Hybrid Cloud & Generative AI Architecture: Accelerates the Business Outcome
Anil Patil, MBA
Executive Architect | Technology Leader | Solution Leader for Hybrid Cloud & Generative AI
Background:
?The biggest issue today is that most technical architectures are fit for purpose, and it lays towards the technical decision vs business decision with product centric approach.
Generative AI is a good example of a technology today’s architectures weren’t designed to support. Traditional architecture inhibits, rather than optimizes, what gen AI can do. Information is locked away in isolated databases, starving generative models of the rich fuel they need to learn and create. Fragmented workflows slow down the training and deployment of generative AI models.
1.??Technical architecture is not any more technical debt, it's a business outcome that drives the revenue for companies
2.?Generative AI is becoming part business outcome and not technical driven
3.?Future architecture is enterprise platform ready
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Architectures built without a clear business vision are often brittle and prone to failure. They struggle to adapt to changing needs and demands of business.
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1.????? Develop a library of business-driven architecture patterns
Not every business problem requires its own unique architecture solution. Imagine a world where your team isn’t reinventing the architecture wheel (or the API) every time they face a business challenge. Developing a library of patterns can help teams work faster as they create architectures that address various aspects of the business. Pre-defined patterns accelerate development by providing reusable building blocks. It will also help eliminate the temptation to build with a tech focus versus a business-outcome focus. The library itself becomes a breeding ground for innovation. New patterns emerge by combining existing ones, leading to creative solutions for complex business challenges. Architecture by design means designing around business needs, not the hottest new technology. The new tech is just a tool to help get you to the business outcomes. But many organizations don’t use that script. Here’s how to begin to flip it:
2.????? Data Governance and Strategy
As you create new architectures, your data strategy needs to keep pace to gain full benefits from it. Data agnosticism allows you to leverage the strengths of different architectural patterns for specific data types, increasing the speed of your data-driven decision-making.
Modern businesses rely on a variety of data sources: structured, unstructured, real-time, and historical.
Evolving your data strategy to mesh with your architecture allows you to leverage different patterns for different data types, maximizing the value you extract from all your data assets. For each architecture pattern, identify the data patterns that are best suited for that pattern. For example, monolithic architecture might work best for structured data for efficient storage and retrieval. Microservices could handle real-time data for event-driven communication. Event-driven architecture works for unstructured data for event processing and analysis. And serverless architecture could be used for historical data for batch processing and analytics.
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3.????? Automation is key for Architecture and Design decisions
The future belongs to architects who can leverage AI to build so they can imagine. We’re seeing the effect that gen AI can have on software development. Developers are freed from repeatable coding tasks and can pursue higher-order design product design work. The same kind of “assistant” use case works for architects. Engage tech architects in training small-model gen AI on the massive amount of architectural documentation your enterprise creates. When the AI assistant can generate routine architectural documentation, architects can spend more time improving customer experiences. Embracing AI doesn’t diminish your role as an architect; it amplifies it. Focus on the strategic aspects of architecture—designing systems that not only function flawlessly but also propel your business towards future success
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4.????? Product centric development approach
Hybrid by design allows you to train core AI models on-premises, ensuring data privacy, while leveraging the cloud for rapid deployment of new features and A/B testing based on user feedback. This rapid iteration cycle fuels product-led development, with real-time user data, a key driver of growth. Imagine a retailer revamps its mobile app with a focus on product-led development powered by AI. The app utilizes a core AI model trained on-premise with anonymized customer data. As customers browse the app or physical
stores, the AI analyzes their behavior in real-time, offering personalized product recommendations. The AI generates dynamic shopping lists based on a customer’s needs and past purchases. Additionally, the AI can present targeted promotions and coupons for relevant products, driving impulse purchases and increasing average order value.
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5.????? The power of decision and choice
The biggest benefit of intentional hybrid architecture is flexibility. Businesses can choose the optimal environment for each stage of the AI lifecycle. Need to train a massive language model? Tap into your on-premises powerhouse. Experimenting with a new computer vision application? The cloud’s your better bet. It’s like having a private supercomputer for core tasks, and a
limitless playground in the cloud to test and refine your AI ideas at warp speed. This agility gets your AI innovations to market faster, giving you a first-mover advantage.
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6.????? Security at every layer
?Hybrid architectures help businesses keep sensitive data and core applications safely guarded within their own walls, while at the same time leveraging the cloud’s robust security features for additional protection. This layered approach minimizes risk, and fosters trust with customers and regulators. Think of it as a double layer of defense, keeping your core AI models under lock and key, while the cloud scrutinizes every update before it goes live.
?Codify and enforce consistent architectural principles. Architecture becomes real through hundreds of day-to-day decisions, so define a complete framework of architectural principles as well as the governance required to make and enforce implementation decisions. Without clear architectural principles, teams risk creating a patchwork of technologies, leading to inefficiencies, security vulnerabilities, and maintenance nightmares. Gen AI architecture requirements should be meshed with decisions about security, data sharing, and development platforms. Zooming out a bit, those technology decisions also need to be in sync with decisions about integration and product-led design, which need to be aligned with overall business objectives
For more detail, please refer to the link below - Hybrid by Design and business outcome.
About the Author
Anil Patil has 26 years of IT experience as a distinguished architect and solutions thought leader. He has worked in the banking, financial, insurance and telecom industries. Anil has developed and innovated different solution patterns, Gen AI use cases, and prototypes using different assets, tools and technologies in large and complex deals and projects. He has also published several books, blogs and publications. Anil holds a bachelor’s degree in electronics engineering and a master’s degree in finance and strategy from Rutgers Business School in New Jersey. For more information, email [email protected]
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