Large Language Model Operations (LLMOps)
What Is LLMOps?
LLMOps (Large Language Model Operations) is a subset of Machine Learning Operations (MLOps) tools, practices, and processes, tailored to large language models’ (LLMs’) unique challenges and requirements. LLMOps specifically focuses on managing and deploying large language models (like GPT-3.5) in production systems. LLMOps includes fine-tuning language models for specific applications, handling large language model inference at scale, monitoring large language model performance, and addressing ethical and regulatory concerns related to large language models.
Why Is LLMOps Important?
This operational domain is critical for organizations looking to safely and confidently derive value from their generative AI projects, especially in a landscape where AI production processes haven’t kept pace with the rapid development and adoption of generative AI and LLMs.
LLMOps aims to tackle a number of organizational, technical, and procedural problems that arise with the growing popularity of generative AI:
- The hodge-podge of AI/ML tooling across multiple platforms, technologies, languages and frameworks used to build models, which can crush production processes, hurt productivity, increase risks around governance and compliance, and elevate cost.
- Internal software development and non-data science teams are aggressively building out generative AI solutions using open source tooling, creating a “Shadow IT” problem for data leaders and IT leaders, with lack of visibility and governance. These “non-traditional” model builders may not be aware of AI lifecycle management best practices and have trouble getting their models into production.
- Improved prediction accuracy requires use-case specific vector databases, which increases the proliferation of “narrow” databases throughout your infrastructure. The best practice for increasing prediction accuracy is to create a specialized vector database for each use case, increasing database sprawl and increasing governance challenges.
- As the number of generative and predictive model assets increase exponentially within your technological infrastructure, so does the complexity in managing, monitoring, and governing these models to ensure top performance.
LLMOps tools and practices address these challenges through a variety of model observability and governance solutions.
These, when applied holistically, allows organizations to:
- Deploy and run generative AI models in production
- Monitor generative models and take actions to improve model metrics
- Learn from users and continuously optimize/improve generative outputs
- Test generative model performance and generate documentation
- Register generative models and manage them across deployment platforms
- Create an audit trail for each generative model and approve changes
LLMOps + DataRobot
DataRobot offers a complete generative AI lifecycle solution that allows you to build, monitor, manage, and govern all of your generative AI projects and assets.
It enables “confidence” in the correctness of responses by combining generative and predictive AI, where predictive models are used to verify and evaluate your generative responses, with confidence scoring, evaluation metrics, and guard models.
With DataRobot, your users can evaluate and rate generated responses for accuracy, so your generative AI applications can learn from their feedback and improve confidence scores.
DataRobot LLMOps provides multiple LLM-specific metrics, out of the box, like anti-hallucination metrics and content safety metrics, like prompt toxicity.
DataRobot AI Registry gives you a 360-degree view of all AI assets no matter where the model was built or hosted, reducing “lock-in risk”:
- Consolidate, organize, and version multiple generative and predictive AI artifacts from any source, regardless of platform or cloud, into a single source of truth and system of record.
- Manage vector databases, LLMs, and prompt engineering strategies neatly together.
- Utilize a unified governance and policies and roles-based access control tied to your AI assets, not your data warehouse, lake or hosting platform.
You can monitor all your generative AI assets in one dashboard, automatically test new challenger models and “hot swap” out old models for the new champion model without disrupting your business processes. Metrics include:
- Data Drift: drift metrics to alert you to the potential causes of model degradation
- Accuracy: variety of prediction performance metrics for any model
- Custom performance metrics: user-defined metrics for all your business needs (including cost)
- Fairness monitoring: bias and fairness metrics
- Data quality checks: user-defined, rules-based data quality ranges with custom alerting
You can use DataRobot as a central location for automatic monitoring and alerting for all of your generative AI assets, regardless of where they are built/deployed, with metrics including:
- Service Health: Operational metrics for deployment (eg volume, response times, mean and peak loads, and error rates)
- SLA monitoring: SLA metrics regardless of model origin.
- Prediction archiving: Archive past predictions for analysis, audit, and retraining
- Custom operational metrics: User-defined metrics including LLM cost
- Maintain cost control: Avoid budget overruns from increased compute with clear monitoring and management of LLM usage.
- Real-time alerts: Ensure models continue to create value with automatic model performance alerting
DataRobot helps you prepare for pending regulations with our generative AI trust framework. Utilize DataRobot Bias and Fairness capabilities to prevent generative models from inadvertently learning biases and creating unfair outputs.