Taking Generative AI From Exploration to Production on AWS

AllCloud Blog:
Cloud Insights and Innovation

Generative AI (GenAI) has captured the imagination of organizations across industries with its ability to create specific solutions for use cases based on provided knowledge, including chatbots, generated content, and QA assistants. However, the journey from a promising lab project to a robust production application has its challenges. As an AWS Generative AI Competency Launch Partner, AllCloud offers proven expertise in helping customers efficiently deploy their GenAI projects in production.

Businesses cross-industry face several hurdles when taking GenAI from lab to production, such as:

  1. Accuracy and Factuality: Large language models (LLMs) are notorious for “hallucinating,” generating seemingly plausible but factually incorrect information. This can lead to detrimental consequences, especially in sensitive domains like finance or healthcare.
  2. Bias and Fairness: LLMs, trained on massive datasets, can inherit and amplify existing societal biases. Mitigating bias requires careful selection of training data and ongoing monitoring of the model’s outputs.
  3. Security and Guardrails: Ensuring secure interactions with LLMs involves establishing guardrails to prevent them from generating harmful or inappropriate content. This requires defining acceptable topics and implementing mechanisms to flag and filter problematic outputs.
  4. Data Security: Proper data security measures are crucial when interacting with LLMs, as they may be exposed to sensitive information during training or usage. This necessitates robust data encryption and access controls.
  5. Latency Issues: LLMs can sometimes take a while to generate responses, impacting user experience and potentially hindering real-time applications. Optimizing infrastructure and resource allocation can help address latency.
  6. Domain Adaptation: LLMs trained on general data often perform poorly on specific industry tasks. Tailoring them to your domain requires techniques like RAG or fine-tuning with domain-specific data.

The Honeymoon is Over
Until not long ago, GenAI was the hottest topic out there. Businesses excitedly experimented with its capabilities, mesmerized by its potential to generate creative content, answer complex questions, and automate tasks. Articles buzzed about a revolutionary future filled with chatbots holding natural conversations, content mills churning out high-quality material, and AI assistants streamlining workflows. It was a honeymoon phase, a time of exploration and fascination.

However, the excitement of tinkering has given way to a hunger for real-world application. The gap between the initial hype and the practicalities of production use is proving significant. Businesses are no longer satisfied with just experimenting; they crave the transformative power GenAI promised.

Bridging the Gap: Essential Considerations for Successful GenAI Implementation
The romance with GenAI can quickly fade when faced with the realities of real-world implementation. But fear not! AllCloud, an AWS Premier Partner with proven expertise in implementing complex Data & AI solutions, can help you bridge the gap between GenAI’s potential and successful application. Here are three key areas that set AllCloud apart: 

  • AWS Expertise: AllCloud possesses a deep knowledge of the relevant AWS services – BedRock for easily building LLM-powered apps, SageMaker for building, training, and deploying models, Amazon Q for easy querying of domain-specific data, and CodeWhisperer for AI-powered code generation. 
  • Production-Ready Experience: A proven track record that is evident in the AWS Generative AI Competency designation, a testament to AllCloud’s expertise in helping customers achieve successful production deployments.
  • Data & Analytics Focus: With over 15 years of experience and the Data & Analytics Competency badge, AllCloud obtained a strong foundation in data governance and security, ensuring your GenAI project prioritizes business value while adhering to best practices.

Putting GenAI in Action: Tarigma Case Study
Tarigma, a utility company, reached out to AllCloud to streamline its power outage reporting process. After analyzing Tarigma’s workflow and identifying bottlenecks, AllCloud built a custom AI model in SageMaker to analyze substation data and pinpoint the cause of outages, and a data lake to store and prepare the information for the model.

To automate report generation, AllCloud implemented AWS Bedrock, eliminating the need for manual report writing. The entire solution was designed for efficiency and deployed in a secure environment. This collaboration significantly reduced report generation time from hours to seconds, allowing Tarigma to quickly respond to outages and minimize downtime for its clients.

Taking GenAI to production requires a multifaceted approach that addresses technical hurdles, security concerns, and domain-specific needs. By partnering with AllCloud, you gain access to a team of experts with the technical know-how, industry experience, and data-centric mindset to bridge the gap between the lab and real-world application, turning the promise of GenAI into a reality.

Learn more on how AI can enhance your existing business processes, improve decision-making and drive innovation with AllCloud Generative AI Assessment.

Jonathan Chemama

AI Tech Lead

Read more posts by Jonathan Chemama