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Cloud Tech Stack: Layered Approach to Gen AI
Cloud Tech Stack: Layered Approach to Gen AI

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Imagine building a house. One starts with a strong foundation, then constructs the walls, and finally, add the finishing touches. Building a successful Gen AI application follows a similar blueprint. The tech stack for cloud embedded with Gen AI offerings can be broken into three essential layers: the sturdy foundation (infrastructure), the creative core (model), and efficient front (application).

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Layer 1: The Foundation - Infrastructure

Just like a house needs a solid base, Gen AI project requires a robust infrastructure. This is where cloud platforms like AWS, Azure, and GCP come into play. These platforms provide the computational muscle needed to train and run complex AI models. Also, there is need for specialized hardware like GPUs from Nvidia or AMD to turbocharge AI.

Layer 2: The Core - Model

This is where the magic happens. Your GenAI model is the heart of your application. There are three primary options:

  • Pre-trained models: Cloud platforms and open-source communities offer pre-built models that can be fine-tuned for your specific needs.
  • Open-source models: Think of this as building a house from an open-source blueprint. One gets more flexibility, but it requires more effort and expertise.
  • Custom models: one can have complete control but it's a time-consuming and resource-intensive process.

Cloud platforms offer services like Amazon SageMaker, Azure Machine Learning, and Google AI Platform to deploy and manage models efficiently.

Layer 3: The Facade - Application

This is where AI comes to life. APIs and SDKs are the building blocks that connect model to the user interface. Frameworks like React or Flutter help create a beautiful and intuitive user experience.

But remember, security is paramount. It's like installing a robust security system to protect house. Protect data and the generated outputs with appropriate security measures.

Finally, data is the lifeblood of AI. A well-maintained data pipeline is essential for feeding model with the right information. Cloud storage services like Amazon S3 or Azure Blob storage can help  manage data effectively.

Building a successful Gen AI application requires a careful balance of infrastructure, models, and application development. By understanding these layers and their interconnections, Enterprises can create powerful and innovative AI solutions.

To know more about the future of Cloud and Generative AI, read Nasscom’s report:

Cloud and Generative AI: A Synergistic Future


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Current Focus Areas: IT Services, AIOps, 5G, Cloud, Project Management. Also specialises in Application Rationalization, Cost Optimization, Benchmarking, Report writing, and Market Research.

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