Machine Learning Framework

SciBiteAI provides a framework for leveraging Artificial Intelligence (AI) and deep learning models alongside our award-winning semantic technologies to unlock the insights hidden in your data.

Implemented as a lightweight, server-based application and deployed via industry-standard Docker containers, SciBiteAI enables users to rapidly prepare, train and deploy deep learning models.

Making Data AI-Ready (.prepare)

Even today, 80% of an organisation's data is held in unstructured text such as Word documents and PDFs. This is also true of external data sources such as patents, blogs, clinical notes and literature databases.

SciBite’s standards-based semantic tools enable Findable Accessible Interoperable Reusable (FAIR) data across the entire enterprise, and our powerful ontology management builds on this approach - turning "strings into things".

Training ML Models (.model)

At SciBite, we have in-depth experience building deep learning models: from named-entity recognition (NER) to semantic relationship extraction and question answering based on semantic structures.

Our consultancy service offers you the opportunity to work with our experts in creating, refining and deploying sophisticated deep learning models for your project.

With first-hand experience of deep learning models such as BioBERT, LSTM and Word2vec, we'll help you select the right algorithm for your data, together with planning and costing your project.

Deploying ML Models (.deploy)

At SciBite, we understand the complexities of public domain machine learning language models such as BERT, BioBERT, ELMo and Word2vec.

These models can be cumbersome to install and integrate, and the code difficult to maintain and distribute within an organization - a significant constraint as these models change frequently.

We understand these constraints and recognise that customers need simple, machine learning services. SciBiteAI separates the API from the implementation, removing the need for labour-intensive proprietary coding.

Connecting ML Output (.connect)

To fully exploit the output of machine learning, one final step is often required: connecting other data via identifiers such as those from ontologies and vocabularies.

The flexibility offered by the SciBiteAI API, and other tools such as TERMite, enable results to be aligned to ontologies and other references. This alignment allows for the deeper exploitation of semantics, for example, parent-child or part-whole relationships. Knowledge graphs can also be used to capture connections between drugs, diseases and targets.

Connecting machine learning with semantics offers a powerful combination in the next generation of SciBite’s text analytics capabilities.

Leverage our Experience of Deep Learning

At SciBite we have experience in developing and deploying semantic deep learning models that perform a wide variety of functions:-

Take the next step...

Contact us to discuss your requirements or read a more in-depth description of SciBiteAI

Customer Use Cases