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Getting
started Guide

1

Go to the lab screen and upload the dataset you want to train a model on.

User interface of Kortical's project workspace showing a file upload dialog with the message 'Drop files here, paste or browse' for CSV files.
2

After the data uploads just select the column you want to predict.

Data upload interface displaying a table with columns for Customer_Ship_To, Location_Ship_To, Sales_Person, Successful_Sale, Quote_Date, Avail_Date, Quantity, and Margin, with options to filter columns and select active column types.
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AutoML

Kortical’s competition crushing, cloud scale, distributed AutoML will start finding the best machine learning solution for your dataset. It builds machine learning solutions from the ground up, data cleaning, preprocessing, feature creation, model selection, tuning and much more. Using distributed cloud scale AI it searches the solution space for the best possible machine learning model and surrounding solution. Kortical has won every competition it entered, including against Google’s AutoML.

3

As Kortical creates new models you can view or edit the model solution code or compare models on the model leaderboard. Once you are ready you can publish the best model.

Computer screen showing a machine learning model interface with a pop-up asking for confirmation to publish the model, featuring options to cancel or publish.
4

Explain any model from a simple Random Forest to a Deep Neural Network with multiple different text encodings. Use the insight to create better features and improve results, remove bias from models and win over stakeholders that would be reticent of black-box AI.

User interface of Kortical Explain showing feature importances with bar charts for high-level and low-level features, highlighting relative importance values.
5

Click deployment and copy the relevant snippet for your business insights tool of choice.

Screen showing sample Python API code for prediction and an example Google Sheets function for prediction integration on the Kortical platform.
6

Open your business insight tool, here we’re showing Google Sheets but it could just as easily be Qlik, Tableau, PowerBI, etc. Paste your snippet and now it’s AI enabled, serving predictions from the models you just created. You can use these models to predict customer churn, do advanced forecasting or customer segmentation among millions of other use cases.

Google Sheets spreadsheet showing sales data with columns for dates, customer, location, salesperson, quantity, margin, probability of sale, and probability adjusted value, alongside a chart comparing probability of sale and probability adjusted value over quantity.
1

Go to the lab screen and upload the dataset you want to train a model on.

User interface of Kortical's project workspace showing a file upload dialog with the message 'Drop files here, paste or browse' for CSV files.
2

After the data uploads just select the column you want to predict.

Data upload interface displaying a table with columns for Customer_Ship_To, Location_Ship_To, Sales_Person, Successful_Sale, Quote_Date, Avail_Date, Quantity, and Margin, with options to filter columns and select active column types.
3

Kortical has a simple high level language for data-science and it generates intuitive high level code that can be refined before running the AutoML. (The language usually takes less than half a day to learn)

Screenshot of Kortical Lab interface showing a YAML configuration for a classification model with dataset details, feature specifications, action buttons to upload data, generate and load code, and training status indicating waiting to begin training.
AutoML AI brain spinner image
AutoML AI brain image

AutoML

Kortical’s competition crushing, cloud scale, distributed AutoML will start finding the best machine learning solution for your dataset. It builds machine learning solutions from the ground up, data cleaning, preprocessing, feature creation, model selection, tuning and much more. Using distributed cloud scale AI it searches the solution space for the best possible machine learning model and surrounding solution. Kortical has won every competition it entered, including against Google’s AutoML. As Kortical rapidly builds thousands of candidate solutions it has inbuilt cross-validation, held back test set testing and some proprietary IP to prevent overfitted model selection. It can be used to build world class models for a wide range of supervised machine learning problems from binary, multi-class, multi-label classification, to regression, to recommendation, to NLP, time series, survival analysis among many more. It’s easier to list what it isn’t suited for - images or video.

4

As Kortical creates new models you can view or edit the model solution code or compare models on the model leaderboard. Once you are ready you can publish the best model.

Computer screen showing a machine learning model interface with a pop-up asking for confirmation to publish the model, featuring options to cancel or publish.
5

Explain any model from a simple Random Forest to a Deep Neural Network with multiple different text encodings. Use the insight to create better features and improve results, remove bias from models and win over stakeholders that would be reticent of black-box AI.

User interface of Kortical Explain showing feature importances with bar charts for high-level and low-level features, highlighting relative importance values.
6

Very few models are one and done. Usually you’ll build a model, learn something useful, maybe create a feature, maybe add a different dataset and then iterate. Because Kortical’s AutoML is code based, it’s really easy to fix parts of the solution and just iterate on the bits you’re working on, saving a lot of time and wasted compute cycles that you’d get with AutoML that doesn’t let you control and focus it easily.

7

With any live project there are usually multiple environments, development, beta, production, etc. Kortical makes it easy to separate code from data-science making model updating and management a breeze through a simple UI or API. Kortical’s deployments can handle any scale and offer mission critical level reliability. Making it the AI platform of choice for the NHS and Deloitte.

Dashboard interface showing deployment integration for a live model with ID 21, user email, published date, model score, and running prediction worker statuses.
8

Click deployment and copy the relevant snippet for your language of choice. It can be called in real-time or batch.

9

AI enable your app, website or microservice in minutes with a simple API. Kortical also offers libraries for data transforms, etc. Production ready ML microservice and website templates that can be easily adapted to create custom apps and services. Microservice and website hosting and deployment. So creating a fully fledged machine learning solution has never been easier. We focus on the code so you can focus on the ML solution.

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A helpful Kortical team member will reach out to set up a time and find out a little about your challenges, usually same day.

Book in a demo / hands on session with your data or Kaggle datasets.

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