Kortical delivers accelerated AI solutions with transparent AutoML and scalable deployment.
Kortical
What is Kortical
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.
Pros And Cons Of Kortical
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
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
Pricing Of Kortical
FQA From Kortical
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.
How does Kortical explain its models?
Kortical boasts advanced model explainability, allowing users to understand how the model works, what features influenced a prediction, and why a specific prediction was made. This transparency is crucial for users to build trust in their models and understand the decision-making process of the AI.
What deployment options does Kortical provide to its users?
Kortical provides both UI and API-based one-click deployment options that expedite the process of getting ML models from the experimental stage to a production environment. This feature supports scalable deployment of solutions and significantly reduces the time-to-market of AI and ML developments.
How can developers create ML apps or services using Kortical?
Kortical equips developers with all required tools to build, train, and deploy ML apps or services. Thanks to the platform’s versatile set of features and interfaces, developers can build and deploy a machine learning application within a matter of hours. The platform offers functionality for exploratory data analysis, custom data cleaning, feature engineering, model building, model explainability, and one-click deployments, all of which streamline the development of ML apps or services.
How user-friendly is Kortical and why?
Kortical is designed with a keen emphasis on user-friendliness. Its philosophy is to provide a platform that's both easy to use and flexible enough to accommodate any detail changes. It accomplishes this by offering dual modes of operation, a fully guided no-code model building interface and a full code-driven SDK, catering to the user's comfort and proficiency level.
How does Kortical adapt to market behavior changes?
Kortical quickly adapts to market behavior changes through its self-learning AI feature. This involves AI models that continually learn and improve as they are exposed to new data over time. Consequently, the AI solutions developed on Kortical can swiftly respond to changes in consumer behavior or market trends, ensuring they stay relevant and efficient.
What is Kortical's approach to self-learning AI?
Kortical's approach to self-learning AI is rooted in its AutoML and ML Ops functionalities. The platform identifies patterns and changes in consumer and market behavior, ushering in swift and efficient adaptation. Self-learning AI helps Kortical users stay ahead of the curve by dynamically improving their AI and ML solutions. Kortical’s philosophy of making AI development easy yet fully controllable enhances the adoption and ease of self-learning AI.
How does Kortical promote transparency with AutoML?
Kortical promotes transparency with AutoML by giving users control of every detail of the AI solutions. Users have the choice to specify every detail or let the AutoML manage it. Kortical also emphasizes advanced model explainability, making it clear to users how the AutoML created models work and what influences their predictions. Additionally, the transparency extends to full code-driven SDK, ensuring users retain complete control of their models.
Why does Kortical emphasize its ease of use in AI solutions development?
Kortical emphasizes its ease of use because it aims to make AI solution development accessible and manageable to a broad spectrum of users - from beginners to experts. All its functionalities, from exploratory data analysis to one-click deployment, are designed to offer maximum utility with minimal complexity. Regardless of an user’s familiarity with coding, the platform with its dual use interface (UI and code interface) ensures that both neophytes and experts can use Kortical proficiently. Furthermore, the platform lowers the barrier to entry by using AutoML to reduce the need for in-depth knowledge of fine-tuning ML models while also providing options to go into the details when necessary.
