Process of Building Machine Learning AI Models

Slide Content

The slide outlines the sequential steps involved in creating machine learning AI models, emphasizing the complexity of the process. Beginning with "Define Problem & Collect Data," where the initial problem is articulated and data is gathered, it moves on to "Feature Engineering & Model Selection," the step where features are engineered for better model performance and an appropriate model is selected. Subsequently, "Data Split & Train, Evaluate, Tune Model" involves dividing the data, training the model, evaluating its performance, and making adjustments to improve it. The fourth step, "Validate Model & Interpret Results," is about confirming the model's accuracy and drawing insights from the outcomes. The final step is "Deploy & Improve Model," where the model is put into practice and continuously refined. The slide also offers an "Explanation" section, which discusses the intricacy of machine learning model development, highlighting proper data assessment and preparation, model building using quality validation, and mentioning SEMMA and CRISP-DM as two key frameworks used in this context.

Graphical Look

• The slide has a clear, structured layout with a white background and a sequence of five steps displayed horizontally.
• Each step is represented by a circular icon with an accompanying label, all connected by directed arrow lines to indicate the order of the process.
• The icons for the steps are colored from red to green, gradually transitioning across the spectrum as the process progresses.
• Adjacent to the steps, there is a rounded-edged rectangular box with the heading "Explanation" and a bullet-point list.
• The color scheme is a soft mix of red, blue, and green shades, which are calming and easy on the eyes.
• All text elements are in a sans-serif font, maintaining readability and a modern appearance.

The slide has a professional and appealing visual design that clearly conveys the machine learning model development process through color-coded steps and descriptive icons.

Use Cases

• To educate an audience on the fundamental steps of machine learning during a training or educational presentation.
• As part of a pitch to potential investors or upper management, illustrating the methodical approach to developing AI-driven solutions within a company.
• For onboarding new data science team members, providing them with an overview of the company's model development workflow.
• During a project kick-off meeting with stakeholders to set expectations of the development process for a machine learning project.