In this video, the speaker discusses the concept of a "new age machine learning engineer" who works with generative AI models and highlights two main differences compared to traditional machine learning engineers:
1. **Software Engineering Skills**: The new age machine learning engineer needs strong software engineering skills. They work on building applications that involve connecting various services, including vector databases and large language model APIs. This requires skills in building web applications, working with APIs, and creating a robust and scalable infrastructure.
2. **Working with Pre-trained Language Models**: They also need to understand how to work with pre-trained language models effectively. These models are non-deterministic, and the engineer must add context and data to make them specific to solving particular business problems. Additionally, they need to have an experimental and debugging mindset, similar to that of a data scientist, to ensure the accuracy and reliability of the model's outputs.
The speaker emphasizes that the new age machine learning engineer combines software engineering skills with a data scientist's mindset to build applications using generative AI models. They also mention the importance of setting boundaries and implementing checks, especially when these applications are customer-facing, to ensure the correctness of the AI-generated answers. The video discusses the impact of these AI applications on customer experiences and how they require careful monitoring and evaluation. Lastly, the speaker highlights the collaboration between backend AI engineers, frontend developers, and other roles in project teams to create these applications successfully.
Here are the key facts extracted from the text:
1. There are two main differences between traditional machine learning engineers and new age machine learning engineers working with generative AI.
2. New age machine learning engineers require more software engineering skills.
3. They work with pre-trained language models through APIs and use retrieval augmented generation.
4. This approach involves connecting various services together and building applications.
5. The unpredictability of generative AI models requires a mindset similar to data scientists.
6. You need to learn how to make these models specific for business problems by adding context and data.
7. New age machine learning engineers must combine software engineering skills with the experimental and debugging mindset of data scientists.
8. Customer-facing applications using large language models require a high level of accuracy and responsible implementation.
9. Teams often consist of backend AI engineers who configure data connections and logic, frontend developers who create user interfaces, and machine learning engineers.
10. The new age machine learning engineer's role is to bridge the gap between software engineering and machine learning, using a new set of tools and skills.
11. Platforms like Langsmith are developing tools for evaluation and monitoring of generative AI applications.
These facts provide an overview of the key points in the text, focusing on the differences between traditional and new age machine learning engineers and the skills required for the latter.