The Do-it-Yourself application to
create, train and deploy
your own machine learning models.
A No-Code AI Interface for Citizen Data Scientists!
The AI Studio allows business analysts, data analysts, and data scientists to rapidly develop Artificial Intelligence and Machine Learning Models through a user-friendly visual interface. Structured in four easy steps, the AI Studio supports the full AI lifecycle.
Craft training data
The toughest and often overlooked part of any ML-Model: Good training data. The AI Studio makes this process a breeze using Squirro’s Cognitive Search to define a Candidate Set and a WYSIWYG interface to quickly label data
Create a model
Take a labelled data set – we call it Ground Truth, select from various Algorithmic Methods – from Proximity Search, Naïve Bayes, SVM, Random Forest, Deep Learning, meld the two and create your ML-model.
Validate your work
A model is as good as its accuracy. The AI Studio provides for a comprehensive test suite to validate your model’s performance at start and overtime and allows you to work in operational feedback from the actual usage of your model
Choose from the Models you built or from our catalogue of pre-trained models and deploy them in Squirro’s Machine Learning Service. The result: Actionable Insights.
Automatically integrated with the Squirro Insights Engine
Continuous support from Squirro in all the stages
Do-It-Yourself model building
Augmented Intelligence solutions and Insights Engine
Turn unused data into actionable
insights with Squirro’s Augmented
A powerful platform empowering data
analysts anda data scientists. That’s the
Squirro Insights Engine
- Craft your own labelling data sets – Ground Truth Sets
- Build your own models based on your data
- Additional pre-trained models available out of the box
- Fully validated ML-models
- Deployed in Squirro or exported for your use elsewhere
- Use our API based classification service with your ML-Models
- Full integration with Squirro Insights Engine
- Accessible to business users, not just data scientists
- Speeds up the ML-model creation by 80%
- Delivers deep insight at speed and scale
- Saves money with less need for data scientists
- Works with an organization’s own training data
- Users have the ability to train their own models