How MLOps Work in the Era of Large Language Models
Large language models (LLMs) and generative AI have taken the world by storm, allowing AI to enter the mainstream and show that AI is real and here to stay. However, a new paradigm has entered the chat, as LLMs don’t follow the same rules and expectations of traditional machine learning models. As such, data scientists need to find a different approach for using MLOps to find structure and create a sense of order as LLMs are developed. Here are a few ways that MLOps can be used to bring some organizational structure to large language models, what you can do, and why you should consider integrating MLOps when developing LLMs.
Managing Data
Possibly the biggest reason for MLOps in the era of LLMs boils down to managing data. Given they’re built on deep learning models, LLMs require extraordinary amounts of data. Regardless of where this data came from, managing it can be difficult.
MLOps can help organizations manage this plethora of data with ease, such as with data preparation (cleaning, transforming, and formatting), and data labeling, especially for supervised learning approaches. MLOps is also ideal for data versioning and tracking, so the data scientists can keep track of different iterations of the data used for training and testing LLMs. Lastly, MLOps helps with data storage and security by providing tools and workflows that enable efficient storage and retrieval of data.
Training Models
Training large language models takes a lot of effort and resources to ensure the best possible outcome. When training LLMs, there’s a lot that goes into it, such as preprocessing data, selecting hyperparameters, tuning model architectures, and so on.
MLOps can help out with the automation of the training process of LLMs, making them more efficient, repeatable, and scalable. This can be done through a variety of ways like experimentation management, distributed training, preprocessing pipelines, model selection & tuning, and model deployment.
Deploying Models
On the note of deploying models, that’s another area where MLOps comes in quite handy. Deploying large language models is a complex process that’s not as simple as hitting “enter.” MLOps can help with packaging the trained model, deploying it to a target environment, and more.
There are quite a few ways that MLOps can help with the deployment of large language models. A big way is with containerization by using tools such as Docker to package an LLM and its dependencies into a single container. MLOps are also helpful for deployment automation by using tools like Kubernetes to manage the deployment process and automate tasks like provisioning infrastructure, deploying containers, configuring network settings, and more. MLOps can also help with continuous integration and continuous deployment (CI/CD), model versioning, and testing and verification.
Monitoring Models
Monitoring LLM models in production is critical to ensure that they are performing well and meeting the needs of users. From real-time monitoring to performance motoring, MLOps are crucial for monitoring LLMs.
With real-time monitoring, MLOps can help organizations in real-time by providing monitoring and alerting capabilities as they happen, and this can include metrics like accuracy, precision, recall, and so on, and even system-level metrics like CPU and memory usage. Anomaly detection is also a crucial part of monitoring models, such as via statistical process control and ML-based anomaly detection so organizations can catch drops in accuracy as they occur. MLOps are also helpful with root cause analysis, performance monitoring, and governance & compliance. All of these features together can help organizations ensure that their LLM models perform well, meet the needs of the user, and comply with laws and regulations.
Conclusion
These are only a few ways that MLOps are needed in the age of large language models. If you want to learn more about this emerging dynamic, then be sure to check out our NLP track at ODSC East this May 9th to 11th where we’ll feature a number of sessions on large language models, generative AI, and more, such as “MLOps in the Era of Generative AI” by Yaron Haviv, Co-Founder & CTO of Iguazio.
Here are a few more sessions:
- Self-Supervised and Unsupervised Learning for Conversational AI and NLP
- Modern NLP: Pre-training, Fine-tuning, Prompt Engineering, and Human Feedback
- Topic Modeling using pre-trained large language model embeddings
- A Zero-shot 2D Sentiment Model Predicts Clinical Outcome in Psilocybin Therapy for Treatment Resistant Depression
- Infuse Generative AI in your apps using Azure OpenAI Service
Originally posted on OpenDataScience.com
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