Introducing LLMOps

- [Instructor] Most of the excitement in the field of AI today is around large language models. Now, large language models have to be treated a little differently from traditional machine learning models. So it's not just MLOps that we use to automate the pipelines for large language models, we have to use LLMOps. First, let's talk about what an LLM is. A large language model is a neural network that's trained on massive text data to generate human-like language, perform reasoning and complete tasks. These models are called large models, because the models themselves are huge. They contain billions of parameters that need to be trained. They're also called large models, because they're trained on massive quantities of data. We've already discussed MLOps in some detail. This is a set of practices that integrates machine learning development with operations, enabling reliable, reproducible and scalable ML deployment and lifecycle management. LLMOps is the practice of managing the full lifecycle of large language models, including fine tuning, deployment, monitoring, feedback, safety and continuous improvement. Just like MLOps, LLMOps requires a collaboration of data scientists, DevOps engineers and IT professionals. The operational requirements of MLOps typically apply to LLMOps as well, but there are challenges with training and deploying LLMs that require a different and unique approach to LLMOps. Large language models are larger, more complex and more expensive to train than regular ML models. They require massive computational resources for training and inference, making their development and deployment costly and technically demanding. LL MOPS introduces structure and automation to manage these challenges at scale. Unlike traditional ML models, LLMs can produce variable outputs even for similar inputs, making testing and evaluation more complex. LLMOps helps monitor, evaluate and refine model behavior through structured feedback and control mechanisms. LLMOps' frameworks enable secure deployment, usage monitoring and risk mitigation, such as mitigating bias and hallucinations in your model. They also help support ongoing improvements in your model through feedback loops, prompt tuning or fine tuning as your application's needs evolve. LLMs are highly sensitive to the input prompts. LLMOps needs to incorporate tools and processes for designing, versioning, evaluating and managing prompts effectively to achieve desired outputs and mitigate issues like prompt injection or hallucinations. This includes tracking prompt performance and user interactions. Traditional ML models tend to have well-defined metrics that objectively evaluate the quality of the model. Evaluating the quality and factuality of LLM-generated text is complex. LLMOps requires specialized monitoring techniques to detect and mitigate hallucinations. That is when the model generates factually incorrect or nonsensical outputs. Specialized techniques also ensure that the generated content aligns with the desired quality standards and this often involves human evaluation and specific LLM evaluation metrics. Many LLM applications like chatbots require managing conversation and context effectively. LLMOps needs to address how context is handled, stored and passed between interactions to maintain coherence and relevance. And it has to consider factors like context window limitations and cost implications. LLMs are trained on large data sets and they end up inheriting and amplifying any biases that exist in that data and this can lead to unfair or harmful outputs. An important bit of functionality included in LLMOps is identifying, monitoring and mitigating these biases throughout the LLM lifecycle and this requires special tools and techniques for bias detection and fairness evaluation.