AI Build Mapping Workshop, guides product development teams through the experimental, operational, and advanced stages of artificial intelligence (AI). This workshop is tailored for ambitious product managers and designers eager to explore the frontiers of AI technology and harness its potential to drive innovation within their projects. Let's embark on this transformative journey together, one step at a time.
Step 1
Problems & Objectives
Core Focus: Defining the problem, setting clear objectives, and understanding AI capabilities and limitations.
Key Activities: Deep dive into problem identification, desired outputs, and establishing relevant metrics.
Step 2
Data Collection and Management
Core Focus: Accumulating diverse, high-quality data while ensuring ethical compliance.
Key Activities: Sourcing, cleaning, and preprocessing data, with an emphasis on diversity, volume, and relevance.
Helpful AI Prompt:
Identify sources for diverse and high-quality data relevant to our project's focus. Include guidelines for ethical data collection and management practices.
Step 3
Data Processing and Labeling
Core Focus: Refining and preparing data for AI training, including data cleaning, normalization, and labeling.
Key Activities: Augmenting datasets, feature extraction, and ensuring data consistency for effective learning.
Helpful AI Prompt:
Create a step-by-step guide for cleaning, normalizing, and labeling our collected data, ensuring it's prepared for effective AI training.
Step 4
Foundational Model
Core Focus: Selecting and understanding foundational models like GPT, LLaMA, or Palm2.
Key Activities: Outlining task specificity, model compatibility, and leveraging transfer learning for efficiency.
Helpful AI Prompt:
Evaluate the suitability of foundational models like GPT, LLaMA, or Palm2 for our project, considering task specificity and the potential for leveraging transfer learning.
Step 5
Model Training & Fine-Tuning
Core Focus: Training the AI model with techniques like neural networks and deep learning, followed by fine-tuning for specific tasks.
Key Activities: Adjusting model weights, employing differential learning rates, and utilizing tools for fine-tuning.
Helpful AI Prompt:
Generate a custom training regimen for our AI model, including recommendations for adjusting model weights, employing differential learning rates, and fine-tuning techniques tailored to our project's needs.
Step 6
Model Evaluation and Refinement
Core Focus: Assessing the AI model's performance and engaging in continuous refinement for accuracy and quality enhancement.
Key Activities: Utilizing metrics and loss functions for evaluation and applying hyperparameter tuning and feedback loops for refinement.
Helpful AI Prompt:
Outline an evaluation framework using specific metrics and loss functions to assess our AI model's performance, including steps for iterative refinement based on evaluation outcomes.
Step 7
Deployment and Monitoring
Core Focus: Ethically deploying the AI model and establishing a system for continuous monitoring and improvement.
Key Activities: Infrastructure setup, API integration, and implementing feedback loops for performance tracking and ethical adherence.
Helpful AI Prompt:
Provide a deployment plan for our AI model, detailing necessary infrastructure, API integration strategies, and a monitoring system to track performance and ensure ethical adherence.
The AI Build Mapping Workshop is not just a learning experience; it's a transformational journey that empowers product development teams to unlock new potentials and navigate the complexities of AI with confidence and creativity. Join us to explore the depths of AI technology and lead your projects to unprecedented heights of innovation and success. Referenced materials from LeewayHertz
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