this by providing a centralized, scalable, and a standardized foundation so data scientists can focus on innovation, not firefighting. Moving on, let's look closer at the main challenges environment, inconsistency. Models behave differently in development versus in production. Tool fragmentation. Too many tools lead to silos and maintenance, headaches, resource inefficiency. Sometimes too much computing power is wasted. Sometimes not enough is available. Fourth, deployment complexity. Putting a model into production takes too much manual work. Fifth, security and compliance risk Patchwork solutions make it hard to stay compliant. These challenges slow down progress and raise costs. So how does platform engineering help? It follows a few key principles. Obstruction, hide the messy infrastructure details, so data scientists can focus on their work self service. Let teams provision environments and deploy models without waiting for it. Standardization, ensure everyone follows the same patterns and tools. Observability, monitor model behaviors, data quality, and performance at all times. Scalability, make sure the system grows and demands grows. Security by design. Build security into the platform from the start, not after the thought. Most of the most important foundation is containerization, containerizations. Make sure models run consistently across environments, avoid conflicts and even allow multiple versions of a model to run at the same time. Then comes microservices. Breaking big monolithic systems into smaller focus services like data integration, model training or monitoring. Each can be scaled independently together with or orchestration and service mesh technologies. This makes platforms more flexible and easier to manage in cloud environment. Certain design patterns make platform reliable and efficient. 12 factor methodology, a framework for scalable maintainable applications. Even driven architecture triggers training or scalable automatically when new data arrives. Immutable infrastructure, every change creates a new version making rollbacks very safe, circuit breakers and bulk. Protect against cascading failures and keep workloads isolated. Autoscaling expand or shrink resources based on demand saving costs. Machine learning pipelines are the most likely to assemble line of data science. They like raw data, transform it, train models, validate them, and deploy results. A strong pipeline provides orchestration to manage dependencies and scheduling data, lineage and versioning to track every step and ensure reproducibility fault tolerance to recover gracefully from failures. Good pipelines also integrates smoothly with enterprise systems and optimized resource. A data science platform must serve many groups. Data scientists need quick experimentation and easy access to data. ML engineers, they need reliable deployment and monitoring tools. Platform engineers need visibility into performance, cost, and compliance. Security team needs strong control without slowing others down. Business stakeholders need to see results. Metrics like performance speed and ROI. Balancing all of these needs is what makes a platform truly successful. Monitoring is the heartbeat of platform engineering, the monitor infrastructure, server storage, networking application. They monitor latency, throughput, error rates, data. In that we monitor freshness, quality schema, compliance models. What is monitored is accuracy, drift and anomalies. With this data, we optimize performance and set up alerts so issues are fixed before they impact users. The future of platforms is exciting. Edge computing brings model closers to where data is generated. Automated ML reduces manual work in model building. Federated learning trains models across different data sources without moving sensitive data. Multi-cloud strategies improves resilience and flexibility. Sustainability is becoming essential. Building platforms that are efficient and environmentally friendly. Platform engineering changes the game. It can triple productivity. Since data scientists are no longer required to deal with infrastructure headaches, it can cut deployment time in half, moving models faster into production. It can double the innovation because teams share knowledge more easily and it can reduce infrastructure costs by 40%. Through efficient resources, it's a real shift from fragmented systems to a streamlined, scalable platform. The key benefits, the benefits are both technical and strategic. Democratization making advanced tools available to more people. Reliability, reducing risk in production. ML system. Collaboration, helping teams share models and insights instead of duplicating work cost optimization, using resources more wisely, future ready, being able to adapt quickly to new technologies. The implementation roadmap. So how do we actually build this? Here is the roadmap. Assessment. Identify pain points and a plan. Foundation, build core components like containers and orchestration, platform development, create workflows and self-service tools, operational excellence, add monitoring and optimization. Continuous evolution keep improving with feedback and new technology. To wrap up platform engineering is more than a technical fix. It's a strategic investment. Yes, it takes planning and commitment, but the payoff is huge, scalable, reliable, and efficient platforms that give companies a real competitive edge. In an AI driven world, the organizations that embrace this will move faster, adapt better, and ultimately win. Thank you for listening. I would be happy to answer any other questions that anyone has.