Switching Generative AI Models Effectively
By using smart strategies, there are multiple ways operators can switch Generative AI models more easily.
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In the ever-evolving landscape of artificial intelligence, Generative AI has emerged as a transformative force with profound implications across diverse industries. From revolutionizing content creation to optimizing business processes, these models showcase remarkable versatility and potential.
Yet, as businesses harness the power of Generative AI to drive innovation and efficiency, they encounter a pressing challenge: effectively switching between these models to meet evolving needs and objectives.
In this article, we explore the broader implications of seamlessly transitioning between Generative AI models, examining the strategies, considerations, and far-reaching impact across a spectrum of industries.
Report: Moving Generative AI into Production
The excitement surrounding Generative AI across industries may be tempered by the low rate of Proof of Concept (POC) Generative AI pilots making it into production. Writing for Forbes, Peter Bendor-Samuel, CEO, Everest Group says “approximately 90%” of these pilots “will not make it into production in the near future, and some may never move into production.”
Learn MoreGenerative AI Models: Using Smart Strategies
By using smart strategies, there are multiple ways operators can switch Generative AI models more easily. Operators need to consider why it might be time for a change, particularly if significant investment has been made.
Presenting at Generative AI Summit, Jean-Paul Paoli, Generative AI business transformation director at L’Oréal says that the first step towards switching models is to examine whether the use case delivery can be optimised:
“You might want a model that is more accurate or, on the contrary, more creative – and in the end, more able to perform every task that you want it to do.”
In addition to assessing the Generative AI use case, the multitude of factors driving model switching encompasses technological advancements, performance factors like latency, cost considerations, leveraging the effectiveness of smaller models, adapting AI to accommodate shifting data dynamics while sustaining peak performance, integrating specialized features, utilizing AI for research and development endeavours, ethical considerations, particularly in addressing identified biases, staying abreast of compliance standards, and gaining deeper insights into customer data through enhanced model personalization.
Report: Moving Generative AI into Production
The excitement surrounding Generative AI across industries may be tempered by the low rate of Proof of Concept (POC) Generative AI pilots making it into production. Writing for Forbes, Peter Bendor-Samuel, CEO, Everest Group says “approximately 90%” of these pilots “will not make it into production in the near future, and some may never move into production.”
Learn MoreGenerative AI: Taking into account infrastructure
To efficiently switch models, well-designed infrastructure is crucial. The strategies for effective switching largely mirror those crucial during the development of a Generative AI pilot. By establishing these fundamentals during the pilot phase, operators lay the groundwork for greater agility in the future.
These strategies encompass deploying models as distinct microservices via APIs, employing containerization for enhanced portability, utilizing orchestration tools to facilitate scalability, incorporating model versioning throughout development stages, implementing load balancing for improved responsiveness, adopting configuration management to facilitate adjustments, ensuring robust monitoring and logging for performance oversight, and deploying automated deployment pipelines for swift updates. Enacting these strategies empowers operators to construct a flexible and scalable framework, simplifying the process of switching between Generative AI models while upholding reliability, continuity, and peak performance.
Report: ‘Moving Generative AI into Production’
As with any business endeavour, the efficacy of these strategies lies in their meticulous implementation and ongoing refinement. Have a look at the Generative AI Summit report ‘Moving Generative AI into Production’ to better understand how to switch Generative AI models effectively.
