Solution Design Template for AI Initiatives

There is growing interest in AI stream and many companies are making it imperative to embrace AI technologies. Use this design template as a guide for your early AI project. Building AI solutions offers valuable lessons and rewarding challenges for many. Sharing some of structured approach.

Motivation

Every AI project starts with a spark of curiosity and a clear understanding of the problem at hand. It's crucial to determine if AI is the right tool for the job and to identify the potential business impact and system capabilities required. We need to understand both the technical feasibility and the business benefits.

Adopting a user-centered design approach is good and continues to be good for AI projects as well. Focusing on the needs and experiences of end-users and using an iterative process to refine the solution based on feedback ensures that the AI application is both useful and user-friendly.

Project Scope and Objectives

Defining the goals and success metrics early on sets the stage for a focused development process. Whether it's improving accuracy, efficiency, or reducing costs, having clear objectives helps in measuring the success of the AI application.

In my experience, setting these metrics upfront has always provided a clear direction and measurable outcomes including some sample input and outputs. Defining the types of data the application will use and the expected outputs is a foundational step. Whether it's text, images, or sensor data, having a clear understanding of inputs and outputs guides the development process.

Engaging with stakeholders and understanding their requirements is crucial. Involving users in the development process ensures that the solution meets their needs.

Shift Left FinOps

Along with the scope and objectives, one of the critical lessons the importance of considering costs early in the development cycle. Estimating initial costs, calculating ongoing operational expenses, and planning for scalability are essential steps to confirm if the solution is providing the results at the right cost point. Implementing Shift-Left FinOps practices with Cloud/AI Financial planning, budgeting, cost monitoring, and optimization to ensure that the project remains financially viable. For example, switching a large language model from 2.0 version to 3.0 version is quickly possible during a project but it can disrupt your planned budget. So avoid any financial surprises.

AI Model and associated tech stack

Selecting the right AI model for your project is crucial, as various models have unique strengths and applications. With a diverse range of options available, including large language models (LLMs) and other types of AI, it's essential to assess each model's capabilities against your specific use case. Experimenting with different models will enable informed decision-making and enhance the chances of project success. Selection of associated tech stack including the vector DBs, the frameworks are very important at this stage.

Initial Research and Experimentation

Specifying the desired outputs and experimenting with prompts on various LLMs is an exciting phase for GenAI projects. Deciding how to orchestrate calls to the model, whether in a single call or a sequence, is a crucial decision. In certain cases, the sequence of calls provided more accurate and contextually relevant outputs while in certain others fetching and feeding the relevant context data was important. This exploration can be done locally on your machine or in a controlled environment if it involves customer data. You can find a lot of custom reusable scripts to run the same query against multiple models. You may revisit some of the earlier stage decision based on this phase's outcomes.

Backend and Frontend Development

Developing backend services to handle inputs and return outputs, along with creating a user-friendly interface, is where the magic happens. Seamless integration between the backend and frontend has always been a priority to ensure a smooth user experience whether it is a project with or without AI. The frontend should also have a mechanism to collect user feedback which can be collected and used to train and enhance the model further.

Model Deployment and Operations

Deploying the chosen model to serve application requests is a significant milestone. Ensuring that the model is optimized for performance and scalability is essential. Like the functional objectives, important to set the performance and scale objectives as well. This determines the end to end response times as well the overall user experience. Collecting user feedback and iterating based on it is a continuous process. This iterative approach ensures that the application evolves to meet user needs better. Regular testing and iteration or continuous testing is essential for an AI application to determine data or model drift.

Addressing security, data privacy and Ethical AI Use is non-negotiable. Ensuring compliance with regulations and ethical considerations is always a priority

Implementation Plan

The AI development workflow includes typically the following items

• Problem definition
• Data collection and preparation
• Feature engineering
• Model selection and training
• Evaluation and optimization
• Deployment
• Monitoring
• Ethical and other considerations.

Implementing MLOps is essential for managing the lifecycle of machine learning models, ensuring their reproducibility, scalability, and maintainability. Key components of MLOps involve tracking experiments, managing workflows, ensuring model and data lineage, maintaining a model registry, and monitoring model performance.

Hyperscalers and AI providers offer AI project templates for easy development and management of AI applications. If you discover useful, repeatable templates or workflows, please share them in the comments. We can discuss them further in the next post.