Dramatically increases data science team efficiency and model performance
Teaching AI about the specifics of an organisation's business is critical for delivering process automation.
Jaid's unique model training and deplopyment platform allows users to 'see through the eyes of the model',
delivering cost-efficient high accuracy models, using minimal data volumes, with unprecedented accuracy, speed and efficiency.
These 'distilled' specialised models are proven to outperfrom much larger general purpose Foundation models.
When trained to understand an organisation's specifics, they are the key to delivering the 'last mile' of the promise of Large Language Models to deliver business transformation.
The Jaid Gym Model Lifecycle
Train
Model Builder deploys generative model assistance to fine-tune models to exceptional
levels of accuracy in just a few hours or days from raw data and a natural language class description.
Build
Engine Builder provides a simple graphical editor to create inference graphs that combine
reusable components, including multiple models and plugin custom business logic, to deliver reliable and stable solutions.
Evaluate
Model Lab delivers advanced evaluation and testing techniques to ensure that the inference graphs and
contained models are predictable, explainable and reliable throughout their operational life.
Run
Consensus Engine executes inference graphs that dynamically select the lowest cost models for efficient
and accurate inferences, and provides detailed telemetry for model feedback and monitoring.
Monitor
Model Monitor continuously observes production model telemetry to highlight divergence from
expected performance and to trigger manual or automated re-training.
Train
Model Builder deploys generative model assistance to fine-tune models to exceptional levels of accuracy in just a few hours or days from raw data and a natural language class description.
Build
Engine Builder provides a simple graphical editor to create inference graphs that combines reusable components, including multiple models and plugin custom business logic, to deliver reliable and stable solutions.
Evaluate
Model Lab delivers advanced evaluation and testing techniques to ensure that the inference graphs and contained models are predictable, explainable and reliable throughout their operational life.
Run
Consensus Engine executes inference graphs that dynamically select the lowest cost models for efficient and accurate inferences and provides detailed telemetry for model feedback and monitoring.
Monitor
Model Monitor continuously observes production model telemetry to highlight divergence from expected performance and to trigger manual or automated re-training.
