reusable ai components
Build your own AI capabilities
Logickube helps you to build best-in-class data and model pipelines to produce your own state-of-the-art AI capabilities, from model training through to deployment.
The process of building AI models follows multiple categories that align with model design, build, monitoring and deployment.
At Logickube, we have condensed the AI pipeline that every organisation needs into 4 components.
Design | build | monitor | deploy
The four components of an AI pipeline
Feature store
A feature store is a centralised repository of processed model features that will empower your modelling training and serving. Closely integrated with your data lake, it is reusable and enables teams to experiment and share ideas.
Automated modelling framework
A scalable and distributed modelling framework that builds AI models by iterating over combinations of algorithms and hyperparameters. The best model and its metadata will be stored in a central model repository ready for model serving.
Model diagnostic platform
Provide offline and online evaluations of your model with performance metrics and key underlying factors. Model diagnostics help your team provide transparent and explainable AI to your stakeholders and enable high quality model outputs from a continuous feedback loop.
Model serving platform
Deploy your models into production to generate a personalised experience for your customers. Whether it is batched based or real time serving, the serving platform should be algorithm agnostic, access controlled, and enable A/B testing of different models. In the case of real time serving, the latency is key as well.
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