Using central Natural Language Processing (NLP) platform for multiple use cases

With the rising prominence of NLP and Large Language Models (LLMs) like ChatGPT, there’s been a surge in interest and expectations from business stakeholders on how to leverage these technologies. These stakeholders recognize the potential for positive change, but need to establish a controlled environment that can facilitate safe, secure, and effective experimentation within the company.  By enabling multiple use cases within this controlled environment, we have helped our client to explore the full potential of NLP and LLM across diverse applications, ranging from customer service and marketing to product development and data analysis. 

Integrating ChatGPT and Large Language Models (LLMs) into our project presents a challenge, especially regarding data protection, authorization, quality of output (e.g. avoiding/minimising hallucinations) and optimizing costs. Our goal is to protect sensitive data while providing authorized users with uninterrupted access to a quality system. Achieving this balance between accessibility and security requires careful planning and robust authentication protocols. 

We are tasked with simultaneously optimizing costs, navigating the dynamic environment of cloud services, and managing the computing resources required for ChatGPT and LLM functions. Finding the sweet spot between performance and spend requires careful resource management. We strive to use cost-effective solutions without compromising operational efficiency or user satisfaction. 

When faced with these challenges, we are driven by our commitment to accelerate innovation and solve problems.  

Implementing GenAI at the client company required consideration of multiple use cases, each requiring a comprehensive and integrated delivery approach. To effectively meet these requirements, we created a dedicated DevOps team consisting of experts in artificial intelligence (AI), machine learning (ML), and Azure cloud services. 

The team collaborate closely to enhance the GenAI platform. AI engineers design and develop tailored AI models, optimizing performance and accuracy for specific client use cases. ML engineers fine-tune machine learning models for predictive analytics and data-driven decision-making. Azure engineers leverage cloud infrastructure for deployment and scalability, ensuring robustness, reliability, and cost-effectiveness. 

Working as part of a shared team with the client, this DevOps team collaborates closely to drive the successful implementation of GenAI across the client company’s ecosystem.