Good morning everyone. Today I'm excited to explore with you how platform engineering principle works, which are critical in modern software development, can draw powerful lessons from industry that has mastered this complexity over a century. I'm talking about automotive manufacturing. If you take a step back, look at the evolution of automotive industry. We see more than just rise of cars. We see birth of model design. Standardized platform and assembly line production. All strategies designed to handle scale variability and complexity. This principle have enabled manufacturer to produce millions of vehicle efficiently with shared component across models while maintaining high level of quality and customization. Sounds familiar, right? It should. These are exact challenges we face in software engineering today as a software team Deals with. Sprawling microservices, distributed system and cloud native architectures. We are in many way trying to solve the same problem, how we manage, integrate interdependent system in a repeatable, scalable way. Platform engineering is our response. Building reusable framework, self-service tools and standardized environment to reduce friction and accelerate development. Just like car manufacturers created common chassis platform to serve multiple car lines, we are building internal developer platforms to serve multiple product teams. In the next few minutes, we will look at what software can learn from the shop floor. Principle like modularization abstractions, assembly line automation, quality gates, and separation of concern, and how they apply directly to the platform we are building today. So let's dive in and uncover how the legacy of automotive manufacturing can drive innovation and modern platform engineering. Thank you. Today I want to take you on a brief journey, a journey from traditional car out manufacturing to a platform thinking and show how this evolution closely mirrors the challenges we face in software engineering today. In the early days of automotive, manufacturing, every car model was essentially a one of effort. Each vehicle line require custom components, tooling, and tailored assembly lines. This led to siloed expertise, massive duplication of effort and scaling operation that were not only expensive, but painfully slow. Sound familiar? Many of us in a software have lived this. Team building similar features from scratch, maintaining separate system that do not, they do the same thing and struggling to scale product without scaling cost and complexity. But then something changed in automotive industry. They underwent a transformation shifting towards modular component piece architecture. Instead of designing every car from ground up, manufacturers started to using shared platform. Common chassis, interchangeable parts and unified process that supports multiple vehicles lines. This shift didn't just reduce cost, it improved quality speed up innovation, and made scaling sustainable. And this exactly the mindset we are now adopting in software, what we call platform thinking, just like auto manufacturer. Moved away from bespoke assembly to modular platform software team building, shared services, reusable components, and internal developer platform that supports a variety of product needs without reinventing the wheel. Every time this shift changed how we approach system design. How we allocate resources and how fast we can innovate because we are no longer solving the same problem multiple time in isolation. So as we continue to our journey into platform engineering, let's take a page from Automotive Playbook Build Once. Reuse often and design with scale in mind moment to appreciate how modularity has transformed not only the way cars are built, but also how we design software platform today in automotive industry, modularity isn't just about parts, it's about architecture. It's about structuring complexity so that innovation become manageable, scalable, and fast. And that starts with the first principle, separation of concern. Leading automaker separates their vehicle architecture into three main condos, structural elements, powertrain system, and user facing feature. For example, the team designing chassis of a car doesn't need to be on infotainment system team. Each group works independently, but because of well-defined interface, everything fits together. Whether it's a sedan, SUV or electric variant, this is similar how the separate concern in platform engineering white with dedicated teams for infrastructures, backend service and frontend expertise, all operating independently but harmonizing through a clear API contracts. The second principle is flexible foundation. Instead of designing entirely different vehicles for electric and gas engines, companies like Volkswagen uses what's called MQB platform. A shared architecture that supports both power trains, whether it's traditional combustion engine or fully electric, the same platform can be support both thanks to standardized mounting points and modular design. Now, think of software platform that supports both container based workloads and serverless function, the runtime. Environment differ greatly, but developer inter interact with both using unified API layers. Just like the NQB platform, the underlying mechanics may differ, but the mounting points or abstraction layer keeps the developer experience consistent. And finally, interface standardization. Today's vehicle contain hundreds of electronic control unit. Controlling everything from breaking to climate system. This CS needs communicate reliably and they do so using standardized protocols like Canvas and Flex Three. Without this braking system from one supplier might not talk to a traction control system from another. In software, we saw this with standardized communication protocol. Whether it's A-G-R-P-C-R-E-S-T, or event buses, this protocol allows independent microservices or components often developed by different teams to interpret seamlessly. So whether you are building a vehicle or a platform, the principle are same. Separate concerns to unlock innovation and design, flexible foundation for future growth, standardized interface for insure. Interoperability. That's the