Customized AI solutions
Grace Platforms 3 different pillars
Successful Al adoption rests on three core pillars that provide the enterprise-grade quality and robustness you need to accelerate and orchestrate Al responsibly
Asset Store
Accelerate AI adoption
Speed-up the implementation of Al and advanced analytics with the Asset Store's ready-to-use solutions
Machine Learning Operations
Orchestrate enterprise-wide AI
Streamline, optimize, and manage model development, deployment, and operation pipelines for Al
Governance, Risk & Compliance
Implement AI responsibly
Ensure adherence to the growing number of global regulations, laws, and ethical guidelines for Al
The Asset Store:
the AI productivity hack
With our Asset Store, there is no need to develop everything from scratch yourself. Benefit from ready-to-use assets, offering everything you need to kick-start and accelerate your Al projects. The Asset Store is sorted into different types of assets:
Model
Accelerators
Use model accelerators as a foundation for reducing the time spent on model development
Pre-trained
Models
Leverage pre-trained models with your data to efficiently accelerate Al implementation
Standalone Pipelines
Automate core data science processes via pipelines that can help transform data and even re-train your models
Governance Frameworks
Adopt regulatory and best-practice frameworks to govern your Al
Examples of Model Accelerators
Recommender Model
Model accelerator
Al model that helps recommend products or services based on customer interactions.
Scrap Reduction
Model accelerator
Al model that identifies parameters that can be optimized in the manufacturing line.
Container Dwell Time
Model accelerator
Al model to increase operational efficiency at container terminals.
Credit Rating
Model accelerator
Determines creditworthiness by using historic data to analyze and predict default probability.
Price Prediction
Model accelerator
Determines the right price for a product or service.
Churn Prediction
Model accelerator
Analyzes past client lifecycles and the likelihood of churn.
Examples of Pretrained Model
Summary Model
Pre-trained model
Generates a summary of English text by shortening content and ensuring the core message stays the same.
Text Completion
Pre-trained model
Completes a text by adding words around a brief statement in the English language.
Car Damage Recognition
Pre-trained model
Detects damages on the outside of a car and classifies them.
Examples of Standalone Pipeline
Pipeline Accelerator
Standalone pipeline
Builds continuous training and delivery pipelines to accelerate MLops pipeline development.
Automatic File Execution
Standalone pipeline
Automates file execution and reruns it on a schedule.
Standalone Data Ingestion
Standalone pipeline
Automates data ingestion from specified data sources.
Examples of Governance
Text Anonymizer
Governance
Detects personal information in text and removes it. It is an essential part of a pipeline when using text with Personal Identifiable Information (Pll).
Code Reviewer
Governance
Offers feedback on the quality of the code you have written so you can improve it to make it stable and robust.
Pll Scanner
Governance
Performs a quick and comprehensive scan within a directory or database to provide insights on Personal Identifiable Information (Pll).
Machine Learning Operations
Efficiency and agility across the ML lifecycle
Improve models time-to-production by seamlessly connecting all your stakeholders, from data science to IT operations and more, to orchestrate enterprise-wide AI
GRACE for Data Science
As a data scientist, you want to have the freedom to build models using open source components you trust. With GRACE, you have the flexibility to do so and the platform reduces the engineering work required.
Data Ingestion & Feature Extraction
✔ Data source catalog
✔ Tabular, text, video, image
✔ Ingest from any data source
✔ Data connectivity with code
GRACE for IT operations
With GRACE, you get the tools to manage models in production efficiently. From monitoring dashboards to alarms and retraining schedules. GRACE makes your daily routine easy.
Production
✔ One-click deployment
✔ Horizontal scaling
✔ Kubernetes pipeline builder
✔ Image, video, and NLP supported
GRACE for digitization of AI project management
GRACE is the digital partner to connect stakeholders across your organization for all AI projects. You manage the roles and operate within one platform rather than multiple single software components, which increases operational efficiency and reduces risk.
Access Management
✔ User management
✔ Permission
✔ User authentication integration
Governance Risk and Compliance
Manage a multitude of laws and regulations for data and AI
Complying with a growing number of global regulations, laws, and ethical guidelines is crucial for any company looking to implement AI. GRACE is the first AI platform to offer comprehensive support in Governance, Risk & Compliance.
How GRACE uniquely handles GRC for data, AI, and leading-edge technologies
Impact Assessments & Regulations
GRACE makes it easy to assess your AI projects’ impact from a regulatory and ethical perspective. You can activate +50 pre-built assessments defined by standard regulations and ethical guidelines or create your own business-specific assessments.
Ready-to-use GRC solutions available in the Asset Store
In addition to the GRC framework, GRACE also offers the Asset Store where you can find ready-to-use solutions for your business needs.
Pll Scanner
Governance
The Personal Identifiable Information (Pll) scanner gives quick insights about Pll within a directory or
Code Reviewer
Governance
Get feedback on the quality of the code you have written so you can improve it to make it stable and robust.
Bias Manager
Governance
Ensuring data sources do not lead to unknown or unintended biases.
Text Anonymization
Governance
Model that detects personal information in text and removes it. It is an essential part of a pipeline when using
Clients
