Kortical

What is Kortical?

Kortical is an AI cloud platform built to really speed up how we deliver AI and ML solutions. It’s packed with features like transparent AutoML, scalable deployment options, ML Ops, and even auto-training for your AI/ML models. It is a powerful assistant for data scientists and coders, offering both a user-friendly interface and code-based access. You can use it for everything from exploring your data to cleaning it up and creating new features.

What Kortical does best is make the whole AI and ML process smoother. It automates a lot of the time-consuming, repetitive tasks, like initial data analysis, cleaning up messy data, and engineering useful features. It uses AutoML to automatically pick and fine-tune machine learning models, which means you can get your AI and ML solutions up and running much faster. Plus, Kortical has this neat code-based dynamic template system. This makes it super easy to build ML applications that you can adapt and launch quickly – sometimes in as little as 30 minutes!

So, what exactly is AutoML? It stands for Automated Machine Learning, and it’s basically a way to automate the entire process of applying machine learning to real-world problems. With Kortical, AutoML takes care of setting up model experiments. You can either tell it exactly how you want every part of the model set up, or you can let the AutoML system handle most of it for you. This really boosts how efficiently you can experiment with models and get them delivered.

Who created Kortical?

Kortical was founded by a team who really wanted to make delivering AI and ML solutions faster. The company officially launched on December 6, 2022. The founders are passionate about building automation tools that give data scientists and coders more power. They even have a vision of creating a Post Scarcity Society through their technology! On top of that, they’re big advocates for Universal Basic Income (UBI) and work with organizations to help adults learn new skills for the digital economy.

What is Kortical used for?

• Quickly exploring your data to understand it better.
• Cleaning up your data in custom ways.
• Creating and refining features for your models.
• Using AutoML to run model experiments automatically.
• Understanding how your models make predictions (model explainability).
• Building ML apps or services.
• Acting as assistive tech to speed up your iteration cycles.
• Helping you tweak the sizes of deep neural nets.
• Managing your models throughout their entire lifecycle.
• Making fast retraining simple.

Who is Kortical for?

• Data scientists
• Coders

How to use Kortical?

To get the most out of Kortical, here’s a breakdown of how to use it effectively:

1. Simplify Your Tasks: Kortical really streamlines things for data scientists and coders. It automates those repetitive jobs and gives you tools for exploring your data, cleaning it up just how you need it, and engineering features.
2. Explore Your Data: Jump into Kortical’s easy-to-use interface for exploratory data analysis. You can get a feel for your data types, check your assumptions, come up with hypotheses, and make smart decisions about which models to use and how to tune them.
3. Custom Data Cleaning: Make the most of Kortical’s quick custom data cleaning. It’s great for fixing missing, inconsistent, or incorrect data, and for structuring unstructured data so it’s ready for the next steps.
4. Feature Engineering: Use Kortical’s feature engineering capabilities to create and select the best features. This is key to improving your machine learning model’s accuracy by turning raw data into something ML algorithms can work with effectively.
5. Run Model Experiments: Dive into model experiments using the AutoML feature. It lets you create and compare thousands of different model variations really efficiently, depending on how much control you want.
6. Understand Your Models: Take advantage of Kortical’s advanced model explainability. It helps you see how the model works internally, understand which features influence its predictions, and why it makes certain decisions.
7. Deployment Options: Kortical offers both UI and API-based one-click deployment. This means you can move your ML models from the experiment phase into production super fast, and it supports scalable deployment.
8. Build ML Apps: Use Kortical’s tools to build, train, and deploy ML applications or services rapidly. Its flexible features and user-focused design are really built with developers in mind.

What really makes Kortical stand out is how user-friendly it is, how adaptable it is to market changes, its transparency, and its self-learning AI. These qualities make it a truly valuable tool for data scientists and coders who want to deliver impactful AI and ML solutions efficiently.