architecture of automotive modularity and blueprint for scalable platform engineering. Let's talk about strategy, balancing act, component standardization, and how the automotive industry offers us powerful playbook for applying it in a software platform. Engineering. When we hear standardization, it's easy to think that uniformity everywhere, but in reality, the smartest manufacturer don't try to standardize everything. Instead, they focus on the area where standardization delivers the greatest impact without stiffing differentiation, where its matters most. Take the automotive world component, like chassis frame, suspension. Mounting points and crash protection system are often standardized across multiple vehicles models. Why? Because this elements require heavy engineering investment. Strict regulatory compliance, and yet they are rarely influenced the customer buying decision. You don't choose a car based on whether the rear suspension bolts are proprietary, but you do care about interior experience, the driving field and infotainment feature, and that's where manufacturers preserve room for differentiation and innovation. Now let's bring this thinking into world of software platform. Not all services in. Tech stack needs to be unique to everything. For example, authentication, logging, monitoring, and development. Pipelines are like chassis and suspension of a digital platform. They're critical, complex. And offer little value when rebuild differently by every team. These are the perfect candidates for standardization, shared, well maintained components that everyone can rely on. On the other hand, services that directly affect user experience or enable rapid product experimentation such as recommendation engines or UI level personalization may benefit from more flexibility. That's where team needs to f. Freedom to innovate and differentiate. Let's take a real world example. Imagine a company building both for delivery app and a right hailing app. You could standardize a payment processing system across both. It's core infrastructure, just like chassis, but driver matching algorithm or the order tracking interface. That's where each product needs a room to innovate based on its unique user journey. So the lesson is simple but powerful, standardized where it saves effort without sacrificing identity. Customize where it's add value and drive differentiation. That's the art of component standardization, and it's just essential in the software as it's on the assembly line. I want to highlight and often overlooked, but absolutely critical aspect of platform engineering, governance and component versioning. And once again, the automotive industry offers a masterclass in how to manage complexity scale and long-term stability. Let's start with architecture review board. In automotive world when a new vehicle model is developed, it's not free for all manufacturer established architectural review board and component approval process that ensures every part from a braking system to 14 minute unit aligns with a broader platform strategy. This cross-functional teams are responsible for maintaining platform coherence. Across dozens of product lines and suppliers. In software, we had same kind of discipline. For example, an internal platform team might manage a shared API gateway or authentication services. With our proper review process, team could introduce breaking changes, duplicate efforts, or drift from enterprise standards. A technical governance group helps ensure the platform evolution. Remains intentional, scalable, and safe. The second principle is long-term versioning. A car isn't something you update every two weeks. Vehicles stay on a road for 10, 15, or even 20 years, which means replacement parts from where updates and newer components all have to remain compatible. Across generation, automotive engineer carefully manage how to comp, how component evolves over time. Because even small chains can create system-wide compatibility issue. Now think about enterprise software platform. Many business critical application in finance, healthcare, or government can't break with every updates. If your platform teams decide to duplicate a shared logging library or update database, API, you must have a clear versioning strategy, backward compatibility plans, and long-term support policy. Because other teams depends on your stability. And finally, stability guarantees. Software teams often take pride in moving fast, pushing weekly updates and trading rapidly. But platform components that supports enterprise workloads often needs to be stable for years, not weeks. Much like cost, safety system can't afford surprises billing platform or identity provider and software must be rock solid. And treated as infrastructure, not experiment. The automotive industry teaches us that governance, long-term thinking and careful versioning aren't constrained. They are enablers for trust, scale, and innovation. So we build our platform. Let's remember governance with intent version with care and guarantee with confidence, because that's what enables sustainable software. Just as it's enabled long lasting vehicles on the road. Let's talk about reality. Every engineering team faces technical depth and how the automotive industry offers valuable lessons on managing it effectively. Car manufacturers have never had luxury to wiping the slate. Screen with each new model legacy platform, decades of investment in tooling training and supply chains, this can't just be tossed aside. Instead, they've developed sophisticated strategies for incremental modernization. One of the most powerful approaches, obstruction layers, rather than replacing entire system engineering design interfaces that allowed new component to integrate with older platform. This preserve existing functionality while enabling forward progress. A strategy that kept factories running supply chain intact and vehicle serviceable long after its release software team applied this exact principle instead of big bang, that risk breaking everything. We can modernize legacy system incrementally. Using APIs, adapters, and platform bridges to integrate new capabilities without destabilizing production system. Watch more automaker