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Kortical ✓

Supercharge your AI with Kortical's AutoML platform

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AI Solutions AutoML ML App/Service Creation
More categories (4)
Scalable Deployment ML App/Service Deployment One-click Deployment AI
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Verdict

A quick read before you dig into the full page.

Best if
Supercharge your AI with Kortical's AutoML platform
Skip if
Limited non-coder suitability
Pick this when
Transparent AutoML

Considering Mistral AI Studio instead? Compare side by side

Overview

Kortical is an AI platform designed to accelerate the delivery of AI solutions, focusing on providing transparent AutoML, scalable deployment, ML Ops, and Auto Training AI/ ML models.

Primarily designed for data scientists and coders, Kortical aims to streamline repetitive tasks and facilitate significant business value. It allows users to rapidly scale the delivery of AI and ML solutions using both UI and code interfaces.

The platform provides functionality for exploratory data analysis, custom data cleaning, and feature engineering. With Kortical, users can create thousands of model experiments and specify every detail or let the AutoML handle it.

The platform also boasts advanced model explainability and offers one-click deployment via UI or API. Furthermore, Kortical allows for ML app/service creation, deployment, and infrastructure with the possibility to build and deploy an ML app with code.

Notably, Kortical emphasizes its philosophy of ease of use while providing the functionality to modify every detail of the AI solutions. The cloud-based platform assists in adapting to consumer and market behavior swiftly and efficiently, promoting self-learning AI.

Kortical website

Access & connections

Where you can use Kortical.

Platforms

Web

Capabilities

What this tool accepts, produces, and is strongest at.

Input

  • Code
  • Tabular
  • Text

Output

  • Code
  • API
  • Text

Pros & cons

Pros

  • Transparent AutoML
  • Scalable deployment
  • ML Ops
  • Facilitates significant business value
  • UI and code interfaces
  • Functional for exploratory data analysis
  • Custom data cleaning
  • Ability to feature engineer
Show 23 more
  • Can create thousands of model experiments
  • Gives Advanced model explainability
  • One-click deployment via UI/API
  • ML app/service creation functionality
  • Cloud-based platform
  • Streamlines repetitive tasks
  • Adapts to market behavior
  • Can build and deploy an ML app with code
  • Assistive tech for increased iteration speed
  • Option for no code model building
  • Support for tweaking sizes of deep neural nets
  • Lifetime model management
  • Facilitates fast retraining
  • Open Source based
  • No vendor lock-in
  • Checks for complete solution transparency
  • Code based dynamic templates for ML applications
  • Has an easy-to-use SDK
  • Operative for both coders and data scientists
  • Compliance with feature engineering
  • ML App deployment infrastructure
  • Consumer behavior adaptability
  • Assisted custom data cleaning

Cons

  • Limited non-coder suitability
  • No offline functionality
  • Potential complexity for beginners
  • Mixed UI and code approach
  • Unclear pricing structure
  • No disclosed integrations
  • Might overcomplicate simple tasks
  • Possible vendor lock-in

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Releases 1

Initial release December 6, 2022 Current
Initial release of Kortical.

FAQ 12

What is 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.
How does Kortical speed up delivery of AI solutions?
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.
What is AutoML and how does Kortical utilize it?
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.
How does Kortical assist in scalable deployment of AI solutions?
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.
What sets Kortical apart in handling ML Ops?
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.
Who are the intended users of Kortical?
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.
How does Kortical simplify the tasks for data scientists and coders?
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.
How does the platform assist in exploratory data analysis?
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.
How does the custom data cleaning feature work in Kortical?
Kortical offers a feature for assisting rapid custom data cleaning. This feature helps users to rectify and handle missing, inconsistent, or erroneous data. It allows users to put structure to unstructured data and cleanse it quickly to make it compatible for further processing. The process can be tailored and automated based on the individual requirements of the project, allowing users to get valuable and clean datasets promptly.
What benefits does the feature engineering in Kortical provide users?
Feature engineering in Kortical provides users with the capability to create and select features, driving the accuracy of a machine learning model. This feature allows users to transform raw data into a format that is compatible with ML algorithms, improving model accuracy. This process can be automated or customized based on the specific needs of the user or the project.
How can users conduct model experiments using Kortical?
Users can conduct model experiments in Kortical by harnessing the AutoML feature. This functionality allows users to create thousands of variations of machine learning models within a short time span and compare the results. Users can plan these experiments to be entirely automated, semi-automated, or entirely manual, based on the details they wish to control.
Can users manipulate every detail in model experiment or can it be automated?
Kortical allows users to detail every aspect of the AI or ML model or let the AutoML handle it. An user can be as involved as they want, adjusting the parameters, layer sizes of deep neural networks, or particular solver settings. The level of autopilot can be adjusted based on a user’s needs and requirements, providing maximum control on the level of granularity.
Kortical
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