Guiding your customer to production: Generative AI POC guidance & tips (PEX106)
Scaling Generative AI POCs to Production: AWS Methodology
Key Takeaways
Less than 30% of POCs are scaling to production
Common reasons POCs get stuck include:
Lack of ROI modeling
Lack of advanced optimization
Lack of data strategy
Lack of in-house skills
Lack of strategic priority
Tactical roadblocks like procurement and compliance challenges
AWS's 8-step methodology for successful POC-to-production journey:
Business Modeling
Data Strategy
Model Selection
Architecture
Security and Compliance
Optimization
MLOps
Monitoring
Business Modeling
Venminder, an AWS partner, streamlined document processing and compliance assessments using generative AI
They unlocked 70% of analysts' time and reduced contract review turnaround time from 65 days to under 3 days
They focused on tight alignment between business and technical teams, integrating multiple AI models to improve data accuracy from 60% to nearly 100%
Data Strategy
Venminder prioritized data accessibility, integration, and security/compliance
They provided rich context to the AI models by integrating diverse data sources like SOC statements, security policies, and business continuity plans
They leveraged Amazon Kendra to handle a variety of data formats
Model Selection
Venminder evaluated multiple models during the POC phase, focusing on accuracy, context understanding, and speed
They selected Amazon Claude Sonnet 3.5, which provided the desired data accuracy improvement
Architecture
Venminder leveraged serverless and managed services like Amazon Bedrock, AWS Lambda, and Amazon Kendra
They implemented a data pipeline to securely move data, task-based routing to use appropriate models for specific document types, and mechanisms to combine outputs from multiple AI models
Security and Compliance
Venminder implemented robust access control, data protection, and human oversight for critical processes
They also built a business continuity plan and conducted regular audits to improve their AI processes and models
Optimization
Venminder used Amazon Bedrock to compare and select the right models for their use cases
They built a prompt catalog system to allow users to reuse and customize tunable prompts
MLOps
Venminder built a data pipeline using AWS services and followed CSAT methods to deploy new AI models and prompts
Monitoring
Venminder implemented a robust monitoring framework to track both business and technical metrics, including customer satisfaction, productivity gains, cost savings, service health, success rates, error rates, and model accuracy
Quantiphi's Solution for ACTO
ACTO, a life sciences customer, wanted to improve productivity of their sales representatives and reduce onboarding time for new hires
Quantiphi built a generative AI solution using their Bionic platform, following the same 8-step methodology
Key aspects include:
Robust data strategy for ingestion, storage, and processing
Event-driven architecture leveraging serverless services like AWS Lambda and SQS
Model selection focused on latency, performance, and cost
Comprehensive security and monitoring implementation
Take-home Kit
AWS has made available an 8-step guide, sample code, Bedrock workshops, and training resources to help customers and partners accelerate their generative AI adoption
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