measure technical depth, not just by age, but by impact. Looking at the component availability, maintenance complexity. Performance issue and integration friction. Similarly, we should define depth in software, not as old code, but the code that slows us down is hard to maintain or block integration in both industries. The goal is same. Preserve value from the past while building for the future. Explore the fascinating parallels between modern manufacturing automation and the world of DevOps. Walk into advanced manufacturing factory today and you will find one of the most sophisticated automation system on the planet. This assembly lines can. Produce multiple vehicles models on the same line with minimal downtime between configuration. That's the level of efficiency is not an accident. It's the result of decades of investment in instrumentation, robotics, and process design. This is the manufacturing world equivalent of fully automated CI ICD pipeline and software just like lights out manufacturing where production lines runs with little to a no human intervention. DevOps teams aim for system that can build, test, and deploy software automatically. This environment rely on observability. Self-healing mechanism and predictive monitoring, very similar to how factories can use sensors and predictive maintenance to keep machinery running 24 by seven. Another striking parallel is layered quality control in auto manufacturing. Every stage of assembly line includes built-in validation. From checking individual component to full system integration test before the car leaves the plant. It's not final inspection. It's continuous assurance In software, we do the same thing with unit test, integration test and end-to-end validation, and throughout our CICD pipelines to catch issue early and ensure that each build is read road ready before it reaches to users. So whether it's cars or code, the principles are same. Automate what you can test continuously and build for scale without sacrificing quality. In high stake environment, observability isn't a luxury, it's a necessity and a few industry. This better than automotive manufacturing where system failures can lead to a real world accident, product recalls or massive financial loss. Let's look at how. Their approach offers powerful lessons for software teams managing mission critical system. First, consider predictive maintenance. Modern engine management system monitor everything from commercial efficiency to component weir by analyzing pattern in a real time. They can predict. Whether the part will fail long before it actually does this prevents breakdowns, reduce downtime and save cost. In software we use same approach with health checks, usage trends, and anomaly detection. For example, a payment platform might monitor API latency or error rates or a flag issue before customer feel its impact. Ensuring uptime and trust. Second there incident response. When fault occurs in vehicle, say a brake system failure engineer, don't just patch it. They diagnose the root cause. Provide temporary mitigation and communicate clearly. Affected customer all while ensuring safety is in compromise. This mirror how DevOps teams should handle major incident. Server crash isn't just about already starting it. It's about diagnosing the underlying issue, mitigation impact, and working towards the permanent fix with transparency for stakeholders. Finally, think about Fleet Elementary. Connected vehicles now send performance data back to central system, helping manufacturer detect patterns, improve reli reliability and shape future models. This exactly what we do with observability platform like Datadog or. We stream logs, metrics, and traces to understand how our system behave in production. Not just to detect problems, but to drive continuous improvement in both cars and code. One principle holds true. You can't control what you can't observe. To understand stakeholder alignment and organizational structure, we would like to take a real world example. Take Ford Motor Company as an example. Traditionally, historically, Ford operates with dedicated engineering teams focused on a specific vehicle lines. For example, one team exclusively responsible for Ford F-150 pickup, another for Mustang sports car and so on. Each team handled all aspects of design, engineering, and production for their models. While this allowed for a deep focus on individual product, it often led to duplicate efforts and higher cost as similar component. Were engineered independently for different vehicles for. If you see the platform structure in four shifted towards modular platform strategy, particularly with its new Ford Global platform approach, this involved creating cross-functional team responsible for shared vehicle architecture and component such as chassis, powertrain, and electronics that can be used across multiple models and even across different vehicle types, trucks, SUV, and cars, for example. The F-150 and Ford Ranger now share a significant portion of their underlying platform elements. The dedicated platform teams manages this shared component, ensuring consistently consistency and quality while product team focus on the model specific feature styling and customer experience. Why it matters. This transition helps forward reduce engineering redundancy, cut cost, and speed of the innovation, while still maintaining the unique identity and appeal of each vehicle. It's also promotes better stakeholders alignment by clearly defining ownership between platform teams and product teams. In software organization, this mirrors the shift to platform teams managing shared infrastructures. Our services, while product teams builds unique feature on top, one of the key factor behind successful innovation in large organization is the ability to build strong internal communities. And foster continuous knowledge sharing this automotive industry offers a great example of how this can be done efficiently. First, many manufacturer established center of excellence, specialized team that develops deep expertise in critical areas like electric power trains, our autonomous driving system. This team don't just innovate, they also act internal consultant, guiding other