Chapter 1: Introduction to AI Frameworks

Developing machine learning or AI solutions can be straightforward when you adhere to a well-defined framework. This approach has been instrumental in delivering AI solutions for diverse applications such as identifying high-risk customers, classifying documents, segmenting customers, and predicting churn. The framework encompasses six key components: Business Capture, AI Problem Framing, Data Strategy, AI System Design, Feasibility Assessment, and Performance Evaluation. It is advisable to begin with Business Capture and progress through the other sections while constructing your prototype solution.

Definition: A prototype in this context refers to a smaller-scale implementation designed to validate the concept.

Advantages of This Methodology

• Prioritizes business value from the outset.
• Facilitates the identification of AI/ML investment opportunities based on value versus feasibility analysis.
• Provides a comprehensive audit trail for data sources and design choices.
• Converts business challenges into actionable ML/AI solutions.
• Offers a reference design document for both your convenience and for engineers tasked with scaling the solution.

Chapter 2: Business Capture

During the Business Capture phase, collaboration with your business subject matter expert (SME) or analyst is essential to articulate the business challenges. Here are some guidelines to follow:

#### Business Area & Ownership

Document the relevant business areas and the individual(s) who may fund a comprehensive deployment if the prototype proves successful.

#### Stakeholders and Roles

Identify and note the key stakeholders, including SMEs, analysts, data scientists, and sponsors.

#### Defining the Problem

Articulate the business issue that needs resolution. If it seems overly complex, divide it into manageable sub-problems. Consider questions like: What factors are driving the change? Who holds responsibility for the processes? What would a successful outcome entail?

#### Estimating Business Benefits

Initially, provide a rough estimate of the potential business benefits should your solution be implemented. This should be a preliminary calculation based on optimal scenarios, which can be refined once the prototype is developed. Aim to express this benefit both quantitatively and qualitatively.

Chapter 3: AI Problem Framing

Once you have a clear understanding of the business problem, you can begin framing it in AI & ML terms at a high level.

#### Value Proposition

Identify the end users, their goals, and how they would gain from implementing machine learning or AI.

#### Decision-Making

Define the decisions your system will make or assist with, considering the scale and frequency. In regulated sectors like finance, be aware of any constraints related to "black box" models and the need for interpretability.

#### ML Services

Determine if your solution will utilize various ML or AI services offered by cloud platforms. Broad categories include Language Processing, Natural Language Processing (NLP), text extraction, computer vision, and speech recognition.

#### Machine Learning Considerations

Not every solution will necessitate a custom machine learning model. Evaluate whether building one is essential, as creating a bespoke model can often be more complicated than leveraging existing ML services.

Chapter 4: Data Strategy

Data serves as the fuel for your machine learning engine, making it crucial to meticulously outline your data requirements. Initially, assess your needs for both training and inference. If you’re training a model, identify potentially predictive features. For inference, determine the necessary data.

#### Data Management & Procurement

Consider your data management and procurement strategies. Are you handling structured, semi-structured, or unstructured data? Each type has unique requirements regarding storage, processing costs, and availability.

Be mindful of industry regulations like GDPR that govern data usage. Establish a compliance strategy, documenting the type of personal data used, data sources, primary contacts, and retention periods to facilitate upcoming audits.

#### Data Acquisition Costs

Recognize that data acquisition is not free; hence, the value proposition may diminish significantly if it incurs high costs.

#### Data Labeling

In an industrial context, if you choose a supervised learning approach but lack labeled data, outline your data labeling strategy. Will you employ a service like AWS’s Mechanical Turk, or will you explore unsupervised learning techniques to create labels?

#### Addressing Bias

Understand and implement strategies to mitigate bias within your dataset, such as:

• Sponsorship Bias: Data from sponsors may omit detrimental information.
• Self-Selection Bias: Data from voluntary respondents can skew results.

Chapter 5: AI System Design

Detail your intelligent application’s design, leveraging insights from the Business Capture and AI Problem Framing phases. Consider the following:

#### Approach

Clearly outline your intended methodology for addressing the problem, including the necessary machine learning services and whether model training is required.

#### Performance Requirements

Establish the minimum performance standards expected, including latency and model performance benchmarks.

#### Outputs

Define the expected outputs. Will your intelligent system provide inferences to another application or generate an insights dashboard? This will inform your design choices.

#### Research

Investigate existing applications that may already address your problem. Often, opting for an established solution can be more efficient than starting from scratch.

#### High-Level Solution Design

Create an end-to-end design map of your solution. For cloud-native solutions, focus on service-level details without delving into the specifics of your ML pipeline.

#### Machine Learning Strategy

If you plan to build your model, list potential models and establish a baseline. Include limitations of each model and outline a detailed ML pipeline encompassing data segregation, wrangling, feature engineering, and model evaluation processes.

Chapter 6: Performance Evaluation

Regardless of whether you're crafting bespoke models or utilizing existing ML services, consistently track their performance against established criteria. For tailored machine learning solutions, implement a scientifically sound evaluation strategy to avoid overfitting and enhance production viability.

Chapter 7: Feasibility Assessment

Your ultimate aim is to develop a model that operates effectively in production and provides value to your organization. After outlining all requirements and constructing the prototype, evaluate the scalability of your solution. Consider:

• Data Infrastructure: Access, volume, and quality of data.
• ML/Solution Resources: Available technical resources, existing solutions, and relevant knowledge.
• Processes & Systems: Any necessary changes to business processes, systems, or organizational structures.
• Expertise: Assess if the necessary technical and domain knowledge is available and the time required to upskill your team.
• Deployment Timeline: Estimate how long it will take to bring your solution into operation.

The Initiative for Applied Artificial Intelligence has published a white paper offering a method to evaluate the feasibility of your intelligent solutions.

Chapter 8: Value vs. Feasibility

Assess your solution's value versus feasibility. Aim for solutions that exhibit high value and high feasibility, though few will fit this description initially. Revisit your design to explore higher feasibility approaches. In organizations early in the intelligent application adoption curve, consider multiple solutions to identify investment opportunities.

Final Thoughts

Implementing artificial intelligence to address business challenges is complex. Given the myriad of components involved, standardizing your approach can enhance efficiency. Feel free to adapt this methodology to suit your needs.

The first video title is AI for Good: A Framework for Building Ethical AI Solutions. This video provides insight into developing ethical AI solutions that can benefit society.

The second video title is Give Me a Framework to Build a Trusted AI System. This video discusses essential frameworks for creating reliable and trusted AI systems.