7 Lessons From Making $100k+ With AI Projects
Welcome to our blog post on "7 Lessons From Making $100k+ With AI Projects"! In this video, I'm going to share seven valuable lessons that I have learned over the past four months while working on various AI projects that generated over $100k in revenue for my company. My name is Dave Abelar, and I am the founder of Data Lumina, a data science and artificial intelligence coaching and consulting business. Over the past few months, I have been focusing on generative AI and exploring the possibilities that large language models offer. So, let's dive into these lessons and gain some insights that can help you on your own AI journey.
Lesson 1: How to Build AI Applications
Building AI applications involves a process called retrieval augmented generation, where text and unstructured data are converted into numerical representations. These representations are used to perform similarity searches and find relevant information from a company's internal data. User interactions with the AI system are also transformed into vector embeddings and used to search the database. This process enables better control over model outputs and counteracts issues like training cutoff periods and hallucination. If you're interested in learning the technical details of building applications with large language models, check out our free group, Data Alchemy, where we provide trainings and resources to get started with data science and AI.
Lesson 2: Navigating Data Privacy
Data privacy is a common concern when working with AI models. To address this, we recommend using Azure OpenAI, Microsoft's cloud platform that ensures data privacy. By utilizing clones of OpenAI models within Azure, your data is kept within your subscription and not shared with other customers or used to improve OpenAI models. This transparency and protection are essential for companies looking to implement AI solutions while maintaining data privacy.
Lesson 3: Adapting Rapidly
The world of AI is constantly evolving, with new frameworks and updates emerging regularly. To succeed in this field, you need to stay updated and adapt rapidly. What might be the best solution today could be outdated in a few weeks. Embrace a flexible product roadmap and be prepared to switch frameworks or adopt new directions swiftly. Continuous learning and staying up to date are essential for achieving success with AI projects.
Lesson 4: Data is Still Key
Despite the capabilities of pre-trained models, the importance of data cannot be overlooked. While these models provide a solid foundation, you still need to provide context and quality data to create useful applications. Building robust datasets and ensuring data quality is crucial. Whether you're building applications or helping other companies, assess and prioritize data quality to achieve successful outcomes.
Lesson 5: Start with a Proof of Concept
When venturing into AI projects, it is advisable to start small with a proof of concept (POC). Instead of planning a one-year project upfront, focus on solving isolated problems to demonstrate the value of the technology. Taking an agile approach with rapid feedback cycles allows you to evaluate if the direction and application you're building are effective. A POC can typically be completed within four to six weeks, providing valuable insights and use cases for further development.
Lesson 6: Transitioning from POC to Production
Moving from a proof of concept to production can be challenging, especially with the non-deterministic nature of large language models. The variability in outputs due to different inputs and user interactions requires a solid evaluation system. Tools like Langsmith can help monitor and evaluate your AI applications, ensuring their performance over time. Consider implementing evaluation systems to monitor drift and maintain the effectiveness of your AI applications.
Lesson 7: Required Skills for Building AI Solutions
Building AI solutions requires a blend of software engineering and a data science mindset. While the technical aspects lean towards software engineering, the uncertainty and experimental nature of data science are crucial. Successful projects often involve a team with various roles, including data scientists, machine learning engineers, software developers, and front-end developers. Depending on the use case, different skill sets may be required. Embrace the opportunity to learn software engineering and become a New Age Machine Learning Engineer, capable of working with large language models and generative AI.
So, those are the seven valuable lessons I learned from working on AI projects and generating over $100k in revenue. While the revenue does not directly translate to profit, it showcases the lucrative potential AI projects can have. Partnering with a team of experts and adopting a practical approach from proof of concept to production is key. If these lessons have been valuable to you, let me know, and I can share more insights in the future. Don't forget to like this video and subscribe to our channel for more informative content. Thank you for reading!
FAQs
1. Can I build AI applications without a team?
While it is possible to build AI applications alone, partnering with a team can significantly enhance the process. AI projects involve different roles and skill sets, and a team allows for better collaboration and expertise in each area.
2. How do I ensure data privacy in AI projects?
Using platforms like Azure OpenAI can ensure data privacy in AI projects. These platforms provide a secure environment where data stays within your subscription and is not shared with other customers or used to improve models.
3. How often should I update my AI models?
AI models should be updated regularly to leverage new advancements and improvements. The frequency of updates depends on the specific technology and framework you're using, but staying informed about updates and incorporating them into your projects is crucial.
4. Are pre-trained models sufficient for AI applications?
Pre-trained models provide a solid foundation, but they still require quality data and context to build useful applications. While the models possess advanced capabilities, the data you provide plays a significant role in the success and effectiveness of the applications.
5. Can I learn AI and data science without a technical background?
While a technical background can be beneficial, it is not a prerequisite for learning AI and data science. There are numerous resources available, including online courses and communities, that can help beginners dive into these fields and acquire the necessary skills.