teams across the company and setting technical standards. Second comprehensive training program. Play a vital role engineer, participate in certification processes and ongoing education initiatives to ensure consistent high level of expertise across the organization. This helps avoid knowledge, silos, and keeps the workforce up to date with the latest technology. Third automotive companies host internal conferences, technical forums, and expertise sharing program. This event build vibrant communities of practice around platform technology, encouraging collaboration, and the rapid spread of best practices. Software platform teams can adopt this same strategy by creating center of excellence for areas like cloud infrastructures or security, establishing structure, training parts, and hosting regular forums or tech talks. Platform teams can drive wide adoption of and a deeper understanding across large, large organization. Building this community, not only scale platform success, but also foster a culture of continuous learning and collaboration. Managing versioning across multiple product lines is one of the most complex and critical challenges in automotive platform engineering. This complexity offers powerful lessons for software team supporting diverse application first. Effective dependency tracking is essential. Automotive engineers use sophisticated tools to map out how component interact across different vehicles models each with its own development timeline. This helps team understand the ripple effect of changes that avoid unexpected conflicts. Similarly, software teams need strong dependency management to coordinate updates across multiple service and products. Second, backward compatibility is non-negotiable requirement. Vehicle component often have to support multiple generation of protocols and interfaces to ensure Part three means interchangeable. System functions seamlessly. The drives careful design decision that prevents costly disruption in software. Backward compatibility, ensure team to deploy, update without breaking existing clients or service. Third, coordinated release are vital when a shared component is updated. Engineer must care. Carefully assess its impact across all product lines and develop strategy that minimize disruption. This often means phase rollouts, fallback options, or clear migration path practice that software teams also need to adapt to maintain system stability. Finally, extensive compatibility testing. Ensure that the sun. And ensure that update. Don't introduce regression or new bugs in automotive. This might mean validating component across dozens of vehicles variance before approval in software. Automated test and stacking environments serves a similar purpose in both domains. Mastering versioning management is the key to maintain reliability, enabling innovation and scaling complexity. Without Kio, the automotive industry especially. Here in United States provides a fascinating insight into sophisticated manufacturing process that closely parallel modern software development strategies. Take Four Motor Company, for example, when four introduce a new component into product lines, whether for F-150 or Mustang, it doesn't simply swap parts overnight. Instead, they follow a carefully controlled process of a gradual integration. New parts go through extensive testing. Pilot runs and phase rollouts before full scale adoption. This mirror software practice like blue green deployment are candidate releases where changes are introduced incrementally to minimize risk and catch issue early. Another critical aspect is supply chain management food as a robust system for qualifying supplier continuously monitoring, qualifying, and maintaining backup supply. For Ensure production never falls. This is directly comparable to managing dependency in cloud environments where teams monitor third party services, ensure ses and. Plan of failover or vendor changes in both manufacturing and software. This strategy helps ensure continuity, maintaining quality, and reduce risk during complex change management. By learning from automated process like this software team can improve deployment, reliability and resilience critical for delivering seamless user experience in today's fast pace environment. As vehicle become more like computers on wheels, the automotive industry is undergoing a massive transformation and evaluation of platform engineering is at the heart of it. Take Tesla, for example, a global leader in electric and autonomous vehicle innovation. Tesla has redefined how platforms are built by treating software as a core, not a layer. Its vehicle receives over their updates just like phone rolling out new features, performance improvements, and even safety patches all without a visit to a dealership. This is only possible through a tightly integrated platform spanning hardwares, software and cloud services and AI system. But Tesla is in salon. Companies like BMW Toyota and Volkswagen are building unified EV platforms like VWs, MEB, that consolidate mechanical, electrical, and digital domains into a single architecture. These platforms are designed to support electric drive trains, but also to scale across different models, geographics and service offering. Nowhere is the platform. Complexity more apparent than its autonomous vehicles. Your engineering teams must integrate mechanical system like braking and steering sensor suits, indicate. LIDAR and cameras, AI decision making and cloud-based infrastructures for mapping fleet learning and real time updates. It's a platform engineering challenge that spans multiple domains, physical, digital, and intelligent. For software team, the parallels are clear. As we build multi-domain platforms, integrating data APIs, machine learning, and realtime services, the automotive world, remind us what successful platform requires deep cross-functional collaboration. Flexible architecture and ability to adapt while maintaining core stability. The road ahead is complex, but full of opportunity for those who built platform with scale and agility in the mind. At last, I want to thank you for joining me on this platform Engineering presentation and taking time. Thank you so much. Have a good day.