Automating Your Business

Automating Your Business

A place to discuss best practices and methodology around process discovery and modeling, decisions, and content management as well as practices to truly transform your business with design thinking, Agile, and artificial intelligence (AI).

 View Only
Back to discussions
Expand all | Collapse all

Inside the AI Tech Stack: Layers, Components and Emerging Trends

  • 1.  Inside the AI Tech Stack: Layers, Components and Emerging Trends

    Posted 20 hours ago
    Edited by Pratik Mistry 20 hours ago

    Over time, AI technology has experienced a quantum leap from novelty to an essential aspect for different businesses. And with a significant increase in AI development, businesses nowadays are keeping pace with the emerging AI tech stack, also ensuring the adoption of the latest technological trends.  

    And as per a report by McKinsey Global Survey,  AI adoption is gaining immense traction, rising from around 50% over the past six years to 72% in the year 2024, emphasizing a boost in global interest and implementation. As more and more businesses embrace AI, having a robust tech stack has become imperative for maintaining an upper hand over competitors. AI tech stack also ensures seamless development and high-performance maintenance in AI applications. 

    Well, in this article, I'll help you explore the fundamental yet significant aspects of the AI tech stack that allow the generation of new data based on datasets. 

    What is AI Tech Stack? 

    An AI tech stack is an extensive set of tools and frameworks to build, leverage, and maintain AI systems. And like every traditional technology stack, an AI tech stack comes with multiple layers. Each works for a specific objective and integrates with others to build a functional system. For instance, these layers often acquire deployment infrastructure, model development, data processing, and many more. 

    The significance of the AI tech stack is all about its specific capabilities to streamline the AI development process. When choosing your ideal tech stack, you can maintain efficiency, scalability, and consistency in your AI projects. Furthermore, incorporating diverse tools and technologies helps your team prevent limitations. In layman's terms, a robust AI tech stack is a cornerstone for developing paramount AI applications. 

    AI Tech Stack Layers 

    Let's break the AI tech stack down into its layers: 

    • Application Layer - This is the major component of software applications. It covers aspects like app accessibility, front-end development, UI, UX, and more. 

    • Backend Layer - Also known as the "server-side," it helps manage the application logic, which includes database connection, application programming interface (API) setup, security, and app authentication. 

    • Data Layer - All the data from user and system interaction with an app needs storage alongside any business information. The data layer has tools to manage data storage, retrieval, backup, and management. 

    • Operational Layer - Applications are deployed into a production environment, where you must consider factors like optimization, management, automation, update schedule, and maintenance. The tools in this layer fall under the umbrella of "DevOps" or development operations.

    Critical Components of AI Tech Stack 

    The following are the critical components of the AI tech stack: 

    • Programming Language - The language developers use to build the stack components, including the AI application's source and integration codes. 

    • Model Provider - Businesses that offer seamless access to foundation models via inference endpoints or alternative means. Foundation and embedding models are typical models majorly used in generative AI applications. 

    • LLM Orchestrator and Framework - A library that abstracts the challenges of incorporating components of modern AI apps by offering integration packages and methods. Components acquire operators that provide tools to build, modify, and handle prompts and condition LLMs for multiple objectives. 

    • Vector Database - It's a data storage solution for vector embeddings. Operators within this component come with features that help manage, store, and effectively shift through vector embeddings. 

    • Operational Database - It's a data storage solution for operational and transactional data. 

    • Monitoring and Evaluation Tool - These tools help track AI model reliability and performance and offer real-time analytics and alerts to optimize AI applications. 

    • Deployment Solution - These services help easy AI model deployment, which manages scaling and incorporation with existing infrastructure. 

    A Successful AI Tech Stack 

    It is significant to opt for an ideal technological stack in order to build reliable artificial intelligence systems. Let's see what perfect tech stack we should acquire. 

    Cloud Resources: 

    • You require vast amounts of processing power and storage for generative AI models. 

    • The scalability of cloud infrastructure ensures that AI solutions can adapt to workload changes without experiencing outages and compromising productivity. 

    Machine Learning Frameworks: 

    • Keras, PyTorch, and TensorFlow provide APIs and tools for building, improving, and verifying machine learning models. 

    • Frameworks are the technology stack's cornerstone, allowing customization to improve metrics like F1 score, recall, and accuracy. 

    Data Manipulation Utilities: 

    • Imputation, encryption, and normalization are common processing steps required for raw data. 

    • Software like Apache Hadoop and Apache Spark offers efficient solutions for large-scale data processing. 

    Programming Languages: 

    1. Python's accessibility and comprehensive package repository make it a prominent machine-learning language. 

    1. In particular, Julia and R are utilized for high-performance computing and statistical analysis. 

    Significant Phases of Modern AI Tech Stack 

    As you might know, working with any AI solution can be challenging. Hence, a systematic approach helps streamline this process, allowing you to seamlessly develop, deploy, and scale your AI systems. So, let's now explore these phases to understand their significance. 

    Phase 1: Data Management Infrastructure 

    Data is the heart of AI abilities, and efficient management is imperative. And to collaborate with it correctly; this phase includes multiple stages to gather, organize, store, and process data. It makes the data ready for evaluation and model training. The stages include: 

    Stage 1: Data acquisition 

    Stage 2: Data transformation and storage 

    Stage 3: Data processing framework 

    Stage 4: Data versioning and lineage 

    Stage 5: Data surveillance mechanisms 

    Phase 2: Model Architecture and Performance Metrics 

    Modeling in AI and machine learning is a consistent and challenging process. It does not include choosing an ideal algorithm but also considers computational limits, data security, and operational requirements. Enlisted are some aspects worth exploring after you complete the first phase. 

    Stage 1: Algorithmic paradigm 

    Stage 2: Development ecosystem 

    Stage 3: Tracking and replication 

    Stage 4: Evaluation metrics

    How to Choose Ideal AI Tech Stack?  

    Before choosing your ideal AI tech stack, it's essential to keep the following factors in mind: 

    1. Technical Specifications and Functionality - Deciding on a project's technical functionalities and specifications plays a significant role in choosing an AI technology stack. 

    1. Information Security and Compliance - A secure data environment is foremost, specifically when managing financial or sensitive data. 

    1. System Scalability - A system's scalability directly influences its adaptability and longevity. 

    1. Competency and Assets - The resources and skills available to the development team are critical in choosing an AI stack. 

    Future Trends in the AI Tech Stack 

    Let's now go through the emerging technologies shaping the future of AI. 

    • AutoML (Automated Machine Learning) - AutoML platforms democratize AI by automating feature engineering, hyperparameter tuning, and model selection workflows. 

     

    • GPT (Generative Pre-trained Transformer) Models – Its advancement will include more sophisticated models capable of understanding context and generating more lucid and contextually relevant responses. 

     

    • Federated Learning - This trend will enable models to be trained across different decentralized edge devices without any requirement to share raw data. 

     

    Moreover, according to statistics, AI is expected to add $15.7 trillion to the global GDP by 2030 and create around 97 million jobs by 2025, underscoring its transformative economic and workforce impact. 

     

    Summing Up! 

    In a nutshell, traversing the AI tech stack is like starting your journey across the infinite worlds of opportunity and creativity. From model deployment to data collecting and beyond, each part of the stack comes with specific issues and opportunities that impact the future of AI-powered systems across multiple sectors.  

    And as we look forward to evolving trends and innovation, it's evident that the increase in growth of the AI tech stack will consistently drive radical change. Hence, the AI tech stack helps businesses to reach new heights of intelligence, efficiency, and value creation. 



Related Content