AI for Business: #5 How to do AI Experiments?

Summary

This episode of the AI for Business course delves into the importance of conducting proof of concept (PoC) experiments for AI projects. It outlines the necessity of testing AI's viability, gathering stakeholder feedback, and planning for production. The script guides through 10 key elements for a successful PoC, using an industrial visual inspection case study. It emphasizes setting clear success criteria, managing data, selecting tools, and assessing the outcomes to inform production planning, ultimately aiming to minimize risk and investment before full-scale implementation.

Takeaways

• 🧐 Proof of Concept (PoC) is essential for AI projects to manage uncertainty and assess the feasibility of an idea before significant investment.
• 🔍 PoCs serve three main purposes: testing the AI approach, gathering early feedback from stakeholders, and aiding in production planning.
• 📝 Planning a PoC involves considering 10 key elements: problem definition, hypothesis, scope, success criteria, data, modeling and tools, infrastructure, deliverables, team, and timeline.
• 🔑 Success criteria for a PoC should be clear and realistic, focusing on improvements over the current state, such as quality, speed, or cost efficiency.
• 📈 Data is crucial for PoCs, requiring a well-defined dataset to train and evaluate the AI model, with annotations where necessary.
• 🛠️ Modeling and tools define the techniques and technologies used in the PoC, which may include cloud AI services or custom deep learning frameworks.
• 💻 Infrastructure needs for a PoC might include powerful hardware for model training, especially when using deep learning frameworks like PyTorch.
• 📑 Deliverables from a PoC should include code, documentation, knowledge transfer, a prototype application, and recommendations for scaling to production.
• 👥 A balanced team for a PoC should consist of data scientists, domain experts, and project managers to cover AI, data engineering, and domain knowledge aspects.
• ⏱ The duration of a PoC should be concise, typically ranging from 3 to 8 weeks, to quickly test the idea with minimal investment.
• 🤔 Post-PoC critical questions include assessing if AI is the right solution, expected accuracy boost, reasons for subpar results, and whether to use off-the-shelf models or build from scratch for production.

Q & A

What is the primary purpose of a Proof of Concept (PoC) in AI projects?

The primary purpose of a PoC in AI projects is to test the feasibility of an AI solution, gather early feedback from stakeholders, and assist in production planning by providing insights into the complexity, time, effort, and cost involved in building a full-fledged production system.

Why are PoCs especially critical for AI projects compared to other types of projects?

PoCs are especially critical for AI projects due to the inherent uncertainty and the need to validate whether AI is the right approach to solving the problem, the availability of required data and skills, and to ensure the outcome serves the business needs effectively.

What are the three main reasons for conducting a PoC?

The three main reasons for conducting a PoC are testing to answer essential questions quickly, feedback to ensure the solution meets stakeholders' needs and increases adoption, and production planning to understand the resources required for a full-scale implementation.

Can you explain the importance of setting clear success criteria for a PoC?

Setting clear success criteria for a PoC is essential as it provides measurable goals to assess the PoC's effectiveness against the current state of affairs. It helps determine if the PoC has shown significant improvement in terms of quality, speed, cost, or other business metrics.

What should be the focus of the scope when planning an AI PoC?

The scope of an AI PoC should be limited and focused on building the model rather than a full production system. It should concentrate on a specific subset of products, geography, or a narrowed-down problem to limit variables and complexity, making the project more manageable and easier to test.

Why is it important to have a team with diverse skills for a PoC?

A diverse team is important for a PoC as it ensures coverage of AI and data engineering, domain knowledge, and project management aspects. This interdisciplinary approach helps in effectively addressing the technical and practical challenges that may arise during the PoC.

What are some examples of deliverables one might expect from a PoC?

Deliverables from a PoC may include a trained AI model, a data pipeline for preprocessing and transforming data, a code repository with source code, a detailed report on model development and evaluation, a prototype application for testing the model, and recommendations for scaling to a production system.

How long should a typical PoC take to complete?

A typical PoC should ideally take between 3 to 8 weeks, focusing on testing the idea quickly with minimal investment rather than spending excessive time on it.

What are some critical questions to ask after completing a PoC?

Critical questions post-PoC include assessing if AI is the right solution for the problem, estimating the expected accuracy boost for a production system, identifying reasons for suboptimal results and deciding on further investment, choosing between off-the-shelf models or building from scratch, and estimating the total expected cost for a production system.

What is the significance of the infrastructure element in planning an AI PoC?

The infrastructure element defines the hardware, storage, and compute resources required for the PoC. It's crucial for training the model and, if necessary, for deploying the model in a prototype application. The choice between cloud services and on-premise solutions will impact the infrastructure needs.

How does the outcome of a PoC help in planning for a production system?

The outcome of a PoC provides insights into the feasibility, potential challenges, and the expected performance of the AI solution. It helps in making informed decisions about the resources, budget, and timeline required for a production system, and in building a business case for the investment.