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Kortical MLOps
Deployment

Enterprise AI and ML creation, deployment and MLOps made easy

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Easy to use apps for better business outcomes

Users don’t consume machine learning models – they consume machine learning apps and services.

Dashboard of NHS Blood Supply & Demand Planning app showing a line graph forecasting blood demand and stock levels with selectable filters and statistics.
Dashboard of a fraud detection solution showing AI detection rate at 83.2%, false positive rate at 3.2%, transaction rate, savings of £24,521.02, top blocked transactions by Western Union, Cash Advance, and Amazon, and a UK map indicating transaction data.
Car finance dashboard showing actual and predicted settlements, EV propensity by gender and age demographics, and renewal propensity scores with categories from low to top.
Blue box with white text reading 'Your App Here', '1 Data Scientist', '1 Web Developer', and '1 Week' on a purple to blue gradient background.
  • Black check mark inside a green circle.

    Build ML apps and models easily and quickly

    Kortical’s groundbreaking ML deployment platform makes it easy to build machine learning models and the apps that use them.

  • Black check mark inside a green circle.

    Apply your expertise

    It's designed to be super simple to get started while also being expert-friendly. It has full code and transparency, so you can change anything and build whatever you want.

  • Black check mark inside a green circle.

    Do more with less, faster

    The idea is to knock out a cool POC ML app, all you'll need are:

    • 1 data scientist
    • 1 front-end Developer
    • 1 week

    This would typically be ready to deploy live to production in 1 month for a relatively straightforward ML app.

Build
Deploy
MLOps

Build

Steps to create your ML App with Kortical

1

Create a project and environments in the platform

Create your ML project - give it a name
2

Install the Kortical Python package

Install the Kortical Python package
3

Select your app template to start from

Select your app template to start from
4

Open the code, make whatever changes you like

Open the code, make whatever changes you like
5

Build your ML model in code or build model in the platform

  1. Exploratory Data Analysis
  2. Data Cleaning / Feature Creation
  3. AutoML
  4. Model Explainability
  5. Automatic Experiment Tracking
Build your ML model in code or build model in the platform
6

Run `kortical app deploy` (this takes your local code and puts it in the cloud)

Run `kortical app deploy` (this takes your local code and puts it in the cloud)
7

Check out your app

Check out your app

Deploy

Steps to put your app live in prod

1

Create a git repo for your app

Create a git repo for your app
2

Commit the code

Commit the code
3

Check all the continuous integration tests have passed

Check all the continuous integration tests have passed
4

Click promote to UAT

Click promote to UAT
5

Click promote to Prod

Click promote to Prod

MLOps

Steps to maintain your ML solutions

1

Check your dashboard / notifications to keep everything running smoothly

  1. Versions tracking and governance
  2. One click deployment and rollback for collections of apps and models
  3. Multiple environments for staged deployment
  4. Continuous integration / deployment
  5. ML Apps that meet the strictest enterprise SLAs
  6. Manage model / environment hardware via simple interface
Check your dashboard / notifications to keep everything running smoothly
2

Check your models

  1. Data drift
  2. Accuracy drift
  3. Business case drift
  4. Explainability
  5. Automatic version tracking
Check your models
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Ready to automate real work?

Contact us to see a focused demo and explore the quickest path to production.

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