Systematic model training
Centralized control and comparison of hundreds of training runs for classic ML use cases such as forecasts, fraud analysis, or recommendation engines.
The STACKIT AI Model Experiments service offers you a fully managed MLflow™ platform to efficiently manage the entire lifecycle of your machine learning models and GenAI applications. From the initial experimentation phase through to production deployment, the service ensures maximum transparency, reproducibility, and quality assurance. Thanks to seamless integration with the secure STACKIT cloud infrastructure, you retain full control over your data and models at all times—a critical factor for compliance with regulatory requirements such as the EU AI Act.
Applications of STACKIT AI Model Experiments
STACKIT AI Model Experiments can be used to professionally map the following scenarios, among others:
GenAI & Agent-Based Systems
Tracking and tracing complex "reasoning chains" in agentic systems, including the management of prompt templates and tool calls.
RAG Optimization
Evaluation and debugging of Retrieval Augmented Generation (RAG) processes to improve the response quality of your language models.
Compliance-compliant documentation
Automated collection of all relevant metrics and parameters to ensure audit-proof traceability of AI decisions.
Features of STACKIT AI Model Experiments
- The central platform enables efficient team collaboration by storing training data and models from all team members in a structured, access-protected manner in one place.
- Comprehensive tracking features automatically record model quality, data versions, and parameters, making every training session precisely reproducible and comparable.
- Specialized tracing and debugging tools for generative AI make complex logic chains of LLMs transparent and help quickly identify sources of error in agent-based workflows.
- The integrated model and prompt management enables precise versioning and control over which version of a model or prompt is actively used in which environment.
- Features such as “LLM-as-a-judge” automate the qualitative evaluation of non-deterministic outputs in terms of relevance, soundness, and safety.
Benefits of STACKIT AI Model Experiments
- You accelerate your development cycles by eliminating data silos and enable your teams to work collaboratively on AI solutions.
- Thanks to the complete history of all experiments, you can effortlessly meet complex regulatory requirements such as the EU AI Act and strengthen trust in your AI applications.
- You benefit from a highly available and scalable managed service solution that is ready to use immediately without any complex setup or maintenance.
- The seamless integration with powerful STACKIT compute resources (GPUs/CPUs) guarantees optimal performance for computationally intensive training and inference workloads.
- Through centralized control and automated optimization of prompts and models, you increase the efficiency of your AI projects while reducing operating costs.
