Asset Store
Get a head start in your AI projects with our pre-built and pre-trained models and accelerators
Everything you need in one place
The Grace Asset Store accelerates your AI implementation. No need for you to develop everything yourself! Get access to assets that others have built and easily repurpose them across all your own AI projects.
What you will find in the Asset Store
Pre-trained models
Leverage pre-trained models with your data to efficiently accelerate AI implementations.
Model accelerators
Use the Grace Model Accelerators as your framework for reducing the time spent in developing your models.
Standalone pipelines
Automate core data science processes via pipelines that can help transform data and even re-train your models.
Document similarity
Build and deploy a document similarity model with this framework.
Pipeline accelerator
Use this template to get started on Mlops pipeline development. Build continuous training and delivery pipelines.
Credit score
A numerical expression based on a level analysis of a person’s credit files, to represent the creditworthiness of an individual.
Summary model
The model can write a summary of a text in the English language. It will shorten the text while the core content will stay the same.
Price prediction
Determining the right price for a product or service. Learn more
Text completion
A model that can complete a text e.g. add words to the latest stock news. The model adds words around a brief statement in English language.
Analyzing past client lifecycles and the likelihood of churn. Learn more
Car damage recognition
The model can detect damages on the outside of a car and classify them.
Automatic file execution
The pipeline will take a python file name and location as input and rerun it on a schedule.
Face recognition
A model to recognize faces in a picture.
Automatic data ingestion
The pipeline automatically ingests data from a specific data source.
Danish speech to text
The model takes voice input and translates it to text (in Danish).
Auto retraining
A pipeline framework that re-trains a model on a schedule.
Bias check
The pipeline automatically checks a data source for biases.
Lead qualification
Lowering acquisition costs and extending customer lifetime. Learn more
Email categorization
Automatically routing emails to the correct recipients. Learn more
Phrase detection
Screening documents for critical phrases. Learn more
Ticket sorting
Identifying tickets with the highest importance. Learn more
Customer classification
Analyzing customer data to identify high-value clients. Learn more
Fraud prediction
Classifying fraudulent transactions. Learn more
Prediction of loan repayment
Predicting payment ability to prevent non-paying customers. Learn more
Insurance claim rejection
Determining which claims to process and which to reject. Learn more
Model deploy template
Model template to build models faster. Develop your model as a python package, and expose it as a callable REST API using this asset.
Pipeline template
Pipeline template to build pipelines faster. Build continuous training and delivery pipelines.
Model sheet-assessment
Helps to document model performance characteristics together with other factors which can minimize model usage in contexts for which model is not well suited.
Text anonymizer
Deploy this pretrained model that handles raw text anonymization by filtering private information and returns a anonymized version.
Data-sheet assessment
This assessment is intended to address the needs of data creators and data consumers.
Document similarity
Build and deploy a document similarity model with this framework.
Pipeline accelerator
Use this template to get started on Mlops pipeline development. Build continuous training and delivery pipelines.
Credit score
A numerical expression based on a level analysis of a person’s credit files, to represent the creditworthiness of an individual.
Summary model
The model can write a summary of a text in the English language. It will shorten the text while the core content will stay the same.
Price prediction
Determining the right price for a product or service. Learn more
Text completion
A model that can complete a text e.g. add words to the latest stock news. The model adds words around a brief statement in English language.
Analyzing past client lifecycles and the likelihood of churn. Learn more
Car damage recognition
The model can detect damages on the outside of a car and classify them.
Automatic file execution
The pipeline will take a python file name and location as input and rerun it on a schedule.
Face recognition
A model to recognize faces in a picture.
Automatic data ingestion
The pipeline automatically ingests data from a specific data source.
Danish speech to text
The model takes voice input and translates it to text (in Danish).
Auto retraining
A pipeline framework that re-trains a model on a schedule.
Bias check
The pipeline automatically checks a data source for biases.
Lead qualification
Lowering acquisition costs and extending customer lifetime. Learn more
Email categorization
Automatically routing emails to the correct recipients. Learn more
Phrase detection
Screening documents for critical phrases. Learn more
Ticket sorting
Identifying tickets with the highest importance. Learn more
Customer classification
Analyzing customer data to identify high-value clients. Learn more
Fraud prediction
Classifying fraudulent transactions. Learn more
Document similarity
Build and deploy a document similarity model with this framework.
Pipeline accelerator
Use this template to get started on Mlops pipeline development. Build continuous training and delivery pipelines.
Credit score
A numerical expression based on a level analysis of a person’s credit files, to represent the creditworthiness of an individual.
Summary model
The model can write a summary of a text in the English language. It will shorten the text while the core content will stay the same.
Price prediction
Determining the right price for a product or service. Learn more
Text completion
A model that can complete a text e.g. add words to the latest stock news. The model adds words around a brief statement in English language.
Analyzing past client lifecycles and the likelihood of churn. Learn more
Car damage recognition
The model can detect damages on the outside of a car and classify them.
Automatic file execution
The pipeline will take a python file name and location as input and rerun it on a schedule.
Face recognition
A model to recognize faces in a picture.
Automatic data ingestion
The pipeline automatically ingests data from a specific data source.
Danish speech to text
The model takes voice input and translates it to text (in Danish).
Auto retraining
A pipeline framework that re-trains a model on a schedule.
Bias check
The pipeline automatically checks a data source for biases.
Lead qualification
Lowering acquisition costs and extending customer lifetime. Learn more
Email categorization
Automatically routing emails to the correct recipients. Learn more
Phrase detection
Screening documents for critical phrases. Learn more
Ticket sorting
Identifying tickets with the highest importance. Learn more
Customer classification
Analyzing customer data to identify high-value clients. Learn more
Fraud prediction
Classifying fraudulent transactions. Learn more
Find out how others are getting value from our Asset Store
Client cases
