Generative AI Path to Production

1. Insufficient initial business scoping and ROI modelling results in disappointing ROI or accuracy expectation
2. Lack of data strategy – Discovered POC to Prod data discrepancy
3. Lack of nuanced optimization for improved ROI
4. Lack of ML/FM engineer skills to productionize)
5. Lack of strategic priority
6. Lack of confidence in security and compliance pauses deployment

Step 1: Business Modeling

1. Be Specific on Your Goal: Clearly define business objectives and how GenAI aligns with these goals to ensure focus on measurable outcomes.
2. Model ROI & Feasibility: Evaluate the feasibility and potential return on investment (ROI) for GenAI initiatives. Consider team capacity, technical requirements, data needs, and budget constraints.
3. Write Tech-Business MoU (Memorandum of Understanding): Draft an agreement between business and technical teams, setting expectations for deliverables, timelines, and roles.

Step 2: Data Strategy

1. Data Types: Identify and categorize data as structured, semi-structured, or unstructured, each with different processing requirements.
2. Data Collection and Storage: Establish batch or stream data pipelines and set up data lakes for large-scale storage and access.
3. Avoid POC (Proof of Concept) Pitfalls: Ensure the real-life data complexity is maintained and not oversimplified during POC testing.

Step 3: Model Selection

1. Open Source vs. Managed Models: Compare open-source models (e.g., community-driven and customizable) with managed models like Amazon Bedrock, which are easier to implement and require less maintenance.
2. Pretrained, Retrieval-Augmented Generation (RAG), and Fine-tuned Models: Choose the right approach—whether using pretrained models, retrieval-augmented generation for added context, or fine-tuning for specific tasks.
3. Model Comparison: Assess models based on factors like accuracy, cost, latency, and any potential data constraints.

Step 4: Architecture/Anatomy

1. GenAI Architectural Components: Set up a robust architecture that includes components like vector databases, ML model serving, prompt engineering, data orchestration, and secure integration layers.
2. AWS Well-Architected: Leverage AWS best practices for building scalable, secure, and resilient GenAI infrastructure.
3. Agentic Workflows: Design workflows where AI agents handle tasks such as translation, extraction, summarization, or any other modular outputs.

Step 5: Security & Compliance

1. Security Responsibilities: Establish roles and responsibilities around compliance, governance, legal requirements, privacy controls, risk management, and resilience.
2. Principles and Controls: Define security principles like fairness, accountability, transparency, and legal/regulatory adherence. Implement controls for model bias, interpretability, and robust protection measures.
3. Human-in-the-Loop: Maintain human oversight in decision-critical applications to ensure AI outputs meet quality and ethical standards.

Step 6: Optimization

1. Model Level: Optimize models by using smaller, lighter models, implementing model quantization, and evaluating multi-modal options.
2. Prompt Level: Fine-tune prompts through techniques like prompt compression and engineering for more effective and efficient model responses.
3. Infrastructure Level: Ensure infrastructure is set up for high-demand situations with batch inference capabilities, throughput provisioning, and reserved instances.

Step 7: MLOps / FMOps

1. Data Pipelines: Automate data pipelines for training, processing, augmentation, and feeding into the GenAI model.
2. Prompt Flow: Build prompt flows for complex interactions where the output of one model serves as the input for the next, using techniques like conditional and multi-stage processing.
3. Model Management: Manage models for reliability, retraining, updating, and collaboration between teams. Include prompt management strategies to fine-tune prompts over time.

Step 8: Monitoring

1. Continuous Innovation: Keep models updated by tracking new foundational models, features, and techniques, and recalibrating when necessary.
2. Responsible AI Checks: Implement guardrails for ethical AI use, monitoring model outputs for harmful biases or incorrect responses.
3. Business SLA Checks: Ensure the model meets business expectations for performance, customer satisfaction, and accuracy as outlined in the MoU.
4. Tech Instrumentation: Set up application monitoring for metrics like availability and error logging, which is critical for stability in production.