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

Understanding Proof of Concept in AI Projects

Introduction to Proof of Concept (PoC)

• AI projects often face uncertainty, making it difficult to determine the viability of ideas. A proof of concept (PoC) can help mitigate risks before significant investments are made.

• This episode is part of the "AI for Business" course, focusing on how AI applies in real-world scenarios and the importance of PoCs.

Importance of PoCs

• Three main reasons for conducting PoCs:

• Testing: Quickly answer critical questions about data availability, skills, business outcomes, and costs.

• Feedback: Gather early input from stakeholders to ensure the solution meets their needs and increases adoption.

• Production Planning: Understand complexity, time, effort, and cost involved in developing a full production system.

Elements to Consider When Planning a PoC

• Ten key elements include:

• Problem

• Hypothesis

• Scope

• Success Criteria

• Data

• Modeling and Tools

• Infrastructure

• Deliverables

• Team

• Time

Example Use Case: Automating Industrial Visual Inspection

Identifying the Problem

• The problem addressed is manual inspection of industrial products which is time-consuming and costly.

Formulating the Hypothesis

• The hypothesis posits that AI can automate defect detection in products leading to faster inspections and reduced costs.

Defining Scope

• The scope should be limited; for instance, focusing on specific product types or geographical areas helps manage complexity. In this case, it targets four defect types using a dataset of adapted images.

Establishing Success Criteria

• Clear success criteria are vital; they may include metrics like accuracy (e.g., aiming for at least 70% precision), speed (e.g., inspecting images under one minute), or cost reductions.

Data Requirements

• The required dataset consists of annotated images indicating defects. For this example, a set of 5,000 images will be used for training purposes.

Modeling Techniques and Tools

Exploring Custom Model Training and Infrastructure Requirements

Infrastructure for Deep Learning Models

• Amazon Recognition and custom model training can be explored using deep learning frameworks like PyTorch. The infrastructure element defines the necessary hardware, storage, and compute resources.

• For custom model training with frameworks like PyTorch, a powerful on-premise or cloud virtual machine is essential to handle data set complexity and training processes.

• A robust GPU is required for deep learning model training; an example of a suitable virtual machine is the Amazon EC2 P3 2x large instance optimized for high-performance computing workloads.

• The Amazon EC2 P3 instance features a single Nvidia V100 GPU, 8 vCPUs, 61 GB memory, and 1.5 TB NVMe SSD storage, making it ideal for compute-intensive tasks.

• When adopting a custom model development methodology versus using cloud-hosted AI services, understanding the deliverables beyond just the trained model is crucial.

Deliverables from Proof of Concept (PoC)

• Key outputs from a PoC include code files for all stages such as data sourcing, cleaning, processing, and feature engineering to facilitate future development.

• Documentation detailing how components interact within the PoC is necessary along with summaries of outcomes achieved during the process.

• Knowledge transfer may be needed if external consultants assist in developing prototypes; this ensures internal teams can continue work effectively post-PoC.

• A prototype application might be required to interface with the model for testing purposes; options include web interfaces or mobile applications that allow user interaction with the model.

• Recommendations on scaling from pilot experiments to production systems are critical learnings from PoCs that guide future implementations.

Specific Deliverables in Industrial Visual Inspection Use Case

• In industrial visual inspection cases, primary deliverables include a convolutional neural network (CNN) capable of detecting defects in products alongside other essential outputs.

• Additional deliverables consist of an efficient data pipeline for preprocessing images into formats suitable for training models while allowing updates with new data over time.

• A comprehensive report should detail model development including dataset descriptions, architecture specifications, hyperparameters used, evaluation metrics employed during testing phases.

• Challenges faced during PoC execution must be documented along with recommendations for improvements based on findings throughout the project lifecycle.

Proof of Concept Planning for AI Solutions

Key Elements for Proof of Concept (POC)

• The speaker emphasizes the importance of including 10 essential elements when planning a proof of concept, whether conducted in-house or outsourced. These elements are crucial for structuring the POC effectively.

• Four examples of POC plans are provided, including a generative AI use case, which can be found in the video description. These examples serve as valuable references to understand methodologies better.

Critical Questions Post-POC

Evaluating AI as a Solution

• The first critical question is whether AI is the right solution for the problem at hand. While AI has significant capabilities, traditional techniques may sometimes offer more efficient solutions.

• After testing machine learning techniques during the POC, it’s vital to assess the return on investment (ROI). Significant returns should be quantified (e.g., 2x, 3x), considering metrics like speed and customer experience.

Expected Accuracy Levels

• The second question addresses the expected level of accuracy improvement from POC to production systems. For instance, if a model achieves 70% accuracy during POC, further investment could potentially raise this to 85% or 90%.

• Depending on use cases—like self-driving cars requiring near-perfect accuracy versus industrial applications that might accept lower thresholds—decisions about further investments can be made based on these projections.

Analyzing Poor Results

Reasons for Insufficient Outcomes

• If results from the POC are unsatisfactory, it's important to identify reasons and determine if another round is warranted or if moving to production makes sense.

• Three main reasons for poor outcomes include:

• Insufficient data

• Insufficient skills

• Working on an incorrect problem

Addressing Data Issues

• Insufficient data may require additional datasets or historical data spanning several years to improve predictive power. For example, forecasting retail demand might need weather and promotional data alongside sales history.

Skills Assessment

• If skills are lacking within the team—especially in specialized areas like computer vision—it may hinder results. Hiring consultants or partnering with experienced technology firms can help bridge this gap.

Problem Re-evaluation

Understanding the Role of AI in Problem Solving

The Limitations of AI for Certain Problems

• Many problems can be effectively solved using traditional methods rather than forcing AI solutions, which may not always be necessary or efficient.

• Organizations often rush to implement AI due to excitement or mandates, overlooking simpler tools that could address their needs more efficiently.

Evaluating Tools and Models for Production Systems

• A critical question is whether to use off-the-shelf models/services or build a system from scratch; this decision impacts the production system's complexity and cost.

• The proof of concept (PC) phase allows exploration of various tools and services available in the market, including cloud AI services and open-source options.

Weighing Pros and Cons of Different Approaches

• Building from scratch offers control over workflows but requires high technical skills and time investment; off-the-shelf solutions save time but may lack flexibility.

• Understanding available solutions based on PC efforts helps assess the complexity, expected time, and budget for a production system.

Cost Considerations for Production Systems

• It's essential to estimate total costs associated with building a production system beyond just model accuracy; this includes ETL processes, software engineering, infrastructure costs, etc.

• Transitioning from a limited scope in PC to an enterprise-level solution necessitates addressing various operational aspects like data integration and automation.

Maintenance and ROI Assessment

• Continuous maintenance is crucial as model performance can degrade over time; organizations must plan for retraining and redeployment.

• Assessing costs against expected returns on investment (ROI) helps determine if proceeding with the full-scale production system is justified.

Planning Next Steps After Proof of Concept

• The insights gained from the proof of concept should inform decisions about scaling up operations while considering potential adjustments to reduce costs where necessary.

• A well-defined business case based on PC findings aids in justifying investments needed for developing a robust production system.

Future Topics: Building AI Capacity

Exploring Team Structures vs. Outsourcing

• Upcoming discussions will focus on building effective AI teams versus outsourcing options, examining pros and cons associated with each approach.