Generative AI has become a highly sought-after technology in the modern world, with most companies keen to be part of the early adoption phase. However, many in the workforce are facing a dilemma on how to begin their journey through navigating the process. This blog will highlight a clear guideline and further steps to take for businesses to maximize their potential and understanding of Generative AI.
Let us analyze the evolution of the Generative AI journey. The early 2000s marked the rapid advancement of various machine learning techniques that could analyze massive amounts of online data to conclude – or “learn” – from the results. The 2010s produced significant improvements in AI’s perception capabilities in the field of machine learning, also referred to as deep learning. Building on exponential increases in the size and capabilities of deep learning models, the future will seek to expand and build on language mastery. The GPT-4 language model, developed by OpenAI, marks the beginning of a new phase in the abilities of language-based AI applications.
Firstly, Generative AI has a wide range of use cases across industries. There is a need to identify high-value use cases, that improve customer satisfaction and cost optimization. Then comes the required skill sets, and to leverage such high-level applications, employees need to have the skill set of these cutting-edge technologies as they are essential in designing, developing, implementing, and maintaining Generative AI applications.
In fact, due to the number of different Large Language Models (LLMs) available, there is a necessity to choose the right model- one that assists us in fulfilling our challenges and use cases. Companies can utilize pre-existing models from the model hub through Application Program Interfaces (APIs) and implement them in their current state, with minimal prompt engineering changes required. Alternatively, they can customize the model by fine-tuning it with their enterprise data sets. Also, they can use Retrieval Augmented Generation (RAG) to integrate enterprise data as the Knowledge base and feed that data to the LLMs for specific responses.
In addition, LLMs rely on vast amounts of curated data to learn and that makes solving data challenges an urgent priority for every business. Improving the maturity of the enterprise data lifecycle will become a prerequisite for success – requiring mastery of new data, new data types, and immense volumes. Generative AI features within modern data platforms will emerge, enhancing adoption at scale.
Furthermore, Generative AI applications require robust infrastructure that can support the complex computational needs and data handling requirements of these technologies, and models that involve the processing of large datasets also require high-performance Graphics Processing Units (GPUs). Also, Cloud infrastructure will be essential for Generative AI applications- for high scalability and availability- and to manage costs.
The last phases of the process include implementing and testing the model, gaining insights from the outcomes, and iterating the process until achieving the desired results from the model. To minimize potential biases and safeguard the ethical use of Generative AI technologies, there is a need to implement guardrails, policies, and rules to secure the data.
The landscape of Generative AI is vast, one that provides a plethora of opportunities for learning and cultivating a platform for innovation. With its exposure in numerous industries and services across the world, Generative AI stands out as a defining avenue for growth, expanding its reach and presence in the modern era. With its continued development in the rapidly evolving nature of the world, it serves as a platform for the next generation, allowing us to build and grow a more inclusive world for all.
About the Author
Sreedevi Velagala has a background in Computer Science and Information Technology, kicking off her career as a developer in India, and has an extensive career spanning over 20 years in the IT industry. Today, she is working as a Senior Architect at AWS. Throughout her career, Sreedevi has held leadership positions in esteemed financial organizations such as UBS (Union Bank of Switzerland), Barclays Capital, OCBC (Overseas-Chinese Banking Corporation Limited), Credit Suisse, and Citibank. Her extensive experience in various IT technologies, strategic leadership, project management, and navigating complex international landscapes has made her an invaluable asset in the world of cloud technology.
Sreedevi is working with Amazon Web Services (AWS) as a Senior Solution Architect. Here, she is heading numerous futuristic AI/ML projects including Generative AI capabilities such as Large Language Models (LLMs), vector databases and Retrieval Augmented Generation workflows. In addition, she has developed new technical assets such as AWS Guidance and Solutions which will include technical architectures, documentation and code. These reusable assets will simplify and accelerate the adoption of these technologies as they will provide customers with “pre-built” components. She is also an advisor to the customers on how AWS Solutions can lower the compute costs for Generative AI scenarios, educating customers and AWS technical Solutions Architects on how to use these technical assets.
Sreedevi is a recognized expert with extensive experience and a deep understanding of AI/ML and Compute domains, making her a trusted authority in these areas. This article encapsulates Sreedevi’s vast knowledge and expertise in AI/ML and Generative AI, providing readers with valuable insights and perspectives from someone with hands-on experience in the field.
Bibliography sources:
1. https://www.gartner.com/en/webinar/517294/1178143
2. https://www.gartner.com/en/topics/generative-ai
3. https://www.bcg.com/publications/2023/ceo-guide-to-ai-revolution
