The video is a sharing of seven lessons learned by the speaker, Dave Abelar, over the past four months working on various AI projects that have generated over $100,000 in revenue for his company, Data Lumina. He emphasizes that this is revenue, not profit.
The lessons are:
1. **Building Applications**: The process involves using a technique called retrieval augmented generation, which converts unstructured text data into a numerical representation for similarity searches. This process counters the training cutoff period of open AI models, hallucination, and gives better control over the kind of outputs and results you can expect from your models [Source: Transcript].
2. **Navigating Data Privacy**: When sharing data with models, it's crucial to ensure data privacy. Azure open AI, a Microsoft service, is recommended for this. It allows the use of open AI models in a protected way where data is kept within your Azure subscription [Source: Transcript].
3. **Adapting Rapidly**: The world of AI and generative AI is rapidly evolving, with new frameworks coming out every week. Therefore, it's important to stay up-to-date, adapt rapidly, and continue to learn [Source: Transcript].
4. **Data Quality**: Despite the pre-trained nature of large language models, the quality of the data input is still crucial for creating useful applications [Source: Transcript].
5. **Start with a Proof of Concept**: It's recommended to start small and work on isolated problems that can be tackled with AI technology. This helps to identify specific use cases and prioritize them [Source: Transcript].
6. **Transitioning from Proof of Concept to Production**: This is a challenging step due to the non-deterministic nature of large language models. It requires a solid evaluation system to monitor and evaluate the models [Source: Transcript].
7. **Skills Needed**: Building these solutions leans more towards software engineering but still requires a data scientist's mindset. This includes the ability to do experimentation and figure out how to improve the data [Source: Transcript].
The speaker concludes by emphasizing the lucrative potential of this area from a business perspective. He shares that his company made about $50,000 in profit in four months, not working full-time on this. He encourages viewers to subscribe to his channel for more future videos on this topic [Source: Transcript].
1. The speaker, Dave Abelar, is the founder of Data Lumina, a data science and artificial intelligence coaching and consulting business.
2. Over the past four months, Abelar has been focusing on generative AI and the possibilities offered by large language models.
3. Abelar has learned seven lessons from working on various AI projects, which he plans to share in the video.
4. The first lesson is about how to build applications using a process called retrieval augmented Generation.
5. The second lesson is about navigating data privacy, with a focus on using Azure's platform to maintain privacy and protection.
6. The third lesson emphasizes the need to adapt rapidly in the world of AI and generative AI, as technologies and frameworks are constantly evolving.
7. The fourth lesson underlines that it's still all about the data, even though the models are pre-trained. Quality data is crucial for building useful applications.
8. The fifth lesson advises starting with a proof of concept, especially for companies or professionals considering AI projects.
9. The sixth lesson discusses the challenges of transitioning from a proof of concept to production in AI projects.
10. The seventh lesson highlights the skills needed to build AI solutions, which lean more towards software engineering but still require a data scientist's mindset.
11. Abelar has made over 100k in revenue from his AI projects over the past four months, with about 50k of that being profit.
12. Abelar emphasizes the importance of teamwork in AI projects, stating that custom development is too much work for a single person.
13. Abelar is exploring new areas of AI and sees huge potential in this, especially for small and medium-sized businesses.