Kortical

What is Kortical?

Kortical is an AI cloud platform that aims to accelerate the delivery of AI and ML solutions through features like transparent AutoML, scalable deployment, ML Ops, and Auto Training of AI/ML models. It targets data scientists and coders, offering both UI and code interfaces for tasks like exploratory data analysis, custom data cleaning, and feature engineering. Kortical emphasizes ease of use as a balance between abstracting complexities while maintaining control over AI modeling details.

Kortical speeds up the delivery of AI solutions by automating repetitive tasks such as data analysis, data cleaning, and feature engineering. It leverages AutoML to automate machine learning model selection and tuning, allowing for quick realization of AI and ML solutions. Additionally, Kortical utilizes a code-based dynamic template system that enables the development of ML applications that can be easily adapted and launched in a short timeframe, sometimes as quickly as 30 minutes.

AutoML, short for Automated Machine Learning, is a process that automates the end-to-end application of machine learning to real-world problems. In Kortical, AutoML handles the creation of model experiments, providing users with the option to specify every aspect of the model themselves or let the AutoML system manage it. This enhances the efficiency of model experimentation and delivery.

Who created Kortical?

Kortical was founded by a team aiming to accelerate the delivery of AI and ML solutions. The company was launched on December 6, 2022. The founders are committed to creating automation tools that empower data scientists and coders, promoting a Post Scarcity Society through their technology. They advocate for Universal Basic Income (UBI) and partner with organizations to support adult learners in upskilling for the digital economy.

What is Kortical used for?

• Rapid exploratory data analysis
• Custom data cleaning
• Feature engineering
• AutoML functionality for model experiments
• Model explainability
• Creating ML apps or services
• Assistive tech for increased iteration speed
• Support for tweaking sizes of deep neural nets
• Lifetime model management
• Facilitates fast retraining

Who is Kortical for?

• Data scientists
• Coders

How to use Kortical?

To use Kortical effectively, follow these steps:

1. Simplify Tasks: Kortical streamlines tasks for data scientists and coders by automating repetitive processes and providing tools for exploratory data analysis, custom data cleaning, and feature engineering.

2. Exploratory Data Analysis: Utilize Kortical's intuitive interface to understand the data type, check assumptions, formulate hypotheses, and make informed decisions regarding model selection and parameter tuning.

3. Custom Data Cleaning: Take advantage of Kortical's feature for rapid custom data cleaning to rectify missing, inconsistent, or erroneous data and structure unstructured data for further processing.

4. Feature Engineering: Leverage Kortical's feature engineering capability to create and select features, enhancing machine learning model accuracy by transforming raw data into a format compatible with ML algorithms.

5. Model Experiments: Conduct model experiments by harnessing the AutoML feature, enabling users to create and compare thousands of model variations efficiently based on the level of control desired.

6. Model Explanation: Benefit from Kortical's advanced model explainability to understand the model's inner workings, feature influences on predictions, and reasoning behind specific predictions.

7. Deployment Options: Choose between UI and API-based one-click deployment options in Kortical to transition ML models from experimentation to production swiftly, supporting scalable deployment.

8. ML App Development: Use Kortical's tools to build, train, and deploy ML applications or services rapidly, thanks to its versatile features and user-centric design catering to developers' needs.

Kortical's emphasis on user-friendliness, adaptability to market changes, transparency, and self-learning AI sets it apart and makes it a valuable tool for data scientists and coders aiming to deliver impactful AI and ML solutions effectively.