Kortical

Kortical is an AI cloud platform that accelerates the delivery of AI and ML solutions. Its focus is on transparent AutoML, scalable deployment, ML Ops, and Auto Training of AI/ML models. Its design primarily targets data scientists and coders, helping to streamline repetitive tasks and facilitating significant business value. Kortical offers both UI and code interfaces to adapt to different users and also provides functionality for exploratory data analysis, custom data cleaning, and feature engineering. Kortical treats ease of use as a balancing act between abstracting complexities and retaining full control over AI modeling details.

Kortical enhances the speed of delivering AI solutions by automating repetitive tasks, such as data analysis, data cleaning, and feature engineering. It integrates the functionalities of AutoML, which allows automatic machine learning model selection and tuning, contributing to the quick realization of AI and ML solutions. Kortical also utilizes a code-based dynamic template system, which enables development of ML applications that can be adapted easily and launched in as little as 30 minutes.

AutoML or Automated Machine Learning is a process that automates the end-to-end process of applying machine learning to real-world problems. It eliminates or minimizes the need for skilled data scientists by automating many elements in the process of applying machine learning, such as pre-processing of data, feature extraction, model selection, and hyperparameter tuning. In Kortical, AutoML is used to handle the creation of model experiments, enabling users to detail every aspect of the model or to let the AutoML system manage it. This increases the efficiency of model experimentation and delivery.

Scalable deployment in Kortical refers to the ability to deploy machine learning models at scale, regardless of the size and complexity of the task at hand. Kortical has simplified this process by incorporating a one-click deployment feature via its user interface or an API. Regardless of the scale of the ML models, Kortical provides a seamless means to get them deployed with minimal effort.

Kortical has a distinct approach to ML Ops or Machine Learning Operations, which refers to the practice of managing machine learning models in production. Kortical's platform includes functionality for model lifetime management and retraining. Furthermore, Kortical incorporates self-learning AI, which helps adapt the ML models to changes in consumer and market behavior quickly and efficiently, thereby reducing the manual effort involved in ML Ops.

Kortical is designed primarily for data scientists and coders. It caters to professionals who are involved in delivering significant business value, who aim to streamline repetitive tasks, and who recognize the transforming role of AI in the business landscape. The platform empowers these users to rapidly scale the delivery of AI and ML solutions with comprehensive UI and code interfaces.

Kortical removes the repetitive tasks for data scientists and coders, enhancing their productivity and allowing them to focus more on delivering significant business value. Kortical provides tools for rapid exploratory data analysis, custom data cleaning, and feature engineering, all through intuitive UI and code interfaces. AutoML functionality offloads the complex task of iterating through thousands of model experiments, while the platform’s user-centric design allows data scientists to keep desired control over model details.

Kortical provides an intuitive interface for exploratory data analysis, a step in the data science process where the users can understand the type of data they are working with before applying ML algorithms. This features aids in checking assumptions about the data, formulating hypotheses for later statistical testing, and informing model selection and parameter tuning.