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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AutoML with Logickube

Leveraging containerisation and orchestration technologies via Kubernetes, we have built many customisable and reusable ML components for an Automated Machine Learning (AutoML) pipeline.

The benefits of having an AutoML for your business include:

  • Focus on building models to solve business initiatives, not managing the underlying infrastructure
  • High speed to market - build production-ready models in weeks
  • Promote reusability in ML components to reduce duplication of work
  • Explainable AI with model accuracy, feature importance, partial dependency, and bias detection
  • Cost savings with PAYG compute and optimised parallel processing and training

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Feature #1

Distributed model training

Our AutoML pipeline trains models in parallel - enabling faster model hyperparameter tuning, ensembling of models for greater accuracy, and scalable to large volumes of data.

Feature #2

Model diagnostics

At the end of model training, our pipeline produces model diagnostics in an interactive dashboard to help you understand the inner workings of the model.

Feature #3

API deployment

Model artifacts produced by the AutoML are optimised for both batch and real time prediction, so that you can start generating value from your AI models from day 1.