By far generative AI
But one common question lies in the mind of all leaders, What is the right approach to deploying the suitable generative AI model?
A comprehensive approach to deploying generative AI The number of pre-trained generative AI models and apps is growing exponentially. As a result, enterprises face the challenge of aligning the suitable AI model with their business use case. With the right approach to generative AI deployment , companies can enhance their overall productivity and outcome. Let’s have a look to understand each model in detail.
Pre-trained generative AI
These generative AI are exclusively designed for end-users who do not have technical knowledge. Businesses can leverage these tools to elevate their customer experience , boost productivity, and streamline operations. Firefly, Midjourney, ChatGPT, and Google Bard are examples of consumable embedded generative AI .
However, a notable drawback is their limited scalability. Such models don't offer flexibility. Organizations might find themselves with reduced control over security and data privacy concerns.
Custom embedding model
Generative AI in a custom application frame has emerged as a transformative approach for business leaders. It allows users to customize the prompt templates based on their business requirements. It can be best used for sentiment analysis, where with the help of specific instruction, the exact results can be drawn.
This model is highly effective, offering value across diverse business scenarios. However, there's a limitation on the amount of data that can be transmitted through the app.
Retrieval augmented generation model
Extending generative AI models through data retrieval involves training the AI with a company's specific knowledge. This knowledge is proprietary and vast but often goes unused. By tapping into this resource, companies can organize and deploy their unique insights more efficiently and effectively. It allows them to enhance accuracy on domain-specific tasks. At the same time augmenting the prompt may increase latency which impacts its viability in real-time business use cases.
Fine-tuning in generative AI model
Fine-tuning is a technique in which pre-trained models are customized to perform specific tasks. It involves modifying a pre-trained model to cater to a more specific topic or targeted objective. For example, a pre-trained model that can generate natural language texts can be fine-tuned to write poems, summaries, or jokes.
This technique is capable of learning from existing artifacts to generate new, realistic artifacts (at scale) that reflect the characteristics of the training data but don’t repeat it.
Foundation model
These models are fully customizable, allowing companies to train them using their own datasets. This approach grants businesses total control, ensuring outcomes tailored to their specific domain. Organization can build their own foundation from scratch.
However, building a model from scratch is costly. Along with significant financial investment, it’s challenging for an organization to keep pace with rapidly advancing technological trends. Such models run a high risk of becoming outdated quickly.
Interpreting AI for Businesses: Crucial Points of Consideration In case you want to deploy these models based on your business requirements, you must consider some crucial points. Here we discuss a few considerations for your reference.
Cost
The financial implications of each approach are largely dictated by the intricacy of the chosen model. Custom-embedded generative AI models are simple, typically incurring a standard subscription fee. In custom application frameworks, costs remain low as only prompts are modified. Data retrieval models, which add more data to the model prompts, cost more to the user. Fine-tuning model costs fluctuate based on the size and parameters on which the models are updated. Among these, custom foundation models are the most expensive.
Organization/domain knowledge
Although foundational AI models possess broad knowledge, tailoring them to specific domains enhances their accuracy and minimizes unintended outcomes. Integrating these models via data retrieval, fine-tuning, and custom model creation is key to infusing them with domain or organizational expertise. Providers like Charli AI (financial services), Huma AI (life sciences), and BloombergGPT now offer specialized models and SaaS solutions, bridging the gap between general-purpose AI and specific organizational requirements.
Security and privacy
In the generative AI sector, security and privacy encompass a wide range of concerns. There's a need for robust access control and data sovereignty. Building or fine-tuning custom models grants businesses greater ownership and control over these assets. While AI providers are continually enhancing their security measures, user organizations must recognize the shared responsibilities between them and the providers. It's crucial for these organizations to implement audit procedures to identify and address any vulnerabilities in generative AI products.
The New Frontier: Safely Integrating Generative AI in the Workplace The digital age is witnessing a transformative shift, with Generative AI. However, with the surge in pre-trained models, enterprises must judiciously select and deploy the most fitting AI solutions for their needs. As we look to the future, the role of Generative AI in reshaping the world of work is undeniable. It's imperative for businesses to not only understand the diverse deployment strategies but also to prioritize safety and knowledge. By doing so, they can harness the full potential of Generative AI, blending technology and human endeavors for enhanced productivity and innovation.
Source
1) https://www.gartner.com/document/4507999?ref=TypeAheadSearch
2) https://metrigy.com/product/customer-experience-optimization-2023-24/
3) https://www.acumenresearchandconsulting.com/table-of-content/generative-ai-market
