In the fast-paced landscape of today’s digital business world, innovation is the lifeblood of progress. As technology advances at an unprecedented pace, there is a need for creative solutions that can keep up with the evolving landscape. Staying competitive hinges on adaptability, businesses must constantly seek inventive strategies to meet the demands of an ever-changing marketplace. In this blog, we will look more into the advantages of GenAI in a Business Perspective with quick use cases.
It has gained sufficient attention recently due to its ability to generate data from text to images, music, and much more.
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LLM for Business Growth
Large Language Model represents a significant impact on Business Growth and development. These Trained LLMs can generate contextual, relevant content, leading businesses in automating content creation, better engagement, and customer satisfaction.
To come up with an example, an E-Commerce company requested to generate tags and product descriptions for 10,000 images per day. Human intervention will consume lots of time in analyzing the image, writing tags, and product descriptions, which also require lots of human resources.
But now, using state of art technology like Deep Learning, which can analyze images, bring contextual meaning, and generate tags. Furthermore, we can now generate the product description based on the tags generated by leveraging Generative AI.
Quick Implementation: Automatic Product Description from Image.
Amazon Rekognition is a cloud-based computer vision solution designed to analyze and interpret visual content in images or videos using advanced state of art algorithms like deep learning. It can perform various tasks such as Label detection, Image properties, Image moderation, Facial analysis, Face comparison, Face liveness, Celebrity recognition, Text in image, PPE detection and Stored Video Analysis, and Streaming Video Events in videos.
I have used the “Label Detection” API to analyze images that have returned the attributes of images like Clothing, Pant, Jean, and Kakhi.
Amazon SageMaker Foundation Model
Amazon SageMaker JumpStart provides advanced foundational models for various applications like content creation, code synthesis, answering queries, copywriting, summarization, categorization, information retrieval, and more. We can utilize the JumpStart foundational models to construct our own AI solutions with generative capabilities and seamlessly incorporate personalized solutions by leveraging Amazon SageMaker.
A foundational model is a large, pre-trained model that can be adapted to various complex tasks. These models are classified into two categories, namely Publicly Available, like stability AI, Alexa, etc., and Proprietary models like Cohere, AI21 Labs, etc. These models are pre-trained on massive amounts of text data and can be fine-tuned for specific language tasks.
There are three ways to use a foundational model with Amazon SageMaker,
- Amazon SageMaker Studio (One step deployment)
- Amazon SageMaker Notebook
- AWS Management Console
In the above image, I have used one of the LLM models from Amazon SageMaker.
Write product descriptions for fashion eCommerce sites based on a list of features.
Product: apparel and accessories
A classic pair of khaki pants is a versatile wardrobe staple that can be worn for both casual and formal occasions. Our khaki pants are made from high-quality materials and feature a slim fit, creating a tailored silhouette. They also feature a button fly and a belt loop waistband for a comfortable and secure fit.
Note: We can even tune the output with better prompts or a few shots of learning.
Examples of Large Language Models
LLaMA (Large Language Model Meta AI):
LLaMA is a collection of foundation language models trained on trillions of tokens ranging from 7B to 65B parameters. It is an open-source model released by Meta.
GPT (Generative Pre-trained Transformer):
It is a type of LLM developed by OpenAI. There are multiple versions of GPT, and currently, emerging ones are GPT 3.5 and GPT 4, which we can see in ChatGPT.
PaLM2 (Pathways Language Model):
It is a state-of-the-art language model developed by Google with improved multilingual, reasoning, and coding capabilities. PaLM 2 is available in four sizes from smallest to largest: Gecko, Otter, Bison, and Unicorn.
BLOOM (BigScience Large Open-science Open-access Multilingual Language Model):
BLOOM, an autoregressive LLM trained on extensive text data, generates coherent content in 46 languages and 13 programming languages. It is the largest open Multilingual Language Model.
It is developed by the UAE’s Technology Innovation Institute (TII). These models shine in diverse language tasks, like question-answering, logical reasoning, and the generation of text closely resembling human text. Falcon is composed of Falcon-40B, Falcon-7B. Furthermore, TII also released the instruct variants for the same Falcon-7B-Instruct and Falcon-40B-Instruct.
Large Language Models (LLMs) have redefined the way of NLP, not just introducing new fields of view in text comprehension and generation but also having the power to generate Audio, Music, etc. With the capacity to learn from vast datasets, LLMs excel in understanding the context and provide responsive outputs.
We have understood the use of LLMs and how they could revolutionize the business with some quick use cases of Automatic Product analysis and generating Product descriptions using Generative AI. The next part of the blog will discuss more on how Amazon SageMaker helps in solutions for the deployment of the LLM model with examples.
Drop a query if you have any questions regarding LLM and GenAI and we will get back to you quickly.
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1. What is Generative AI?
ANS: – Generative AI is a class of Artificial Intelligence that can generate new, creative, and contextually relevant content/data like text, music, etc.
2. What is Amazon SageMaker?
ANS: – Amazon SageMaker is a cloud-based machine-learning platform offering developers capabilities to create, train, and deploy machine-learning models in the cloud. It also extends its utility to non-technical individuals by enabling them to derive meaningful insights from data in a few clicks.
3. Can we fine tune the LLM model for a specific use case?
ANS: – Yes, Fine tuning is possible with the LLM model.
WRITTEN BY Ganesh Raj
Ganesh Raj V works as a Sr. Research Associate at CloudThat. He is a highly analytical, creative, and passionate individual experienced in Data Science, Machine Learning algorithms, and Cloud Computing. In a quest to learn and work with recent technologies, he strives hard to stay updated on advanced technologies along efficiently solving problems analytically.