The Generative AI Development Stack

Key elements to build tailored solutions

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To build GenAI apps using your data, you need a modular, well-integrated stack that powers tailored solutions.

Today I'll cover:

  • The Modern AI Stack

  • Compute and Foundation Models

  • Data: The Fuel for AI’s Engine

  • Deployment: Bringing AI to Life

  • Observability: Watchtower of AI Systems

  • RAG: The Architectural North Star for Today’s AI

  • The Future AI Revolution is still evolving

Let’s Dive In! 🤿

The Modern AI Stack

In 2023, the modern AI stack solidified its position as a generative AI powerhouse, with record-high investments in the industry. Menlo Ventures, identified four critical layers in this evolving AI landscape that I agree with:

  1. Compute and Foundation Models: This foundational layer includes both the AI models and the infrastructure needed to train, optimize, and deploy them. It's the bedrock of the stack, enabling the core functionalities of AI applications.

  2. Data: Serving as the lifeblood of AI, this layer focuses on the infrastructure that integrates Large Language Models (LLMs) with enterprise data systems. It encompasses data preprocessing, ETL (Extract, Transform, Load) processes, and various databases, ensuring that AI models have access to relevant and structured data.

  3. Deployment: This layer is all about bringing AI models into practical use. It includes tools that help developers manage AI applications, ensuring smooth integration and consistent performance.

  4. Observability: The final layer of the stack is dedicated to monitoring and security. It includes solutions that track the behavior of LLMs in real time and guard against potential threats, ensuring safe and reliable AI operations.

Modern AI Stack from Menlo Venture

Compute and Foundation Models

The compute and foundation model layer encompasses the core AI models and the infrastructure required for their training, fine-tuning, optimization, and deployment. Let’s go through each of them:

  1. GPU Providers: GPUs are crucial for processing AI algorithms. They handle complex computations required for model training and inference, with leading providers like NVIDIA and AMD at the forefront.

  2. Foundation Models: These are extensive, pre-trained models (like GPT, BERT) that form the basis for specialized AI applications, providing a broad understanding that can be tailored to specific tasks.

  3. Training: Training involves using large datasets to educate models, enabling them to make informed predictions and decisions. It's a complex process, balancing data diversity and computational demands.

  4. Fine-tuning + RLHF: Fine-tuning adjusts these models to specific needs, while Reinforcement Learning from Human Feedback (RLHF) further refines them based on human interactions, aligning AI outputs with real-world requirements.

  5. Model Deployment: The final step is deploying these models into applications, a process that involves strategic considerations like cloud platforms, edge computing, and scalability.

Data: The Fuel for AI’s Engine

Data is the lifeblood of AI systems. Let’s examine how the Data layer acts as the fundamental fuel driving AI systems, focusing on the integration, processing, and utilization of data for AI applications.

  • Integration and Processing: Essential for customizing AI to specific business needs through the effective collection and transformation of diverse data sets.

  • ETL Processes: Fundamental in preparing data for AI use, these processes standardize data into a usable format for AI analysis.

Steps Required in Data Integration and Processing

  • Vector Databases: Vital for handling complex data, converting complex data (like text or images) into numerical vectors, enabling efficient processing for tasks like similarity searches and pattern recognition, essential for handling large datasets in AI applications.

Converting content into Vectors and storing it in a Vector Database

Deployment: Bringing AI to Life

In the Deployment layer, we focus on crucial elements for launching AI models into real-world applications:

  • Prompt Management: Essential for fine-tuning AI responses to user inputs, ensuring relevant and accurate interactions.

  • Agent Tool Frameworks: Provides tools for deploying AI agents, enabling them to perform tasks autonomously in varied environments.

  • AI Orchestration: Involves coordinating multiple AI models and processes to ensure seamless, efficient operation, crucial for complex AI systems in enterprise settings.

This layer is key to transitioning AI from concept to practical application, aligning technical capabilities with user and business needs.

Steps to deploy AI in production

Observability: Watchtower of AI Systems

The Observability layer in AI systems is crucial for ensuring transparency, security, and trustworthiness, while also facilitating performance evaluation.

  • Observability: Essential for monitoring AI behavior and performance, ensuring transparency, and the ability to fine-tune systems for optimal operation.

  • Evaluation: Involves assessing AI model performance and accuracy to guarantee they meet objectives and provide reliable results.

  • Security: Focuses on protecting AI systems from threats like data breaches and model tampering, crucial for maintaining data integrity.

  • Trustworthiness: Addresses the need for AI systems to be fair, ethical, and unbiased, building confidence in their use and deployment.

Overall, this layer is critical for maintaining the efficiency, security, and ethical standards of AI systems, ensuring their ethical application.

RAG: The Architectural North Star for Today’s AI

RAG (Retrieval Augmented Generation) enhances LLMs by integrating database access, improving accuracy and contextual relevance in AI outputs. More scalable and cost-effective than traditional methods, RAG reduces errors in LLMs, increasing trustworthiness and adaptability across various industries.

  • RAG in Action: Enhances various AI applications, such as chatbots and content generation, by integrating external knowledge for depth and relevance.

  • Advantages:

    • Contextual Awareness: Provides access to vast information, making outputs more relevant.

    • Flexibility and Scalability: Adapts to different domains with dynamic data integration.

    • Enhanced Accuracy: Combines generative capabilities with external data for improved reliability.

  • Future of RAG: Points towards AI systems that intelligently integrate diverse data sources, promising more sophisticated, responsive, and autonomous AI solutions.

In essence, RAG is a transformative element in AI architecture, essential for creating more intelligent and adaptable AI systems. See the architecture below as a simple, yet very powerful example of a blueprint to develop RAG for most use cases.

RAG Solution Architecture

The Future AI Revolution is still evolving

Emerging developments shaping the future of AI include:

  • Advanced RAG Applications: Addressing limitations in current RAG systems with sophisticated techniques for improved accuracy and context understanding.

  • Rise of Small Models: Shifting focus to smaller, task-specific models for efficiency, supported by advanced ML pipeline infrastructure and quantization techniques.

  • Innovations in Observability and Evaluation: Developing automated tools for better AI performance review and reliability, moving beyond manual evaluations.

  • Serverless Architectures in AI: Transitioning towards serverless solutions for AI infrastructure to simplify operations and enhance agility and scalability.

I hope you enjoyed today’s newsletter.

See you again next week!

Armand 🚀

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