The Data Leader’s Guide to Generative AI, Part I: Models, Applications, and Pipelines - Audio Blog
Dec 15, 2023
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This podcast explores the introduction of Generative AI and its impact on early adopters, covering the benefits and challenges faced. It discusses the process of fine-tuning a pre-trained Language Model on domain-specific language and facts. It also explores the application of Gen AI in industries such as travel booking, software development, and healthcare. Additionally, it discusses the responsibilities of data teams in managing data pipelines for Generative AI and the importance of preparing teams for successful Gen AI initiatives.
Data leaders should modernize their environments, extend governance programs, and collaborate with data science teams to ensure the success of Gen AI initiatives.
Companies aiming to gain a competitive advantage with Gen AI should focus on feeding domain-specific data to Language Models and implement options such as building a model from scratch, fine-tuning a pre-trained model, or injecting domain-specific data into prompts.
Deep dives
Key Point 1: Benefits and Risks of Generative AI
Generative AI, also known as Gen AI, offers both upsides and downsides to early adopters. The upsides include rich digital interactions and increased productivity. However, the downsides can range from confusion among customers to operational errors and privacy breaches. The key to achieving positive outcomes lies in having timely, accurate, and trustworthy data. Chief data officers (CDOs) and their teams play a crucial role in making Gen AI initiatives successful by modernizing their environments, extending governance programs, and collaborating closely with data science colleagues.
Key Point 2: Language Models and Domain-Specific Data
Language Models are neural networks that generate digital content based on existing data. Companies aiming to gain a competitive advantage with Gen AI should focus on feeding their Language Models domain-specific data rather than relying solely on public Internet content. To achieve this, they have three implementation options: building a Language Model from scratch, fine-tuning a pre-trained model with domain-specific language and facts, or injecting domain-specific data into prompts. Data leaders must train their teams on Gen AI technology and implementation options, evaluate their environment's support for each option, and ensure robust data governance to mitigate risks related to privacy, bias, or intellectual property.
Data leaders must prepare their teams to deliver the timely, accurate, and trustworthy data that GenAI initiatives need to ensure they deliver results. They can do so by modernizing their environments, extending data governance programs, and fostering collaboration with data science teams.
Published at:
https://www.eckerson.com/articles/the-data-leader-s-guide-to-generative-ai-part-i-models-applications-and-pipelines
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